Machines doi: 10.3390/machines14060686

Authors: Zhengjiu Ma Yuxin Liu Yongbin Li Zhi Niu Zhaoqing Kang Zedan Li Tong Wang Tiejun Li

The dual-arm cooperative operation mode can effectively address the problems of insufficient load capacity and limited motion flexibility of traditional single-arm robots during the installation of construction boards. However, the selection of the end-effector grasping position of dual-arm robots will significantly affect their motion performance during handling operations. To address this issue, this study proposes an end-effector grasping strategy for sheet installation in the dual-arm cooperative operation mode of a dual-arm robot, which determines the optimal grasping position to ensure the robot’s good operational performance. We developed a dual-arm robot prototype for board installation and established a kinematic model of the robot’s manipulators. Based on the dexterity index’s service sphere, we obtained the dexterity envelope surfaces of the robot end-effector at different grasping distances and analyzed the relationship between grasping distance and dexterity. The mechanical model of the robot was established, and simulations were performed for each joint. The effects of different grasping points on the torque, stiffness, and stability at the robot’s key points were investigated, and the end-effector grasping range of the robot with optimal mechanical performance was analyzed. Finally, the proposed robot grasping strategy was verified on the robot prototype. The results demonstrate that the strategy is feasible and effective, helping to improve the robot’s operational performance.

Machines doi: 10.3390/machines14060685

Authors: Nizamuddin Maitlo Rafaqat Hussain Arain Kaleem Arshid Nooruddin Noonari Ghulam Mustafa

Autonomous unmanned aerial vehicles (UAVs) must decide when to trust onboard perception, when to request edge support, and when to avoid acting under poor visual or communication conditions. This study develops a risk-aware edge-assisted UAV perception framework that combines calibrated visual confidence with next-window service-level agreement (SLA) feasibility. The local branch uses MobileNetV3-Small for fast onboard color recognition, while the edge branch uses ResNet-18 for stronger remote inference. Low-confidence samples are offloaded only when the SLA predictor estimates that the wireless link is feasible; otherwise, the system enters fallback, meaning that the current prediction is not treated as immediately actionable. The evaluation follows a hard cross-illumination split: indoor and fluorescent light samples are used for training and validation, and indoor night and sunlight samples are reserved for testing. Under this setting, the local model achieves 76.89% accuracy and 73.25% macro-F1, while the edge model achieves 81.26% accuracy and 77.58% macro-F1. The SLA predictor, trained on enhanced telemetry features while preserving the original target label, achieves 85.74% accuracy, 85.57% macro-F1, 0.9420 ROC-AUC, and 0.9585 PR-AUC on temporally held-out records. The joint policy achieves 93.23% coverage and 79.90% success over active decisions, using local inference for 82.76% of the samples, edge offloading for 10.47%, and fallback for 6.77%. These results indicate that the framework is best understood as a tunable risk management layer for UAV perception rather than a pure accuracy maximization classifier. It avoids blind offloading and reduces forced decisions when both visual confidence and communication feasibility are weak.

Machines doi: 10.3390/machines14060684

Authors: Shicheng Zheng Tianxiang Qin Jingkun Wei Jiaming Cheng Xiaming Yuan Jihong Zhu

To address the limitations of the centralized hydraulic steering system used in the first-generation heavy-duty wheeled platform developed by our team, this study proposes a fully electrified steering system based on a compact direct-drive electro-mechanical actuator (DEMA) architecture. Compared with the original hydraulic system, the proposed solution reduces the steering-system weight from approximately 150 kg to 32 kg in the single-channel configuration and 40 kg in the dual-channel configuration, while significantly improving system integration and maintainability. For the single-channel DEMA steering system, a composite control strategy combining three-loop PID control with feedforward compensation is developed to improve dynamic response and position-tracking accuracy. AMESim simulation results under a steering resistance torque of 6000 ± 500 Nm show that the system achieves an overshoot below 2%, a steady-state error below 0.1°, and a tracking error below 0.4°. To reduce motor power and thermal-management requirements, a dual-channel DEMA steering architecture is further proposed. Considering inter-channel parameter differences, a primary–secondary synchronization control strategy is developed to suppress force-fighting behavior and improve motion consistency. Simulation results demonstrate that the proposed strategy effectively reduces synchronization errors and maintains highly consistent force output between channels while preserving excellent steering accuracy and tracking performance. The proposed fully electrified steering system and synchronization control strategy provide an effective solution for improving the dynamic performance, lightweight design, and reliability of heavy-duty wheeled platforms.

Machines doi: 10.3390/machines14060683

Authors: Jinlin Huang Qingfeng Sun Chen Wang

Conventional finite element method (FEM) has a complex model and a long optimization time for Halbach array PM machines. This paper proposes a hybrid analytical method that combines the subdomain method (SM) and the magnetic circuit method (MEC) for analyzing a high-torque-density, three-segment Halbach array rotor permanent magnet (PM) machine, accounting for Halbach array magnetization and end leakage flux. Firstly, to address the challenge posed by complex PM shapes in the Halbach array PM machine, a novel subdivision equivalence method is conducted. Then, the magnetic equivalent circuit (MEC) of the stator and rotor is established, and the axial leakage flux and nonlinearity of the iron core are taken into account. In addition, electromagnetic performance, such as air gap flux density, cogging torque, electromagnetic torque, and back electromotive force (back-EMF), is obtained based on the proposed hybrid analytical model. The analytical results are verified by using the finite element method (FEM), and the results show that the error is less than 2%. Finally, a 15 kW prototype PM machine with a Halbach array PM rotor is manufactured and tested, and the results validate the accuracy and efficiency of the analytical method.

Machines doi: 10.3390/machines14060682

Authors: Yangfeng Ji Rongfei Xia Miaojiao Peng

Rolling element bearing monitoring under operating condition shifts remains difficult because fault signatures are transient, fault data are scarce, and degradation trends may depend on load and speed. This study evaluates a condition-aware DANN-LSTM framework for joint fault diagnosis and RUL prediction. A one-dimensional CNN extracts vibration features, a gradient reversal branch aligns condition-related distributions for fault classification, and an LSTM models chronological degradation features without direct adversarial regularization. The model jointly optimizes classification, condition-discrimination, and RUL losses. Experiments on public bearing datasets show high class-wise identification rates, a validation accuracy of 0.989, and an RUL RMSE of 7.9. Controlled ablation indicates that moderate condition alignment improves transfer classification while preserving useful degradation ordering for RUL prediction. The framework offers a practical data-driven baseline for bearing condition monitoring under controlled condition shifts.

Machines doi: 10.3390/machines14060681

Authors: Li Lei Qingyang Wang Yesong Li

High-end vertical five-axis machining centers commonly adopt dual-motor direct-drive configurations for their cradle-type A-axis to improve dynamic performance; however, this approach introduces control challenges in balancing counteracting torque and synchronization accuracy due to high-rigidity coupling. To address this issue, this study presents a novel error compensation control strategy based on a synchronous state observer. First, a system dynamic model incorporating dual-axis coupling effects is developed to systematically investigate the coupling mechanism between synchronization error and counteracting torque. Based on this model, a synchronous state observer is designed, which achieves real-time reconstruction and feedforward compensation of synchronization disturbances induced by factors such as transmission parameter mismatches and inter-axis torque imbalance, thereby enabling coordinated control of high-precision position synchronization and torque balance. The effectiveness of the proposed method is verified through simulation and experiments conducted on a VMC630 vertical five-axis machining center. Results show that under various speed and acceleration conditions, the maximum position synchronization error remained below 6.3e−4∘, with comparable convergence performance; the current deviation between the dual motors was constrained to within ±0.25A, demonstrating effective mitigation of counteracting torque. In machining tests of S-shaped specimens, all measured contour deviations fell within the ±0.060mm tolerance range, and the specimens exhibited excellent contour consistency and surface quality. These results validate the proposed strategy’s status as an engineering-viable solution for precision motion control in high-rigidity coupled dual-motor systems.

Machines doi: 10.3390/machines14060680

Authors: Marek Karbowniczyn Jerzy Baranowski

One of the main challenges in BLDC motor diagnostics is the identification of faults with different physical origins, especially in mixed states where the symptoms of multiple faults may overlap. In this work, a classification system based on feature-level data fusion was developed by combining current and rotational signals. A homogeneous Stacking Ensemble model was used as the main mechanism for fault classification. The study was conducted on a dataset of 184 samples representing four operating conditions: healthy operation, mechanical faults, electrical faults associated with permanent magnet degradation, and their combined occurrence. The stability of the proposed classifier was evaluated using ten different data splits. The experiments showed that omitting PCA preserves more diagnostically relevant information contained in the raw features, resulting in a classification accuracy of 97.3% with a standard deviation of 0.017. PCA consistently reduced performance across all considered data modalities. The model was further analysed using SHAP, indicating that its decisions were driven by physically interpretable features from both the rotational and current domains.

Machines doi: 10.3390/machines14060679

Authors: Shih-Sheng Chen Ruei-Hung Hsu Jau-Liang Chen

In precision gear manufacturing, longitudinal crowning on tooth flanks is commonly produced by applying diagonal feed in worm-type generating processes using tools such as variable-tooth-thickness hobs and dressable grinding worms. However, precise twist control remains difficult because the geometric parameters of the generating tool are strongly coupled with the machine feed settings in the underlying generating kinematics. In addition, direct numerical optimization becomes unreliable near the standard tool state, where the sensitivity of the diagonal-feed coefficient degenerates and conventional linearized solvers may lose effectiveness. To address these issues, this study proposes a multi-variable optimization framework for twist-constrained worm-type gear generation. An iterative singular value decomposition (SVD) scheme is developed to construct and update the sensitivity matrix, while a warm-start continuation strategy is introduced to overcome the local singularity and improve numerical robustness. Two closed-form expressions for the diagonal-feed coefficient are also proposed as practically useful initial estimates, corresponding respectively to the minimum SVD topographic residual and the minimum tooth-flank twist. Numerical validation over a 60-case parameter sweep shows maximum relative errors below 1.6% within the tested range. The proposed framework coordinates the tool-geometry design and diagonal-feed selection to generate tooth flanks with prescribed crowning characteristics while satisfying a specified twist requirement and limiting the required diagonal shift. Numerical examples show that the iterative framework reduces the root-mean-square (RMS) topographic error from 1.14 μm to 0.027 μm relative to the analytical setting of Hsu and Fong. These results indicate that the proposed method provides a reliable computational basis for twist control and process-parameter design in advanced CNC gear generation. From a manufacturing standpoint, because the three design criteria are accessed by adjusting only the diagonal-feed ratio on the machine, a single generating-tool design can serve a range of crowning and twist requirements without retooling, reducing setup and tooling efforts in production.

Machines doi: 10.3390/machines14060678

Authors: Xianpeng Zhang Xiaojian Zhang Xu Zhang Tao Ling Dawei Tu

Kinematic modeling and parameter identification are essential for achieving high-precision robot calibration. A widely used strategy involves utilizing the end-effector position error for parameter identification. However, the strong coupling between length and angular parameters often impedes calibration accuracy. In addition, substantial differences in their scales further exacerbate this issue. To overcome these limitations, following the variable projection method, this paper reformulates the conventional Modified Denavit–Hartenberg (MDH) model into a separable nonlinear structure. This allows independent identification of the two parameter types. Non-geometric errors such as joint compliance and backlash are also explicitly taken into account. The backlash errors are separated from the angular positions of each joint by modeling their bidirectional positioning errors with Chebyshev polynomials. This method enables the establishment of a comprehensive positioning error model to mitigate the influence of backlash errors. Based on the variable projection method, an improved variable projection with modified Gram–Schmidt (IVPMGS) identification method is proposed, which also eliminates redundant parameters that hinder identification robustness. Simulations indicate that the proposed method achieves faster convergence and higher identification accuracy. Compensation experiments demonstrate that the average absolute positioning error is reduced from 0.1804 mm to 0.0917 mm compared with the traditional MDH model, corresponding to a 49.17% improvement in positioning accuracy. These findings confirm the accuracy and effectiveness of the proposed approach.

Machines doi: 10.3390/machines14060675

Authors: Tingting Wu Yaxin Liu Laihe Qi Pu Wang Qingtao Liang Shuai Li Lijian Li Xingyuan Miao Hong Zhao Xingxing Wang

A pipeline intelligent plugging robot (PIPR) is an important tool in subsea pipeline maintenance and emergency repair. Precise position-tracking control is crucial for the in-pipe plugging operation of a PIPR. The anchoring module is the key component responsible for fixed-point braking, which faces the challenges of insufficient structural adaptability within a narrow space. Additionally, traditional PID control may lead to poor robustness under fluctuating working conditions and load disturbances. To address these issues, this study designs a novel anchoring module combining screw transmission, an eccentric crank–slider mechanism, and a parallelogram linkage. To achieve adaptive tracking control, the improved deep deterministic policy gradient (DDPG) algorithm is introduced to optimize the parameters of the PID controller. A reward function with mechanical constraint penalties and a dual-phase strategy is proposed for dynamic parameter optimization. All control performances are analyzed and verified through simulations. The results indicate that the proposed method outperforms traditional PID control as regards response speed, overshoot, and robustness, which can achieve precise anchoring. This study provides a theoretical foundation for ensuring the precision of the plugging process.

Machines doi: 10.3390/machines14060677

Authors: Habib Dhahri Fahad Alotaibi Awais Mahmood Mousa Jari

Intelligent autonomous driving systems must not only predict the appropriate driving manoeuvre but also provide human-interpretable evidence that justifies the decision. However, existing methods typically address these objectives separately, leading to three practical limitations: multi-stage perception-to-language pipelines can propagate upstream perception errors into downstream explanations; post hoc saliency methods often produce pixel-level highlights that are difficult to interpret semantically; and decoupled decision and explanation modules cannot guarantee that the explanation reflects the same scene evidence used for behaviour prediction. In this paper, we propose a unified framework that jointly performs vehicle behaviour prediction and human-centric interpretation from a shared visual backbone. Specifically, a hierarchical Swin Transformer encodes the driving scene into a sequence of spatial tokens, which are processed by two complementary branches. The first branch, termed the Object Selection Module (OSM), learns a compact scene-level semantic representation through query-guided cross-attention, while the second branch extracts a small set of class-agnostic object-centric tokens without requiring bounding-box or segmentation supervision. These two representations are subsequently integrated by a Semantic–Object Fusion (SOF) module based on scaled dot-product attention, residual connections, and a feed-forward network. The behaviour prediction head operates on the fused representation, whereas the interpretation head leverages the semantic representation through a skip connection to preserve decision-relevant context. For surround-view perception, learnable per-camera embeddings are introduced to maintain viewpoint identity with negligible additional parameter cost. Furthermore, a compact language model fine-tuned via Low-Rank Adaptation (LoRA) generates fluent, label-conditioned natural-language justifications. Extensive experiments on two public benchmarks, BDD-OIA and nu-AD, demonstrate that the proposed framework consistently delivers superior performance and provides effective, human-readable interpretations of driving decisions.

Machines doi: 10.3390/machines14060676

Authors: Wei Liang Shi-Yuan Kong Xin Zhao Pi-Yao Liu

To address the difficulty of directly applying finite element simulation trajectories to actual spinning machines, as well as the discrepancy between simulation data and equipment execution in the spinning process, this paper proposes a trajectory mapping method based on simulation trajectory extraction and data-driven error compensation. First, based on secondary development in ABAQUS, a discrete shell is introduced and combined with coordinate transformation to achieve accurate extraction of the roller center trajectory, and the simulated trajectory is converted into a discrete coordinate sequence. Subsequently, a roller trajectory acquisition and visualization system is developed, and machine motion data are collected and visualized for comparative analysis via the Modbus-RTU protocol. On this basis, to address the systematic deviation between simulated and actual execution trajectories, a trajectory error compensation method based on the projection method and Gaussian Process Regression is proposed. By modeling normal-direction errors and applying normal-direction compensation, smooth and stable optimization of the original simulation trajectory is achieved. Finally, experimental validation is conducted on a single-roller spinning machine, and the variation in trajectory deviation before and after compensation is comparatively analyzed. The results show that the proposed method effectively reduces trajectory execution errors, decreasing the average error from 0.503 mm to 0.229 mm, and significantly improves trajectory matching accuracy. This study provides an effective technical pathway for high-precision transformation of simulation trajectories to actual equipment in spinning processes and offers important support for the transition from experience-driven to model-driven spinning manufacturing.

Machines doi: 10.3390/machines14060674

Authors: Dehai Zhang Yang Song Zizhen Yang

Slip ring–brush assemblies are widely used in satellite mechanisms to transmit power and signals across rotating interfaces. Under authentic space environments—vacuum, radiation-dominated thermal exchange, and long-duration operation—the coupled effects of mechanical contact dynamics, electrical conduction, intermittent separation, and arcing can accelerate wear and degrade reliability. This paper presents a surface-resolved multiphysics model for multi-track slip rings with staggered brushes. The ring surface is discretized on a circumferential–axial grid and endowed with correlated 3D roughness, enabling interference-based asperity contact. Brush normal dynamics (mass–spring–damper) convert runout and micro-vibration into normal-force ripple and separation events. Electrical conduction is modeled by a parallel admittance network combining pressure-dependent micro-contact conduction and an event-based arc channel activated by separation, opening velocity, and current density with stochastic ignition. A 2D thermal model with ADI integration accounts for Joule/friction heating, radiative cooling, and optional hub conduction. Wear evolves via an Archard-type mechanical term and an arc-energy-driven erosive term. A FAST–MACRO multiscale scheme (20 s FAST, 100 h MACRO with periodic recalibration) enables tractable long-horizon wear prediction while preserving arc statistics. Baseline simulations for a 28 V bus demonstrate rare but nonzero arc activity and predict spatially non-uniform wear at the micrometer scale after 100 h.

Machines doi: 10.3390/machines14060673

Authors: Haoze Ren Haichao Zhou Wei Zhang Zhiwei Gao Ting Xu

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Machines doi: 10.3390/machines14060672

Authors: Shahil Kumar Giansalvo Cirrincione Rahul Ranjeev Kumar

This study presents a proof-of-concept investigation into the use of proximal policy optimization (PPO), a deep reinforcement learning (DRL) algorithm, for estimating the remaining useful life (RUL) of rolling element bearings. Although DRL has shown growing promise in prognostics, existing applications have predominantly relied on off-policy deterministic actor–critic methods such as deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3); the suitability of on-policy clipped-objective methods such as PPO for this task remains comparatively unexplored. To address this gap, statistical time-domain features are extracted from raw vibration signals and used as input to train a PPO agent with an actor–critic architecture, in which the actor network predicts RUL values and the critic network evaluates prediction quality through state-value estimation. A preprocessing pipeline comprising feature extraction, normalization, and sliding-window segmentation is developed, and the PPO framework incorporates generalized advantage estimation (GAE), a custom-designed reward function, and a policy-clipping mechanism to support stable training. The method is evaluated on a representative bearing (Bearing 2_1) from the XJTU-SY run-to-failure dataset using a chronological train/test split, and benchmarked against long short-term memory (LSTM) networks, multilayer perceptrons (MLPs), and a naive linear regression baseline. Performance is assessed using root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), and a domain-specific asymmetric scoring function that penalizes late predictions more heavily than early ones. Experimental results show that the PPO-based model produces more stable and operationally favourable RUL estimates than the supervised baselines on the unseen late-degradation segment, particularly in the critical end-of-life region. The findings support PPO as a viable on-policy DRL formulation for bearing RUL prediction and motivate further validation across multiple bearings and operating conditions, identified here as essential future work.

Machines doi: 10.3390/machines14060671

Authors: Abdelhak Goudjil Mostafa Kamel Smail Muhammad Sharjeel Javaid Houssem Rafik El-Hana Bouchekara

This paper introduces a novel fault diagnosis framework for wiring networks that integrates Time Domain Reflectometry (TDR) with Gramian Angular Field (GAF) representations and a deep residual neural network. The proposed methodology transforms TDR responses into GAF images, which are directly exploited by the residual neural network to enable robust feature extraction from complex reflectometry signals. To support supervised learning, a forward modeling strategy is employed to generate representative TDR responses under a wide range of fault scenarios. Theframeworkis designed to provide real-time fault detection, localization, and characterization, demonstrating high effectiveness on complex topologies such as the YY-shaped network. Numerical results demonstrate high diagnostic performance for hard faults, achieving an overall accuracy and macro-averaged sensitivity exceeding 99%, thereby highlighting the effectiveness and reliability of the proposed approach.

Machines doi: 10.3390/machines14060670

Authors: Yuying Ji Qiang Xu Qiang Gao Hao Li Runmin Hou

This paper addresses a specific dynamic limitation in conventional adaptive super-twisting sliding mode control (ASTSMC) for permanent-magnet synchronous motor (PMSM) speed regulation: the reactive lag of gain adaptation. In standard ASTSMC, controller gains are adjusted based solely on the sliding variable, which grows only after a disturbance has already induced a tracking error. This reactive behavior may produce a non-negligible transient speed droop during abrupt load variations. To alleviate this limitation, a proactive gain-scheduled ASTSMC (PDG-ASTSMC) strategy is proposed. A second-order nonlinear extended state observer (NESO) is employed to estimate the lumped disturbance and to extract its time derivative d^˙l. This disturbance-derivative signal is incorporated into the gain adaptation law to increase the controller gains during the incipient phase of a load change, before significant speed error accumulates. Stability analysis based on a composite Lyapunov function establishes uniformly ultimately bounded convergence of the closed-loop system, and a quantitative relationship between the proactive index and transient droop reduction is derived. Experimental validation on a 1.42 kW PMSM platform shows that, compared with conventional reactive ASTSMC, the proposed PDG-ASTSMC reduces transient speed droop by over 17% (from 10.5 rpm to 8.7 rpm) and shortens load recovery time by approximately 69% (from 140 ms to 44 ms), without increasing steady-state chattering or current ripple.

Machines doi: 10.3390/machines14060669

Authors: Yanan Sun Zezheng Tian Na Li Haiyong Jiang Chao Yang Chongchong Chen Lei Yang Lei Xing Lijie Zhang

The brake–release stability of electro-hydraulic thrusters (EHTs) significantly affects the safety of hydraulic braking systems, especially under low-temperature conditions with varying fluid viscosity. Most existing studies have focused on macroscopic braking characteristics, while the internal flow field variation and vortex evolution mechanism during the brake–release process remain insufficiently explored. In this work, transient CFD simulations are conducted to investigate vortex formation rules and flow field characteristics inside an EHT. Three typical vortex structures denoted as α, β, and γ are identified, and the independent and coupling influences of fluid dynamic viscosity and motor speed on vortex intensity and piston-bottom pressure are quantitatively analyzed. The results show that vortices α and β trigger flow disorder and additional hydraulic energy loss, while vortex γ optimizes flow uniformity and assists piston extension. Higher fluid viscosity exacerbates vortex development and pressure fluctuation, while increasing motor speed accelerates transient flow field evolution. This study clarifies the internal flow mechanism of EHT brake–release behavior and provides reliable parametric guidance for optimizing the low-temperature performance of electro-hydraulic braking systems.

Machines doi: 10.3390/machines14060668

Authors: Li Cao Yan Hu Jiangning Wang Bohan Wang Siyu Wu Jingshan Zhang

High-speed permanent magnet synchronous motors (PMSMs) used in electric pump-fed liquid rocket engines require stator shielding sleeves to prevent corrosive propellants from causing harm under cyclic pressure. However, metallic sleeves suffer significant losses due to eddy currents. Conversely, pure carbon fiber reinforced polymer (CFRP) sleeves have failed when exposed to 98% H2O2. Micro-CT analysis of a failed pump sleeve reveals a four-stage failure mechanism. Manufacturing defects caused matrix cracking, which propagated under pressure and thermal cycling. This progression resulted in the formation of through-thickness leakage paths, which ultimately triggered catalytic decomposition and explosion. To address these issues, an improved dual-layer sleeve is proposed, featuring a 2.5 mm PEEK 450G liner and a 2.0 mm T700S/epoxy CFRP overwrap. Finite Element Analysis (FEA) indicates peak von-Mises stresses of 86.25 MPa and 112.16 MPa, yielding Tsai–Wu safety factors of 2.9 and 1.7. Furthermore, various tests, including immersion, fatigue, burst, hydraulic, and thermal evaluations, demonstrate a burst margin of 2.37× at 7.12 MPa, with only 0.19% increase in mass. This design effectively eliminates leakage pathways while preserving zero eddy-current loss and ensuring a low weight.

Machines doi: 10.3390/machines14060667

Authors: Mingyuan Hu Huaimiao Zhu Changning Wei Lei Zhang Haoran Wei Yaqing Gu Bo Gao Yaohua Ma Dongjun Zhang

High-performance control of surface-mounted permanent magnet synchronous motors (SPMSMs) is critical for unmanned aerial vehicle (UAV) rotor servo systems, which demand fast dynamic response, high steady-state accuracy, and strong robustness against complex disturbances. However, conventional sliding mode control (SMC) methods often suffer from inherent issues like integral windup, persistent chattering, and sensitivity to parameter variations, limiting their effectiveness in such challenging applications. To address these limitations, this paper proposes a novel composite control strategy. The method integrates an improved terminal integral sliding mode controller (ITISMC) with an adaptive super-twisting reaching law (ADSTA) and a terminal integral sliding mode observer (TISMO). The key innovations include: (1) a redesigned sliding surface incorporating a smooth nonlinear function to suppress chattering and a variable-gain integral term to mitigate integral windup; (2) an adaptive reaching law that dynamically adjusts its gains based on the system state to balance convergence speed and chattering suppression; and (3) a disturbance observer that provides real-time estimation and feedforward compensation of total disturbances, significantly enhancing robustness. The proposed ITISMC-ADSTA-TISMO strategy was implemented and validated on a TMS320F28379D DSP-based experimental platform. Comparative results demonstrate its superiority over benchmark methods (e.g., SMC-STA). Key achievements include a rapid no-load startup time of 0.45 s, high steady-state precision with speed fluctuations suppressed to only 3 rpm, and superior disturbance rejection capability under sudden load changes, sinusoidal disturbances, and parameter perturbations. The method also yields favorable q-axis current response. These results confirm that the proposed strategy offers a high-performance, practical solution for advanced UAV servo control systems.

Machines doi: 10.3390/machines14060666

Authors: Andrei Baneasa Debora-Gabriela Buleandra Ivas Catalin-Dorin Mihai Olimpiu Tatar

Modular Mobile Robotic Systems (MMRSs) require simulation tools capable of supporting distributed control architectures, dynamic reconfiguration, and scalable experimentation. This work evaluates three complementary simulation strategies for a homogeneous MMRS composed of autonomous Two-Wheel Inverted Pendulum (TWIP) modules: (i) Webots, selected for rapid prototyping through its integrated GUI; (ii) Pinocchio, paired with the Jiminy simulator to enable modern rigid-body dynamics and control-oriented modeling; and (iii) PyBullet, chosen for programmatic flexibility and reinforcement learning (RL) compatibility. A minimal and controlled benchmark scenario was implemented across all platforms to isolate core simulation characteristics: two differentially driven robots were coupled using the most appropriate mechanism available in each environment and simulated for 1000 steps in headless mode while monitoring CPU usage, memory consumption, and execution time. In addition, a feature-based analysis focused on MMRS-relevant requirements, including dynamic reconfiguration, multi-agent scalability, and suitability for RL workflows.

Machines doi: 10.3390/machines14060664

Authors: Xiang Liu Chuan Zhao Fangchao Xu Wenhui Zhao Junjie Jin Rui Man Jichao Liu Feng Sun

Based on outer raceway control theory and a five-degree-of-freedom quasi-static model of angular contact ball bearings, a raceway wear model considering curvature radius variation is proposed, which couples the quasi-static model with a modified Archard wear formulation and a dynamic curvature radius update mechanism. As wear accumulates, the worn curvature radii are fed back into the quasi-static model to recalculate the raceway contact dynamic parameters. Taking the SKF 7012ACE/HCP4A spindle bearing as an example, the wear depth evolution and the variations of contact ellipse area, contact stress, sliding velocity, and wear coefficient with wear time are investigated under combined loads. The results indicate that as wear progresses, the raceway curvature radii increase, leading to a decrease in contact ellipse area but an increase in contact stress and sliding velocity, which in turn accelerates the wear process. The findings demonstrate that the degradation of raceway curvature radius has a cumulative and non-negligible influence on wear evolution and should be incorporated into bearing wear calculations for more accurate life prediction.

Machines doi: 10.3390/machines14060665

Authors: Hüseyin Tayyer Canseven Mustafa Ercire Merve Cömert Abdurrahman Ünsal Nur Sarma

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments.

Machines doi: 10.3390/machines14060663

Authors: Zixuan Chen Qiaozhong Li

To overcome the inherent limitations of conventional offline programming in adapting to dimensional deviations and assembly-induced errors during robotic welding of ship structures, this paper proposes a point-cloud-enhanced visual scanning paradigm that enables automatic weld seam identification and collision-free trajectory planning. A dedicated monochromatic vision system is rigidly integrated onto a six-axis industrial robot, enabling high-fidelity feature extraction and geometric contour reconstruction for the precise localization of multi-configuration weld seams. The proposed approach substantially reduces manual teaching operations, enhances environmental adaptability in unstructured shipbuilding workshops, and improves global positioning accuracy. The core technical contributions are threefold: (1) systematic design and precision calibration of the integrated robotic vision system, including a hand–eye calibration procedure; (2) development of a hybrid 2D image-3D point cloud processing pipeline that combines SURF and FLANN for image stitching with RANSAC-based plane segmentation and PCA-driven contour reconstruction; and (3) extensive experimental validation across five distinct workpiece configurations. These results confirm the system’s strong applicability for intelligent and efficient shipbuilding welding, significantly outperforming conventional offline programming, which exhibits deviations exceeding 5 mm under identical conditions. Quantitative error analysis demonstrates that the online recognition method achieves a weld localization root mean square error (RMSE)of 0.82 mm, a standard deviation of 0.45 mm, and a verified maximum absolute deviation of 1.5 mm.

Machines doi: 10.3390/machines14060662

Authors: Chaewon Jo Seonghwi Kim Ju Lee

Electric power steering (EPS) motors require low torque ripple, low cogging torque, and smooth torque output to ensure precise control and driving comfort. However, consequent pole permanent magnet synchronous motors (CP-PMSMs), although advantageous in reducing permanent magnet usage, exhibit an imbalanced magnetic flux distribution due to the iron poles, resulting in even-order harmonic components in the back electromotive force (BEMF) and significant torque ripple. In this paper, a rotor pole arrangement for CP-PMSMs is proposed to improve torque characteristics for EPS applications. Symmetric and asymmetric pole arrangements are introduced to modify the magnetic flux distribution and suppress harmonic components generated by the iron poles. In addition, the iron pole arc ratio is selected as a key design variable and analyzed for each model to achieve low torque ripple while maintaining torque performance. The electromagnetic characteristics of the proposed structures are evaluated using finite element analysis under identical operating conditions. The results show that the torque ripple of the proposed models is reduced by approximately 33.3%p and 34.1%p compared with the conventional CP-PMSM, and the cogging torque is also significantly reduced. Although average torque decreases, overall torque characteristics improve due to reduced torque ripple and harmonic components. These results demonstrate that the proposed rotor pole arrangement effectively enhances torque quality in CP-PMSMs without increasing axial length or requiring three-dimensional analysis.

Machines doi: 10.3390/machines14060661

Authors: Saugirdas Pukalskas Alfredas Rimkus Gabrielius Mejeras Donatas Kriaučiūnas Saulius Stravinskas Tadas Vipartas Andrius Ušinskas

This study investigates the effect of hydrogen injection pressure on combustion, energy, and emission characteristics of a spark-ignition engine under stoichiometric operating conditions. Experiments were performed on a four-cylinder Nissan HR16DE engine at 2500 rpm and 0.48 MPa brake mean effective pressure using gasoline and hydrogen-enriched blends containing 10%, 20%, and 30% hydrogen by mass. Hydrogen was injected into the intake manifold at pressures of 1.2, 1.4, 1.6, and 1.9 bar, while spark timing was adjusted to maintain peak in-cylinder pressure at 14–15 CAD after top dead center. Results showed that hydrogen mass fraction had a much stronger influence on engine performance than injection pressure. Increasing hydrogen content intensified combustion, shortened ignition delay, increased heat release rate and in-cylinder temperature, and reduced brake-specific fuel consumption by up to 36% compared with pure gasoline. Hydrogen enrichment also reduced HC and CO2 emissions, but increased NOx emissions. Effect of injection pressure was secondary and depended on hydrogen concentration. Under the investigated conditions, the lowest tested pressure, 1.2 bar, was generally the most favorable, especially at lower hydrogen fractions. Overall, hydrogen injection pressure acted mainly as a mixture formation control parameter, while hydrogen mass fraction remained the dominant factor determining engine behavior.

Machines doi: 10.3390/machines14060660

Authors: Xiaoqing Yang Lei Xu Guolin He Canyi Du Junjie Yu Haiyang Zeng

An abundant gear fault modulation signal is closely related to the gear fault type, especially to the gear fault severity. However, the modulation signal simultaneously includes coupled frequency modulation and amplitude modulation, which hinders the precise modulation separation and modulation-based gear fault severity assessment. Therefore, a new modulation separation method is proposed, which incorporates a rotation speed extraction technique based on the extreme value search, frequency modulation mechanism and Fourier series fitting. The rotational speed induced by the gear fault is first calculated by the extreme value search, which is then combined with a frequency modulation mechanism to solve the frequency modulation signal with the Fourier series fitting. Based on the vibration modulation signal model and Fourier series fitting, amplitude modulation is finally obtained. Simulation verifies the superiority of the proposed method in aspects of effectiveness and anti-noise performance compared with other modulation separation methods. The maximum relative errors of frequency modulation and amplitude modulation parameters under a signal-to-noise ratio of 0 dB are 2.9125% and 4.1143%, respectively. Two modulation intensity indicators regarding fault-induced frequency modulation and amplitude modulation signals are presented to assess gear faults. Experiment results also demonstrate the effectiveness of the proposed method in the severity assessment of misalignment and tooth breakage. Therefore, the research provides a new technique for gear fault severity assessment based on the frequency modulation or amplitude modulation signal.

Machines doi: 10.3390/machines14060659

Authors: Mohamed Eladawy Ryma Lebied Mahmoud A. Elsadd

Maximum power point tracking (MPPT) is a critical function for maximizing energy extraction in photovoltaic (PV) systems. Due to the inherently dynamic nature of the maximum power point under varying irradiance conditions, achieving fast convergence, low steady-state oscillations, and high tracking efficiency remains a challenging research problem. This paper proposes a hybrid ANN-based MPPT strategy for photovoltaic systems operating under rapidly changing environmental conditions. The proposed approach integrates a rule-based operating-condition estimation stage with a recurrent ANN-based control stage, enabling adaptive duty-cycle generation using measured PV voltage and current signals. Unlike conventional MPPT techniques, the proposed method utilizes operating-region estimation together with an extended ANN input feature vector and a recurrent backpropagation neural network to improve dynamic tracking performance under abrupt irradiance variations. In addition, a composite loss function is adopted to enhance tracking accuracy, guidance consistency, and control smoothness. The ANN is initially trained offline and subsequently refined online using lightweight incremental adaptation to maintain effective operation with a low computational burden. The proposed MPPT strategy is evaluated against P&O, FLC, SMC, and existing ANN-based approaches. Simulation results demonstrate improved tracking performance, faster dynamic response, and reduced steady-state oscillations under abrupt irradiance variations.

Machines doi: 10.3390/machines14060658

Authors: Junpeng Cheng Yihua Zhu Chao Luo Jiawei Yu Wenzhe Hao Guangqi Li Zhiyong Dai

Permanent magnet synchronous generators (PMSGs) are inevitably subject to aperiodic and periodic disturbances due to complex operating conditions and internal coupling effects. To improve speed regulation under such disturbances, this paper develops a hierarchical control framework that integrates a parameter-decoupled extended state observer (DESO) with quasi-resonant control. A novel parameter decoupling method enables independent tuning of the observer bandwidth and controller parameters, while the quasi-resonant control module specifically targets periodic torque ripples caused by the tower shadow effect. Simulation results under stochastic wind conditions confirm that the proposed QR-DESO significantly outperforms conventional methods, reducing the speed tracking root mean square error (RMSE) by 61.8% and the total harmonic distortion (THD) to 0.17%. The system also exhibits strong robustness against ±20% parameter mismatches, validating its effectiveness for offshore wind power applications.

Machines doi: 10.3390/machines14060656

Authors: Sota Amano Renke Liu Hideyuki Sawada

This paper introduces a novel diameter-varying spherical robot that locomotes by local shell deformation and rolling. The robot is based on a dodecahedron-inspired shell structure equipped with 12 movable outer modules driven by a central screw-driven lifting mechanism. Unlike conventional spherical robots that mainly use the internal mass shift or internal rotating units inside a rigid shell, the proposed robot changes its locally deforming geometry by extending selected outer modules. To investigate the resulting rolling behavior, a physical simulation environment was constructed in Unreal Engine coupled with an external Python controller through OSC-based communication. The physical simulation preserves the essential geometry of the robotic body including control circuits situated in the core part and provides a repeatable platform for observing contact-driven rolling locomotion under time-managed operation. As the initial operational observation of the diameter-varying spherical robot, hardware observations were conducted to examine whether the same deformation-induced rolling principle could be physically realized in the fabricated robot. The paper presents the design of the spherical robot, the locomotion principle, physical simulation environment and the rolling simulation, and the experimental verification of the fabricated robot.

Machines doi: 10.3390/machines14060657

Authors: Kang Wang Yaqiang Zhu Qingjin Peng Runhua Tan

Agile innovation is becoming increasingly important for complex mechatronic products. Existing studies often remain at the project management level and offer limited operational guidance for conceptual structural design. This paper proposes an agile innovation design method that integrates the Length–Time (LT) dimension and Theory of Inventive Problem Solving (TRIZ) to translate user feedback into engineering-oriented conceptual solutions. First, user pain points are organized into a fishbone-based functional model, and core problems are mapped to LT dimensions using a natural-language processing rule set. Second, a neural network trained on cases of technological evolution predicts the corresponding TRIZ evolution law. Third, structurally similar engineering cases are retrieved based on LT-dimensional similarity and transformed into conceptual schemes by structural mapping. Finally, the technique for order preference by similarity to an ideal solution is used to rank alternative schemes with explicit normalization, distance calculation, and sensitivity checking. The method is demonstrated through the conceptual redesign of a vertical-axis wind turbine.

Machines doi: 10.3390/machines14060654

Authors: Xikun Wang Weijia Li Junlei Huang Fayou Liu

Large Unmanned Underwater Vehicles (UUVs) are playing an increasingly critical role in complex marine missions due to their enhanced payload and endurance capabilities. However, the safe recovery of these platforms remains a significant challenge, complicated by their high inertia, strong hydrodynamic interactions, and unpredictable environmental disturbances. In particular, the nonlinear coupling effects between the mechanical structure and the hydrodynamic environment exert a considerable influence on the system, accounting for nearly 50% of the tension on the arresting cable. To address these challenges, this paper proposes a variable tension control strategy for a UUV underwater arresting recovery system, utilizing a Well-Shaped Reward Entropy-regularized Proximal Policy Optimization (WSR-E-PPO) algorithm. In this framework, the real-time velocity and displacement of the UUV are utilized to represent the spatiotemporal characteristics of the recovery state, and a hybrid reward function integrating sparse and continuous rewards based on Potential-Based Reward Shaping (PBRS) is designed. Simulation results demonstrate that the proposed method enables the UUV to return to the docking point without oscillation, while effectively limiting the total recovery time to approximately 80 s—a 37.5% reduction compared with existing methods. Furthermore, the strategy ensures smoother tension regulation throughout the process. These findings provide a solid technical foundation and assurance for the stable and safe underwater recovery of large UUVs.

Machines doi: 10.3390/machines14060653

Authors: Jinhao Ma Meilin Song Xue Li Feng Zhang

Thermoelectric refrigeration systems employing the Peltier effect are widely applied in electronic cooling and environmental control. The performance of such systems is influenced by inevitable uncertainties in key parameters that can lead to noticeable variations in system responses. However, previous sensitivity analyses have been limited to flat-plate thermoelectric systems; annular thermoelectric refrigeration systems have not been analyzed. Accordingly, this study conducted a dispersion analysis of an annular thermoelectric refrigeration system by treating the input parameters as random variables that follow normal distributions and generating samples using Latin hypercube sampling. The changes in three response indicators—the cold-end temperature, refrigeration efficiency, and exergy efficiency—with small shifts in the mean and standard deviation of each input parameter were subsequently calculated to determine their influence on system performance. The results indicated that a slight decrease in the hot-side temperature significantly enhanced overall system performance, with an average influence weight of 0.45, and a moderate increase in the cold-side heat absorption improved both the refrigeration and exergy efficiencies, with an average influence weight of 0.29; by contrast, changes in the annular geometric parameter had relatively weak effects on system performance and the dispersion of system responses, exhibiting an average influence weight of only 0.02. Therefore, this study demonstrated an effective approach for evaluating the uncertainty inherent in annular thermoelectric refrigeration systems and provides a reference for optimizing their designs to ensure reliability.

Machines doi: 10.3390/machines14060655

Authors: Qiuyang Zhou Xiaoyu Xian Zhengyu Chen Lei Yan Yuming Fan Kexin Yin

Few-shot fault diagnosis is a challenging task in rotating machinery health monitoring because only limited labeled fault samples are available in practical industrial scenarios. Under such conditions, deep learning models are prone to overfitting and may fail to extract stable fault-sensitive features from vibration signals. Moreover, the weak fault-related components are usually coupled with operating-condition variations, background vibration, and environmental noise, which further degrades the discriminability and generalization ability of diagnostic models. To address these problems, this paper proposes a complex-valued disentangled representation learning network for few-shot fault diagnosis of rotating machinery. First, a direction-pair complex augmentation strategy is developed for triaxial vibration measurements. Two directional vibration components are selected and organized as the real and imaginary branches of a complex-valued input, which increases sample diversity under few-shot conditions. Then, a lightweight complex-valued convolution block is designed to model the coupled dynamic characteristics between different vibration directions and extract fault-sensitive representations. Furthermore, a dual-branch disentangled representation structure is developed to decompose the learned features into fault-sensitive representations and condition-related interference representations. To enhance the separability of fault embeddings under limited samples, a cosine-based disentangled representation loss is introduced, which improves intra-class compactness and inter-class discrimination while suppressing irrelevant interference information. Finally, a few-shot diagnosis strategy is constructed to identify fault categories with only a small number of labeled samples. Experimental results demonstrate that the proposed method consistently outperforms representative methods in terms of diagnostic accuracy, feature separability, and robustness, especially under extremely limited labeled samples.

Machines doi: 10.3390/machines14060652

Authors: Erkan Caner Ozkat

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework for LBW state classification from a single optical microphone, evaluated on an open dataset (183 AA1050 welds, fs = 2.5 MHz) under a five-class taxonomy: lack of fusion, lack of connection, sound, marginal, and piercing. The contributions are: (i) a compact 1-D CNN encoder on a mel-scale STFT spectrogram, reaching the highest macro-F1 (0.72 mean across three-fold replicate-out cross-validation) and 100% piercing recall in every fold—a multi-representation fusion variant adding a wavelet-packet decomposition and a 24-feature library targeting the 8, 63 and 110 kHz keyhole-resonance peaks was evaluated as an ablation arm and did not survive cross-validation, so the proposed model is mel-only; (ii) a systematic benchmark against six classical-ML and four deep learning baselines in which Transformer-hybrid ablations and ACGAN-style augmentation underperform compared to the compact CNN on the 122-sample training set, with the Transformer underperformance confirmed by a 30-configuration grid search over learning rate, weight decay, and dropout (best tuned macro-F1 = 0.441 vs. CNN 0.724); and (iii) a Grad-CAM analysis that recovers the keyhole-resonance bands without prior knowledge. A single optical microphone is thus a viable real-time alternative to multi-sensor stacks for battery-pack laser welding.

Machines doi: 10.3390/machines14060651

Authors: José I. Pedrero Miguel Pleguezuelos Andrés F. Hidalgo Miryam B. Sánchez

In a planetary gear system, the planet phasing depends on the number of teeth in the sun and the ring and the number of planets. When the tooth numbers are both multiples of the number of planets, all planets mesh at the same relative position—which is called synchronous configuration—and the input torque is shared evenly among them. Otherwise, the configuration is asynchronous, or sequentially phased, and the torque sharing is uneven. This directly influences the instantaneous load sharing between the external planet–sun and internal planet–ring meshes, consequently altering both load-induced tooth deflections and the resulting transmission error. The profile relief, frequently used to avoid the mesh-in impact, influences the teeth contact along the interval of relief, which also affects the load distribution, mesh stiffness, and transmission error. Since the transmission error is a source of dynamic load, noise, and vibrations, its peak-to-peak amplitude should be controlled, and the geometry of the profile modification provides an efficient tool. In this paper, the transmission error of spur planetary gears is studied with an analytical model previously developed, based on the minimum elastic potential energy. The study also assesses the influence of the depth and length of the tip relief and compares the behavior of synchronous and asynchronous configurations. As a result of this analysis, it has been found that the variation in the amplitude of transmission error is significantly lower in sequentially phased configurations and reaches the minimum variation for the adjusted depth of relief and medium length of relief. Furthermore, an odd number of teeth on the planets results in a higher mesh stiffness than an even number, which induces a slightly lower peak-to-peak transmission error.

Machines doi: 10.3390/machines14060650

Authors: Yanhong Peng Fernando Gomez-Bravo

Artificial intelligence is increasingly reshaping the foundations of robotics by enabling machines to perceive complex environments, learn from interaction, make adaptive decisions, and collaborate with humans in a safe and effective manner [...]

Machines doi: 10.3390/machines14060649

Authors: Zhuming Bi Song Cai Jeremy Schaffer

Bone staples that are in fixation of fractures or arthrodesis are mostly fabricated using Nitinol. While the attractiveness of this type of staple is widely recognized, concerns have been raised about the suitability of these staples in patients who are allergic to nickel. In this paper, current studies on bone staples are discussed and have found that existing bone staples are mostly Nitinol-based and are inapplicable to some patients who are allergic to Nitinol. The concept of digital triad (DT-II) is introduced to model, verify and validate bone staples with the consideration of installing parameters in an inserting process. A design study is defined to investigate the impact of two main inserting parameters on clamping forces of a staple. The study leads to the conclusions that with an appropriate adjustment on inserting parameters, new Ni-free β-Ti Alloy can be used to produce bone staples with clamping forces equivalent to those that are made from Nitinol-based materials. These findings may guide orthopedic surgeons in the selection of bone staples for use in a given fracture fixation or arthrodesis case.

Machines doi: 10.3390/machines14060648

Authors: Long Tan Chao Yang Yu Wang

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome this limitation. The proposed UAV integrates four complementary morphing strategies—shear-type variable sweep, variable span, morphing wingtip, and a continuously variable camber trailing edge—to adapt its geometry for different flight phases. An automated parametric modeling platform is developed, enabling the dynamic generation of 3D CAD models driven by design variables. This geometry is coupled with a suite of analysis modules for aerodynamics, propulsion, weight estimation, flight performance, and radar cross-section. The multi-mission profile, including takeoff, climb, cruise, turning, and landing, is decomposed into several phase-specific single-objective optimization subproblems, which are solved using an elitist real-coded genetic algorithm. The results quantify the optimal morphing configurations for each phase, demonstrating significant performance gains over the baseline, such as a 17% increase in range. Critically, the study analyzes the trade-off between aerodynamic benefits and the weight penalty of morphing mechanisms, revealing that both range and maneuverability are the most sensitive to the added weight. The proposed framework uses mission-phase-specific optimum geometries to define the required morphing envelope, actuation ranges, and net performance benefit of a candidate morphing flying-wing UAV after considering mechanism-induced mass penalties. This framework provides a quantitative basis for mission-driven morphing decisions and establishes a viable approach for designing highly adaptive next-generation UAVs.

Machines doi: 10.3390/machines14060647

Authors: Lei Wang Menghan Wei Shunjie Zhu Xiaoxiao Wang Xuejian Ge

This study developed a frequency-domain fault-tolerant iterative learning control (ILC) strategy for spatially interconnected systems subject to external disturbances and actuator faults. An equivalent one-dimensional system model was established by performing model transformation and reformulation on the original spatially interconnected system. On this basis, the iterative learning control law was designed using two-dimensional system theory, and the closed-loop system was further characterized as an iterative process model. By applying the generalized Kalman–Yakubovich–Popov (KYP) lemma, it was demonstrated that within a finite frequency range, the resulting discrete repetitive process can guarantee both batch-wise stability and monotonic asymptotic convergence of the tracking error. Experimental results obtained from a precision collaborative machining platform verify the effectiveness of the proposed control method.

Machines doi: 10.3390/machines14060646

Authors: Edoardo Risaliti Francesco Del Pero Andrea Antonacci Gabriele Arcidiacono

Railway screw couplings are safety-critical, yet service failures show fatigue cracking at geometric discontinuities. This work assesses the response of two UIC screw-coupling components—the shackle and trunnion—under longitudinal forces from Traction–Emergency Braking (TEB) manoeuvres. A linear-elastic 3D finite element model was built for 42CrMo4/AISI 4140 steel, idealising the threaded load transfer with an RBE2 condensation and the hook–shackle interface with a tied contact to provide a repeatable baseline. Longitudinal force histories were generated in TrainDy for a freight consist and mapped to Regions of Interest; fatigue was evaluated in Altair HyperLife using rainflow counting, Goodman mean-stress correction, and Palmgren–Miner accumulation on a uniaxial S-N curve. For the 636 kN envelope case, the model predicts an axial displacement of 0.985 mm and von Mises stresses in several relevant regions near the nominal yield strength. Fatigue results rank the trunnion pin fillet as the governing hotspot: representative TEB sequences yield damage indices greater than 1 (often of order 20), while a lower-amplitude braking block shows negligible damage. Overall, the analysed spectra leave little endurance margin for the current geometry and support redesign of critical radii and more realistic contact/boundary modelling.

Machines doi: 10.3390/machines14060645

Authors: Christian Lourido Kishan Reddy Raghunath Vikram Kapila

As robots and humans start to share common spaces and perform collaborative tasks, it has become critical to facilitate information exchange between them for communicating and interpreting each other’s intentions. By overlaying virtual objects on a view of the physical world, mixed reality (MR) technology offers a compelling approach for designing innovative models of human–robot interaction (HRI). For robot manipulators, mobile MR frameworks that allow a user to communicate a goal position for the robot’s end effector have been widely studied. However, HRI applications that may require other relevant information for the manipulator to complete more complex tasks remain unexplored. Thus, we propose an MR-enhanced HRI framework, deployed on a touchscreen tablet, that utilizes a virtual arrow object to communicate force intent (i.e., location, direction, and magnitude) to the manipulator and provide visual force feedback to the user. To evaluate the system performance and user experience, we conducted a user study with 25 participants who used a manipulator robot to complete four insertion subtasks, reporting a task success score of 96%, a usability overall mean score of 4.35 out of 5, and a low task load index of 21.49 out of 100. The results show that the MR-HRI framework is intuitive to operate, allowing users to successfully perform assigned tasks by effectively communicating their intentions through the virtual arrow.

Machines doi: 10.3390/machines14060644

Authors: Jiajia Zhang Weijun Zhang

Despite their demonstrated effectiveness in modeling sensor interaction networks for industrial fault diagnosis, graph neural networks (GNNs) still encounter two key limitations: black-box operation that lacks transparency in fault identification and propagation analysis, and unreliable attention mechanisms whose weights fail to faithfully reflect the genuine relevance of sensors or their interactions. To tackle these challenges, we put forward the Stochastic Mask Causal Graph Network, a novel framework that integrates a learnable stochastic masking mechanism guided by the information bottleneck principle. Unlike conventional attention-based or post-hoc approaches, our method automatically suppresses label-irrelevant graph components while preserving causally relevant structures, thereby providing faithful inherent interpretability without biased assumptions and effectively removing spurious correlations to enhance generalization. Comprehensive experiments on realistic complex industrial system datasets demonstrate that the proposed method achieves superior diagnostic accuracy and enhanced interpretability compared with existing advanced approaches.

Machines doi: 10.3390/machines14060643

Authors: Julián Álvarez Alejandro Fernández Pablo Zapico Natalia Beltrán Pedro Fernández David Blanco

This work presents the development and validation of an automated 3D scanning device based on two opposed consumer-grade Apple TrueDepth sensors integrated into a controlled rotational architecture, designed for the digitization of complex freeform surfaces such as the external cranial geometry. The system design was guided by a prior metrological characterisation of the sensor’s distance-dependent behaviour and complemented by an additional study of the influence of surface orientation, from which a suitable operating window for complete head acquisition was derived. On this basis, a mechatronic system was implemented comprising a mechanical structure, electronic hardware, a control architecture, and a calibration procedure that registers the local point clouds from both sensors into a common global coordinate system. Geometric validation was performed using symmetric and asymmetric cranial phantoms digitized with both the proposed device and a professional reference scanner. Surface comparison revealed localized discrepancies concentrated in fine anatomical details, while the cranial vault showed good overall agreement, with RMS deviations of 0.314 mm and 0.286 mm for the symmetric and asymmetric phantoms, respectively. Morphometric consistency was assessed through the cranial vault asymmetry index (CVAI), for which both systems produced the same general trend with a maximum difference of 0.2%. These results demonstrate the feasibility of the proposed system as a geometrically consistent and morphometrically reliable instrument for head surface digitization under controlled laboratory conditions.

Machines doi: 10.3390/machines14060642

Authors: Cong-Thanh Pham Thanh-Dat Mai Duc Huynh Hien Van

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency.

Machines doi: 10.3390/machines14060641

Authors: Wei-Jen Chen Zhang-Hua Fong

Most existing analytical models for gear skiving define the cutting edge indirectly through hypothetical generating gears. This abstraction introduces an inherent geometric inconsistency between the theoretical cutting edge and the true conical rake surface of the cutter, limiting prediction accuracy and hindering measurement-driven compensation. To address this limitation, this study proposes a unified analytical and measurement-driven framework for cylindrical gear skiving that eliminates the generating-gear assumption entirely. A direct surface-based meshing formulation is developed by enforcing positional coincidence and tangential compatibility directly on the cutter’s conical rake surface, ensuring strict geometric consistency with the physical cutting mechanism. To incorporate real cutter deviations, a tension-controlled spline reconstruction method is introduced to recover smooth and curvature-stable cutting edge curves from noisy three-dimensional measurement data, overcoming the oscillation and instability commonly associated with high-order polynomial fitting. By integrating direct surface-based meshing, spline-regularized reconstruction, and CNC-oriented kinematics within a single formulation, this work establishes a complete digital chain for precision skiving cutter modeling, simulation, and compensation, providing a practical foundation for advanced tool design and manufacturing optimization.

Machines doi: 10.3390/machines14060640

Authors: Jiacheng Zhou Zhuo Chen Shengli Lv Libin Zhang Feng Hu Min Shen Fei Fan Lianqing Yu Mingzhang Chen Chengcheng Wang Xiaoshuang Xiong

Air-jet looms are widely used for weaving lightweight fabrics due to their outstanding high performance. To enhance the overall structural strength of air-jet looms and reduce operational vibration, dynamic analysis and structural parameter optimization of the loom frame system were carried in this study. First, after the structural designing and finite element modeling, the modal tests of the loom frame system were conducted. The modal results showed the high consistency between the simulation and experiment, confirming the accuracy of the dynamic model. Then, the dynamic characteristics and maximum stress data of the frame were obtained and analyzed through vibration tests and simulation calculations. The results indicated that the maximum deformation occurred at the middle of the beam while the maximum stress occurred at the connection between the lower beam and the wall panel. Moreover, the loom frame parameter optimization model was constructed based on the BA-HHO-SVR (Balanced Adaptive—Harris Hawks Optimization—Support Vector Regression) algorithm, demonstrating excellent learning and predictive capabilities. Eventually, the optimal combination of the frame structural parameters was obtained through above optimization algorithm. Furthermore, the effectiveness and reliability of the optimal combination were verified by finite element calculations. The vibration analysis method and optimization strategy proposed in this study provide valuable guidance for subsequent structural design and optimization of the high-end textile equipment.

Machines doi: 10.3390/machines14060638

Authors: Ümit Yılmaz

Reliable fault detection and diagnosis (FDD) plays a key role in inverter-driven permanent magnet synchronous motor (PMSM) systems, especially in applications where operational continuity cannot be compromised. In this work, a hybrid deep learning framework is developed by combining one-dimensional convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and a multi-head self-attention mechanism. The model targets multi-class fault classification in a three-phase PMSM inverter system. Its effectiveness is evaluated on a publicly available experimental dataset consisting of 10,892 multi-sensor samples collected under nine operating conditions, including normal operation, open-circuit faults, short-circuit faults, and half-bridge overheating scenarios. To avoid temporal data leakage, a block-aware chronological splitting strategy is applied. Model hyperparameters are determined through a validation process involving 24 different configurations. The proposed CNN–BiLSTM–Attention model achieves a macro F1-score of 0.9681 ± 0.0195, accuracy of 0.9810 ± 0.0102, Matthews correlation coefficient (MCC) of 0.9757 ± 0.0130, and ROC-AUC of 0.9996 ± 0.0003 over five independent runs, achieving the highest accuracy and MCC among all evaluated models; although the Random Forest baseline attains a marginally higher macro F1 score (0.9747) by operating on temporally aggregated features without temporal modelling, the proposed model provides superior discrimination across the full confusion matrix structure alongside end-to-end temporal interpretability via SHAP. Model interpretability is provided through SHAP (SHapley Additive exPlanations) GradientExplainer analysis, revealing that temperature-related features dominate fault discrimination, particularly for over-heating conditions, while current imbalance features are critical for distinguishing open- and short-circuit faults.

Machines doi: 10.3390/machines14060639

Authors: Karim Ahmadi Dastgerdi Seyed-Yaser Nabavi-Chashmi

Existing fault-tolerant control methods for multicopter UAVs often exhibit degraded performance under actuator faults and modeling uncertainties; therefore, this paper presents a robust-adaptive control algorithm for multicopter UAVs operating under actuator fault conditions. A data-driven approach based on an Artificial Neural Network (ANN) is employed to estimate actuator fault using IMU measurements. The ANN is trained using data generated from a closed-loop system controlled by a robust-adaptive control, rather than an open-loop configuration, improving its ability to capture realistic fault dynamics. To mitigate limitations in training data coverage, an adaptive mechanism is incorporated to enhance robustness under varying operating conditions. In addition of inherent fault-tolerant control characteristics of the robust-adaptive control, the estimated fault signals are used for motor speed compensation to enhance the robustness of the algorithm. The inner-loop controller is designed based on a robust-adaptive algorithm, ensuring system stability and robustness against model uncertainties and fault estimation errors, even the actuator faults change both system dynamics and actuation. The outer-loop Proportional–Integral–Derivative (PID) controller is employed to achieve accurate trajectory tracking. For validation and benchmarking, a standalone robust-adaptive controller and a model-based recursive least squares (RLS) estimator are also implemented. Simulation results demonstrate that the proposed ANN-based approach provides accurate fault estimation and effective compensation, resulting in improved tracking performance under actuator fault conditions. Furthermore, the proposed framework contributes to the development of a fault-tolerant UAV systems by integrating robust-adaptive control, ANN-based fault estimation, and actuator compensation into a unified architecture, thereby enhancing reliability, robustness, and tracking performance in the presence of actuator faults and modeling uncertainties.

Machines doi: 10.3390/machines14060637

Authors: Xinyang Fan Fenglei Ni

This paper investigates the conflicting multiple constraints and safety challenges in humanoid robot teleoperation for nonprehensile transportation tasks. The robot’s complex workspace and high degrees of freedom frequently conflict with highly dynamic task requirements, imposing stringent demands on coordinated motion. To address these issues, this paper proposes a Multiple-Constraint Safety-Critical Control Framework (MC-SCCF) featuring a hierarchical three-layer architecture. The top layer guarantees intrinsic safety against workspace boundaries using a continuously differentiable reachability surrogate model and an improved control barrier function (CBF)-based safe velocity filter for smooth deceleration. The middle layer maps user commands into pose-coupled reference trajectories to ensure task-level object safety, satisfying strict non-slip and non-toppling constraints. The bottom layer utilizes a quadratic programming (QP)-based inverse kinematics solver to achieve self-collision avoidance, coordinated motion, and optimal configuration while strictly enforcing joint and manipulability limits. Simulations and hardware experiments demonstrate that the MC-SCCF achieves real-time, high-precision reachability evaluation and successfully coordinates task dynamics with physical constraints, enhancing operational safety and the human–robot interaction experience.

Machines doi: 10.3390/machines14060635

Authors: Xinyi Xiao Nassim Nader David K. Orisekeh Rodrigo Gomez Juarez Byeong-Min Roh

Hybrid manufacturing (HM) integrates two or more distinct manufacturing processes—most commonly additive manufacturing (AM) and subtractive machining—within a coordinated workflow (often on a single platform) to achieve capabilities that neither process provides alone: high geometric freedom and rapid material addition from AM, plus tolerance, surface finish, and datum control from machining. This review first surveys major classes of hybrid manufacturing (additive–subtractive, multi-energy, multi-material, and assistive hybrid). It then focuses on multi-axis directed energy deposition (DED) combined with in-loop subtractive machining—arguably the most mature and industrially impactful HM configuration—emphasizing process planning methods, persistent limitations, and application domains, including repair/remanufacturing and functionally graded materials (FGMs). The review highlights current CAM strategies for hybrid DED, integrated planning frameworks, and emerging trends such as digital twins and closed-loop metrology-driven replanning.

Machines doi: 10.3390/machines14060633

Authors: Ganxin Jie Cailiang Zhang Junqing Ma Yang Yang Chuan Chen

This study investigates class imbalance in motor fault diagnosis. Fault samples, especially those at different severity levels, are often much fewer than healthy samples. To address this issue, a self-attention guided Wasserstein conditional GAN with gradient normalization (SWGAN) is proposed. The method is based on synchronized three-phase current and vibration measurements. It separately generates label-conditioned current spectra and vibration spectra to supplement minority fault classes. Self-attention is used to capture long-range spectral dependencies. Gradient normalization is introduced to improve adversarial training stability. The generated current and vibration spectra are then fused at the feature level and fed into a stacked autoencoder (SAE)-based multi-modal classifier. Experiments were conducted on a PMSM stator fault dataset and a variable-speed three-phase asynchronous motor dataset. On the PMSM dataset, SWGAN achieved highest accuracies of 98.90% and 97.81% under two fault-category imbalance settings. On the variable-speed motor dataset, the proposed method achieved accuracies of 98.10% and 97.65%, respectively. These results show that SWGAN can provide effective supplementary samples for minority fault classes. They also indicate that the proposed method improves diagnostic performance under both fixed-speed and variable-speed conditions.

Machines doi: 10.3390/machines14060636

Authors: Pan Fan Meng Tian Yuhang Du Guodong Lang Liang Li Yafeng Li

To address the challenges of severe background interference and unstable grasping of irregular maintenance tools in complex railway ballast environments, this paper proposes a robotic system that integrates enhanced visual perception with bio-inspired soft grasping. The core components of the system include a lightweight detection network (RA-YOLO), asymmetric “Fin Ray” soft fingers, and a visual servoing control framework. By embedding the CBAM attention mechanism and incorporating Mosaic data augmentation, RA-YOLO achieves robust feature extraction under complex backgrounds. The fingertip topology is optimized using the Yeoh constitutive model and finite element analysis, thereby improving stiffness under heavy loads and overall adaptability. Experimental results demonstrate that proposed RA-YOLO achieved a mAP@0.5 of 93.6% on the standard test set with an inference speed of 105 FPS. The visual-servo localization experiment an average Euclidean positioning error of 1.03 mm, with the maximum component-wise absolute error remaining below 2.5 mm. In system-level grasping experiments involving five categories of irregular tools, the integrated system achieved an overall grasping success rate of 91.8%, indicating its potential for automated tool recovery in unstructured railway maintenance environments.

Machines doi: 10.3390/machines14060634

Authors: Ancuța-Mihaela Aciu Claudiu-Ionel Nicola Maria-Cristina Nițu Marcel Nicola

Using dissolved gas analysis (DGA) to diagnose faults in power transformers is essential for preventing major failures and improving the reliability of power systems. This paper proposes a diagnostic framework based on the Duval Pentagon Combined Complex (DPCC). This framework integrates the areas of Duval Pentagons 1 and 2, along with the electric arc and paper charring subregions, into one geometric structure. This results in 16 distinct defect regions. A physically consistent dataset was generated, respecting the relative proportions of the five key gases (H2, CH4, C2H6, C2H4, and C2H2) and the typical concentration ranges in ppm reported in the literature. Four machine learning (ML) classifiers were trained using this dataset: Neural Network (NN), Fine Gaussian Support Vector Machine (SVM), Weighted K-Nearest Neighbors (KNN) and Bagged Trees Ensemble. Cross-validation results indicate high performance for all analyzed models. The Wide NN classifier had an overall accuracy of 96.53%. The Fine Gaussian SVM reached 96.07%. The Bagged Trees Ensemble achieved 96.26%. The Weighted KNN had an accuracy of 95.74%. The area under the curve (AUC) values were close to 1 for most classes, confirming the regions defined by DPCC were highly separable. Compared with conventional ML-based methods relying on individual classifiers and standard geometric representations, the proposed method provides more accurate defect separation, increased robustness in transition regions, and improved stability of the diagnostic decision. The integration of the DPCC representation with a weighted probabilistic ensemble framework reduces ambiguities between classes and enables more accurate identification of defects associated with electric arcs and insulation paper carbonization. To improve the robustness of the classification in transition zones, we implemented a Weighted Probabilistic Ensemble framework, in which each model’s contribution is proportional to its validation accuracy. This strategy minimizes the impact of geometrical ambiguity on the decision and provides a more reliable defect type estimate. The proposed methodology demonstrates that combining DPCC geometric modeling with modern ML techniques allows for the development of a robust, automated diagnostic system suitable for power transformer monitoring and predictive maintenance applications.

Machines doi: 10.3390/machines14060631

Authors: Víctor Manuel Vega-Gutierrez Israel Martínez-Ramírez Jorge Andrés Ortega-Contreras Sebastian Santarrosa-Rodriguez Isaí Espinoza-Torres Felipe J. Torres Miguel Ernesto Gutierrez-Rivera

The tube-notching process is widely used to manufacture structural joints and ducting systems for fluid transport. In these applications, accurate intersection angles and proper fit-up geometry are essential to ensure reliable assembly and system performance. Consequently, CNC-based automation is increasingly adopted to improve productivity in operations where precision and cycle time are critical. The main problem, however, lies in the complexity of generating accurate cutting trajectories for tube–tube intersections and converting them into machine-executable commands. This study addresses this gap by proposing a simple, novel mathematical model for toolpath generation capable of producing intersection profiles at arbitrary joint angles, including lateral offset (non-coaxial) configurations. A systematic procedure was developed to convert the resulting trajectories into G-code, which was processed in a low-cost CNC plasma cutter designed to experimentally validate the toolpaths. The machine incorporates a fourth axis to enable bevel cutting during tube processing. Experimental results demonstrate stable operation, high dimensional accuracy (error ±0.1°), and consistent cut quality for trajectories generated by the proposed model, confirming the feasibility of the low-cost CNC plasma system and its scalability to diverse fabrication requirements.

Machines doi: 10.3390/machines14060632

Authors: Rujun Li Junci Cao Dong Li Xu He Ren Liu

This article investigates the design and optimization of a high-speed surface-mounted permanent magnet synchronous motor with an amorphous stator core for hydrogen compressor applications, where high efficiency and high power density are critical. Amorphous materials offer significant potential for reducing excessive core losses at high electrical frequencies—a key requirement for high-speed hydrogen compression systems. However, manufacturing processes such as annealing and cutting degrade their magnetic properties, making raw ribbon data insufficient for accurate loss calculation. To address this, the magnetic performance degradation of the processed amorphous core is characterized by introducing a loss modification factor, improving core loss prediction accuracy. Based on a conventional silicon steel baseline motor operating at 60,000 rpm, a finite element model incorporating the corrected amorphous loss data is developed by replacing the stator core with amorphous material. The Dowell analytical model for AC windings is employed to accelerate computation. A multi-objective evolutionary algorithm is then applied to optimize the motor geometry, maximizing efficiency while maintaining output power to meet the demanding requirements of hydrogen compressors. Finally, a prototype is manufactured and tested. The experimental results validate the efficiency improvement of the amorphous material-based motor and confirm the effectiveness of the proposed optimization methodology for hydrogen compressor applications.

Machines doi: 10.3390/machines14060630

Authors: Asiye Aslan

Exhaust Gas Temperature (EGT) is a critical parameter in Gas Turbines (GTs) in terms of performance monitoring, fault detection, and operational optimization. In this study, a comprehensive and data-driven modeling approach was developed to predict EGT under variable load conditions and different Inlet Guide Vane (IGV) positions in a 401 MW GT unit located in a Combined Cycle Power Plant (CCPP) with a single-shaft design. A large-scale dataset obtained from a total of 18,334 h of real operating conditions was used in the study. Operational parameters such as Gas Turbine Power Output (GTPO), IGV, Compressor Inlet Temperature (CIT), Fuel Gas Flow (FGF), and Lower Heating Value (LHV), together with environmental parameters such as Atmospheric Pressure (AP) and Relative Humidity (RH), were evaluated simultaneously, and the combined effect of these variables on EGT was investigated. In order to model the nonlinear relationships between EGT and the input variables, six different tree-based ensemble learning methods, namely Bagged Trees, Random Forest, Gradient Boosting, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), were applied and compared. The results showed that all models were able to predict EGT with high accuracy. The most successful model was LightGBM, which achieved the best overall prediction performance with a Coefficient of Determination (R2) of 0.9703 and a Root Mean Square Error (RMSE) of 1.5280. The analyses revealed that the most influential parameters affecting EGT were GTPO, CIT, FGF, and IGV, whereas the environmental variables had secondary but still significant effects. The proposed approach provides a reliable and computationally efficient tool for sensor validation, fault detection, and predictive maintenance applications.

Machines doi: 10.3390/machines14060629

Authors: Shiheng Liu Ziwen Wu Jianxiong Gao Wenlei Sun Yiping Yuan Likun Fan

To address the frequent occurrence of multiple fault types, the difficulty of feature extraction, and the susceptibility to noise interference in rolling bearings under complex operating conditions, this paper proposes a fault diagnosis method based on an improved one-dimensional convolutional neural network (1DCNN) integrated with a Transformer architecture. This approach leverages the 1DCNN to efficiently extract local impact and energy features from vibration signals, while the improved Transformer enables global modeling of long-range temporal dependencies, thereby significantly enhancing the recognition accuracy for multi-class fault signals and the generalization capability of the model. Experimental data are sourced from the Case Western Reserve University bearing fault dataset, with multi-channel vibration signals subjected to preprocessing and balanced sampling, and various types of simulated noise systematically introduced to comprehensively verify the noise robustness of the proposed model. Experimental results on the public dataset demonstrate that the improved 1DCNN-Transformer model achieves a classification accuracy of 99.43%, markedly outperforming traditional methods such as ANN, CNN, LeNet, and SVM. Further t-SNE visualizations and confusion matrix analyses reveal the method’s superior feature discrimination and high-precision performance across multiple fault categories. Tests under strong noise conditions further indicate that the model exhibits high robustness and excellent potential for engineering applications. In summary, the proposed method provides an efficient and reliable solution for intelligent fault diagnosis of rolling bearings in complex environments and lays a solid foundation for future model development and industrial deployment.

Machines doi: 10.3390/machines14060628

Authors: Zhengxing Li Jinfeng Li

Cascaded H-bridge (CHB) multilevel inverters are pivotal in high-power applications, such as renewable energy subsystems and motor drives, due to their superior modularity and harmonic performance. However, selecting the optimal number of levels remains a complex engineering trade-off between power quality, switching losses, and system complexity. This study presents a systematic investigation into CHB inverters ranging from three to twenty-one levels under carrier-phase-shifted sinusoidal pulse width modulation (CPS-SPWM) control. A detailed MATLAB/Simulink framework in version R2023a was established, incorporating a zero-order hold (ZOH) data synchronization protocol and parameterized macro-model MOSFETs to accurately quantify total harmonic distortion (THD) and individual switching energy dissipation. To evaluate the efficiency–quality equilibrium, a novel comprehensive evaluation index, the performance-to-loss ratio (PLR), is proposed. Simulation results indicate that while THD improves significantly with higher level counts, the marginal gains diminish beyond the 13-level configuration. Utilizing the PLR framework, the nine-level configuration is identified as a local optimum for cost-sensitive modularity, whereas the twenty-one-level setup provides the global optimum for high-performance scenarios where spectral purity is paramount. Accordingly, this proof-of-concept study provides a quantitative roadmap for designers and experimentalists to navigate the complex design space of multilevel inverters, enabling optimal allocation of hardware resources toward the net-zero vision while guiding future experimental efforts away from costly, exhaustive hardware characterization.

Machines doi: 10.3390/machines14060627

Authors: Qian Qian Zhao Jong Woon Kim Il Han Chung Won Young Yun

This paper deals with a redundancy allocation optimization problem (RAOP) for multi-indenture systems under common cause failures (CCF). A beta-factor model for CCF is applied to predict the system reliability in multi-indenture systems. Human errors are considered a main source of CCFs, and it is assumed that the effects of CCFs depend on the hierarchical system structure, and CCFs exist in redundancy at the lowest level (component level). A beta-factor model is used to represent CCF effects, including human-error-related factors such as maintenance, installation, and operational errors. The proposed model compares the reliability effects of component-level and module-level redundancy under CCF. A genetic algorithm (GA) is used to determine the optimal redundant objects and redundant numbers that maximize system reliability under a design cost constraint. The sensitivity analysis is performed to investigate the effect of model parameters on the objective function and the optimal solutions. The effects of CCF on the system reliability between module-level redundancy and component-level redundancy are compared in numerical examples.

Machines doi: 10.3390/machines14060625

Authors: Haidi Wang Hailong Zhang Yongjuan Zhao Chaozhe Guo Jiangyong Mi Yawen Li

Accurate vehicle state estimation is a critical prerequisite for electric vehicle motion control, yet its performance is highly sensitive to deviations in inertial parameters. Variations in vehicle mass and moment of inertia caused by changing loads can lead to model mismatch, thereby degrading the accuracy and robustness of state estimation. To this end, this paper proposes a hierarchical collaborative estimation framework that integrates the Maximum Correntropy Adaptive Unscented Kalman Filter (MCAUKF) with a Physics-Informed Neural Network (PINN) for inertial parameter identification and key state estimation in electric vehicles. The upper layer employs MCAUKF for robust online identification of unknown inertial parameters, such as vehicle mass and moment of inertia. The lower layer develops a PINN-based state estimator that incorporates physical constraints by embedding the coupled dynamic residuals of longitudinal, lateral, and roll motions into the supervised learning process, thereby enabling high-precision real-time estimation of key dynamic states, including yaw angle, longitudinal velocity, and roll angle. Simulation results demonstrate that the proposed method can effectively achieve coordinated estimation of inertial parameters and key states under varying load conditions and complex maneuvering scenarios, significantly improving overall estimation accuracy and robustness.

Machines doi: 10.3390/machines14060626

Authors: Ziguang Wang Hanping Zhang Shelong Du Yu Zhang Xiaoguang Guo Yu Liu Zhihua Sha Song Yuan

Polycrystalline diamond has great potential for power-device heat dissipation and precision manufacturing owing to its exceptional hardness, excellent thermal conductivity, and superior wear resistance. However, the challenges of material removal and controlling thermal damage hinder high-quality machining. In this study, a three-dimensional transient temperature field model is developed for multi-pulse nanosecond laser ablation of polycrystalline diamond. The model incorporates the Gaussian spatial distribution of laser energy, Lambert–Beer depth-dependent absorption, multi-pulse energy superposition, and three-dimensional heat conduction. The heat conduction equation is numerically solved using MATLAB, and lateral and longitudinal temperature gradients are introduced to characterize thermal accumulation and material removal behavior. The model is validated by comparing the predicted ablation depths with experimental measurements, which show consistent variation trends. The results indicate that increasing the number of scans, single-pulse energy, and pulse frequency enhances thermal accumulation, expands the microgroove width, and increases the ablation depth, whereas increasing the scanning speed weakens thermal accumulation and reduces the ablation depth. In addition, a shorter pulse width increases the instantaneous power density and strengthens near-surface thermal concentration. This study provides theoretical guidance for controlling the heat-affected region and optimizing process parameters in precision laser machining of diamond.

Machines doi: 10.3390/machines14060624

Authors: Ivana Klačková Leonid Y. Yuferev Zuzana Ságová Vladimir V. Kuvshinov Boris A. Yakimovich Oleg V. Maschev Pavol Božek

This study analyzes the energy consumption of unmanned aerial vehicles (UAVs) under the influence of external environmental factors, including ambient temperature, wind speed, and turbulence, as well as their effect on battery performance and flight endurance. The aim of the study is to improve the understanding of UAV energy efficiency under different climatic and operational conditions relevant to agricultural and infrastructure-monitoring missions. The results show that external factors substantially affect both UAV power demand and operating time. In particular, wind and turbulence increase energy consumption because of additional aerodynamic drag and the need for repeated stabilization efforts. The proposed framework integrates aerodynamic, thermal, and battery-related factors into a unified energy-consumption model and introduces empirical correction coefficients to improve the applicability of the calculations under different operating conditions. The study also discusses practical approaches to energy-aware mission planning, including route-level considerations and adaptation of flight parameters to environmental conditions. The obtained results indicate that UAV energy efficiency should be assessed not only from nominal battery parameters, but also with account for environmental loading and mission geometry. The proposed framework is relevant for both agricultural and rural infrastructure applications and may support comparative endurance estimation, preliminary route planning, and further development of UAV energy-assessment methods for operation in challenging climatic and operational environments.

Machines doi: 10.3390/machines14060623

Authors: Mingyu Shen Lixiang Duan Jiaqi Zhu Shuang Cai Stevan Dubljevic

Deep learning-based methods have achieved promising results in planetary gearbox fault diagnosis. However, complex vibration signals often contain redundant information and disturbance-related responses, and varying operating conditions can cause distribution discrepancy between training and testing data, leading to degraded diagnostic performance. To address this coupled challenge, a diagnostic method termed DART18 (Domain Adaptation diagnosis of ResNet18 embedded with a Time–frequency enhanced attention mechanism) is proposed. DART18 is designed to improve both the discriminability and transferability of fault features by combining input-level time–frequency refinement with feature-level distribution alignment. Specifically, vibration signals are first transformed by the optimal generalized S-Transform (OGST) into time–frequency representations to characterize their joint time–frequency information. Then, TFEAM is designed to refine the input time–frequency representations before deep feature extraction. By aggregating features from different receptive fields and adaptively emphasizing fault-related time–frequency structures, TFEAM provides more informative inputs for subsequent feature learning. On this basis, ResNet18 is employed to extract fault features, and multi-kernel maximum mean discrepancy (MK-MMD) is introduced to statistically align the feature distributions of the source and target domains by jointly using labeled source-domain data and unlabeled target-domain data. Experimental results on two planetary gearbox datasets under multiple domain adaptation tasks show that DART18 consistently outperforms five comparative methods in terms of accuracy and F1-score, demonstrating its effectiveness and robustness for fault diagnosis under varying operating conditions.

Machines doi: 10.3390/machines14060622

Authors: Jiyan Zeng Yujie Cheng Chen Lu Zili Wang Xuanbo Liu Xinwei Wang Dengwei Song

Aircraft maintenance scheduling is a critical task in air transportation and national defense security, characterized by complex multi-step procedures, strict precedence dependencies, and multi-resource constraints involving personnel skills and equipment availability. Traditional scheduling methods and standard metaheuristic algorithms often suffer from insufficient model adaptability, poor population diversity, premature convergence, and complex encoding schemes that require frequent feasibility checks. To address these challenges, this paper proposes a comprehensive optimization framework based on the Resource-Constrained Project Scheduling Problem (RCPSP) model. A decimal priority-based encoding method is introduced to replace traditional integer permutation encoding, significantly reducing computational complexity and enhancing search space continuity. Furthermore, an improved hybrid Particle Swarm Optimization algorithm integrating reverse learning and partial random operations (RL-PSO) is developed. The reverse learning mechanism expands the global search space by generating reverse particles, while partial random operations maintain population diversity and prevent premature convergence. The proposed framework converts priority encoding into feasible schedules through a priority sorting and left-shift resource allocation strategy. Simulation experiments on maintenance tasks involving up to 50 aircraft demonstrate that RL-PSO achieves optimization accuracy of 332 min, convergence speed of 92.07 s, and stability of 2.8843 min in standard deviation, which are superior compared to standard PSO, Simulated Annealing, and Teaching–Learning-Based Optimization combined with the serial schedule generation scheme (SSGS). The method effectively balances global exploration and local exploitation, making it suitable for complex, large-scale aircraft maintenance scenarios. Future work will extend the framework to multi-objective optimization and dynamic scheduling environments.

Machines doi: 10.3390/machines14060621

Authors: Erbiao Yu Shunlu Wang Zuhou Teng Lei Wang Xiaoteng Tang

This paper proposes LA-PPO, an improved Proximal Policy Optimization algorithm for quadruped robot locomotion control on mixed terrain. To address partial observability, temporal dependence in contact states, and non-uniform importance of historical information in complex-terrain quadruped locomotion, LA-PPO integrates Long Short-Term Memory (LSTM) and Multi-Head Attention (MHA) within an Actor–Critic framework. The LSTM module models temporal dependencies in historical observations, while the MHA module adaptively emphasizes historical information most relevant to the current action decision. Based on IsaacGym, we construct a mixed-terrain environment consisting of flat regions, sloped regions, and random rough-terrain regions and conduct algorithmic comparisons, statistics over multiple random seeds, reward component ablation studies, and attention mechanism analyses for both walking and trotting gaits. Simulation results show that LA-PPO achieves the highest final reward and the longest mean episode length in both gaits. Compared with the PPO baseline, the final reward and mean episode length are improved by approximately 42.3% and 42.7%, respectively, in the walking task, and by approximately 39.8% and 25.7%, respectively, in the trotting task. Real-robot tests further show that the learned policy can perform walking and trotting on flat ground, sloped terrain, and random rough terrain, demonstrating preliminary sim-to-real transfer capability.

Machines doi: 10.3390/machines14060620

Authors: Yu Fang Zhaorong Li Liang Zhu Zhen Wu Yan Ping Kai Zhou

Deep neural networks can fit nonlinear bearing vibration responses, but their learned parameters are difficult to relate to contact deformation, rolling element angular position, and other acceleration-generating mechanisms. To improve physical traceability in data-driven bearing dynamics identification, this study develops a physics-informed SINDy-NN with a mechanism-guided feature library. This paper presents a novel approach for constructing a physics-informed SINDy-NN (Sparse Identification of Nonlinear Dynamics-based Neural Network) and demonstrates its application in identifying bearing dynamics. A 5-DoF (five Degrees of Freedom) bearing dynamics model is built, and the primary components influencing the acceleration response are analyzed. This analysis forms the basis for defining a physics-explainable basis function library for the SINDy-NN. For comparison, widely used polynomial and Fourier libraries are also employed to evaluate modeling accuracy and convergence speed. Furthermore, to address the limited number of bearing data, virtual states are generated by applying multiple finite differences to the acceleration signal, expanding the dimensionality of the model and enabling the use of a Multi-Input–Multi-Output (MIMO) model in SINDy-NN. Finally, experimental data from the FEMTO bearing test bench are utilized for validation. The results demonstrate that the physics-informed SINDy-NN offers superior modeling efficiency, with sufficient accuracy and improved interpretability compared to general SINDy-NN.

Machines doi: 10.3390/machines14060619

Authors: Hongluo Li Weixiong Li Xiang Li Yusheng Xiang Jingxiang Li Hongyang Xia Tianqing Su

With the emergence of global traffic problems, the development of safe, efficient, and reliable intelligent driving technologies has become a research hotspot. As a key component of intelligent driving technology, trajectory planning directly affects the safety, comfort, and operational efficiency of vehicles in complex traffic scenarios. Existing research typically relies on high-dimensional iterative numerical optimization or tightly coupled planning and control structures, leading to high computational complexity, insufficient real-time performance, and difficulty in ensuring trajectory smoothness. To address these issues, this paper proposes a decoupled and integrated trajectory planning and control method. Firstly, a method is proposed to construct the lateral trajectory based on a fifth-order polynomial and generate the longitudinal motion based on a quadratic acceleration model. Then, lane-change time is introduced as a single optimization variable to construct a cost function that balances comfort and efficiency, and continuous optimization is performed under longitudinal safety distance constraints. Finally, a horizontal and longitudinal hierarchical structure is constructed through model predictive control to solve the direction and speed adjustment problems and achieve high-precision tracking of the optimal trajectory. To verify the effectiveness of the proposed method, coupled simulation verification of trajectory generation and vehicle dynamic response is performed based on a joint simulation platform of MATLAB/Simulink and Carsim. The simulation results show that the proposed method can generate smooth, efficient, and controllable overtaking trajectories; significantly reduce computational complexity; and meet safety constraints, thus verifying the feasibility of the proposed method in complex lane-changing scenarios.

Machines doi: 10.3390/machines14060618

Authors: Kexin Kuang Sen Du Xuanwen Jia Bowen Zhang Longyu Li Weixuan Jiao

Pumping stations serve as the foundation platform for large-scale vertical fluid machinery, and their structural dynamics directly govern the vibration levels and long-term reliability of the installed pump units. In low-head vertical pumping stations, the interaction among the massive underwater substructure, flexible above-ground powerhouse, and surrounding backfill soil creates a complex dynamic system whose behavior remains insufficiently characterized. This study presents a comprehensive dynamic analysis of a large-scale vertical pumping station using a high-fidelity three-dimensional finite element model that incorporates the powerhouse superstructure, submerged concrete substructure, and backfill soil. Modal analysis under four boundary condition scenarios—varying in soil participation and interface contact conditions—systematically quantifies the influence of soil–structure interaction on natural frequencies and mode shapes. Resonance verification against three primary excitation sources—rotational frequency (4.917 Hz), blade passage frequency (24.583 Hz), and rotor–stator interaction frequency (196.667 Hz)—is extended from the first 50 modes to the 400th mode to assess potential high-order resonance risks. Results show that the roof slab, with its large span and low stiffness, exhibits the highest vibration susceptibility. For the rotational frequency, modes 4–12 fall below the 20% code-specified safety margin but rapidly exceed the threshold thereafter. For the blade passage frequency, the separation ratio decreases progressively with increasing mode order within the first 50 modes, and the extended analysis up to the 400th mode shows that the separation ratio remains well above 20% throughout modes 51–400. Consequently, no substantial resonance risk exists for the blade passage frequency within the entire computed range. The rotor–stator interaction frequency remains safely separated with margins exceeding 95%. These findings demonstrate the profound influence of soil–structure interaction and confirm that, despite a decreasing trend in frequency separation at higher orders, the blade passage frequency poses no substantial resonance risk up to the 400th mode. This work provides a rigorous analytical framework for vibration-informed design and optimization of pump foundation systems, with direct implications for the reliability and operational safety of large-scale vertical fluid machinery.

Machines doi: 10.3390/machines14060617

Authors: Xianlei Yang Ning Chen Yinghui Wang Kexin Wang Donghao Xie Yinbao Cheng Yingqi Tang

To ensure the credibility of measurement data from the Track Geometry Detection System (TGDS) and to achieve its dynamic and accurate evaluation, this paper analyzes and assesses the sources of uncertainty in the measurement of track geometric irregularities by the track inspection system based on a calibration test bench in the laboratory. To address the issue that the track inspection system is prone to sporadic outliers under electromagnetic interference and vibration, while conventional statistical methods are sensitive to outliers and tend to overestimate the repeatability uncertainty, this paper introduces a robust statistical method based on median absolute deviation (MAD) to evaluate the uncertainty introduced by repeatability. This robust approach effectively suppresses the influence of outliers by using the median instead of the mean and absolute deviations instead of squared deviations, thereby yielding a more realistic and reliable estimate of repeatability. Taking track gauge measurement as an example for uncertainty evaluation, experimental results show that the expanded uncertainty U = 0.64 mm, which satisfies one-third of the tolerance requirement for track gauge measurement, verifying the feasibility of the proposed method. The quantitative results of uncertainty sources in this paper can be used as Type B input for uncertainty evaluation in field practical measurements, providing a reliable metrological basis for the uncertainty evaluation of track inspection systems. Meanwhile, the dynamic evaluation of track inspection systems is realized, filling the gap in their dynamic and reliable evaluation under complex interferences.

Machines doi: 10.3390/machines14060615

Authors: Farshad Jabari Baxter Gonzalez Meysam Khaleghian

This study examines the influence of adding different profile geometries on the rollability performance of a wheeled robot released from various heights under controlled conditions. Three profile configurations were parametrically designed, computationally modeled, and optimized using a physics-based simulation framework. The optimized designs were then 3D-printed and attached to a robot chassis and evaluated alongside a baseline configuration (no profile addition). Rollability success was defined as the chassis returning to a stable, on-the-wheels configuration after launch. Experiments were conducted across two drop heights (75 cm and 130 cm), two launch speeds (0.8 m/s and 1.5 m/s), and launch angles ranging from −60° to +60°. The results demonstrate strong sensitivity of rollability performance to geometric configuration. Two of the optimized profiles showed significant improvements compared to the baseline. The best-performing profile exhibited robust performance across varying heights, speeds, and angles, whereas the other profile showed substantial performance gains at higher speeds and drop heights. These findings confirm that appropriate geometric optimization of profile structures can substantially enhance rollability stability for wheeled robots under dynamic impact conditions.

Machines doi: 10.3390/machines14060614

Authors: Haitao Li Yuanbiao Wang Yawen Wang Yunlong Yu Zehao Wang Weihao Su Yehao Zhu Lijun Yang Chi Ma Jie Li Meng Zhang

In the measurement of volumetric errors in CNC machine tools using multi-station laser tracking technology, the coordinate calibration accuracy of external measurement base stations is a key factor determining the system’s final accuracy. Traditional calibration approaches typically use the commanded positions of the machine tool directly to inversely determine base station coordinates, which results in strong coupling between inherent geometric errors and base station parameters. Consequently, the measurement accuracy cannot be properly evaluated, and metrological traceability of the results remains difficult to achieve. To address this issue, this paper proposes a novel calibration principle based on an independent external physical standard and develops a base station calibrator independently. This device employs a precision turntable, G5-grade precision spheres, and electromagnet groups to construct an equivalent target with four feature points at the spindle end. Verified by a high-precision coordinate measuring machine (CMM), the maximum difference in repeated calibrations of the device is 1.4 µm, indicating its excellent positioning repeatability. The calibrator was further applied to measure the positioning errors of a CNC milling machine, and comparative experiments were performed with a Renishaw XL-80 laser interferometer. The results indicate that the error variation trends obtained from the two measurement principles are highly consistent. In both the X-axis and Y-axis directions, the maximum deviations of linear errors are controlled within 3.7 µm, while the maximum deviations of angular errors remain within 4.6 µrad. Furthermore, the reliability of the system data was confirmed through an uncertainty analysis. The external physical standard developed in this study ensures that base station calibration accuracy is not affected by the inherent errors of the machine tool, providing a novel and reliable scheme for high-precision calibration and metrological traceability of machine tool spatial errors.

Machines doi: 10.3390/machines14060616

Authors: Huaibei Xie Qingliang Zi Yan Wang

The mixing valve is a key component of an ultra-high-pressure premixed abrasive waterjet system, in which the abrasive–water mixing uniformity plays a decisive role in determining the erosion and cutting performance of the jet. The geometric parameters of the mixing chamber inside the valve are therefore critical factors affecting this uniformity. In this study, the liquid–solid two-phase flow within the mixing chamber was numerically investigated using the Eulerian k–ε turbulence model coupled with the Fluent–Rocky DEM approach. Single-factor simulations were first conducted to identify the effective ranges of key structural parameters influencing the mixing performance. Subsequently, a response surface model was established to describe the relationship between the mixing efficiency (ME) and four critical chamber parameters, namely the throat diameter (TD), throat length (TL), abrasive inlet pipe diameter (AD), and the distance between the throat exit and the abrasive inlet pipe center (TE). Based on this model, the optimal structural parameters of the mixing chamber were determined. The results indicate that when TD = 4 mm, TL = 12 mm, AD = 10 mm, and TE = 7 mm, the simulated ME reaches 34.40% ± 0.49%, which is in close agreement with the predicted value of 34.57%. Experimental validation conducted on a premixed abrasive waterjet test rig shows that the mean absolute relative error between the simulated and measured ME values is 7.54%, which is below the 10% threshold, confirming the reliability and accuracy of the numerical model.

Machines doi: 10.3390/machines14060613

Authors: Massimo Stefanoni Imre Kovács Ákos Odry Peter Sarcevic

Magnetometers are widely used in robotics and localization systems but are susceptible to magnetic disturbances generated by nearby ferromagnetic objects, which degrade their accuracy. Traditional calibration methods often fail in dynamic environments, such as those encountered by mobile robots. This paper investigates a dipole model-based disturbance compensation method using a magnetometer array with increased sensor density, extending prior configurations with fewer sensors. The method leverages a detection system to locate disturbing objects, models them as magnetic dipoles, and estimates their parameters through optimization. Experimental validation was performed using magnetic fingerprints of metallic objects in multiple configurations. The results show that increasing sensor density significantly improves compensation performance, reducing magnetic field errors to below 6.64 μT and heading errors to 0.31 rad in most scenarios. In low-to-moderate disturbance scenarios, the four-sensor array achieved heading error improvements of approximately 13% compared to the uncompensated case. In contrast, the proposed nine-sensor array achieved improvements exceeding 50%. In highly complex scenarios involving multiple overlapping disturbances, performance degrades, highlighting limitations of the dipole-based model. These results indicate that increasing sensor density enhances robustness and suggest that adopting compact array geometries may further improve performance in highly disturbed scenarios.

Machines doi: 10.3390/machines14060612

Authors: Wenjie Zhou Yuanliang Zhang Lixue Ni

Autonomous UAV landing on dynamic unmanned surface vessel platforms is affected by deck motion and degraded visual observations, which may lead to unsafe final descent decisions. This paper proposes a fully decentralized reliability-enhanced predictive landing method that combines probabilistic perception, visual quality assessment, and model predictive control. Target posterior probability, perception uncertainty, and task-oriented image quality are fused into an online observation reliability index, which is used to adapt observation noise, constrain phase switching, and penalize unreliable descent opportunities. FFT-based dominant-mode identification and Kalman correction are also used to predict deck roll and pitch for landing-window selection. Simulation results show that the proposed method achieves a 90% small-angle landing success rate and keeps the touchdown attitude angle within 5°. Compared with standard MPC, landings within a 15° deck inclination increase from 24% to 82%, and the 80th-percentile touchdown inclination decreases by 9°. Compared with SHMPC, the average solution time decreases from 913 ms to approximately 104 ms per iteration. These results indicate that the proposed reliability-aware framework can reduce unsafe descent decisions and improve landing robustness while maintaining real-time feasibility under degraded maritime visual conditions.

Machines doi: 10.3390/machines14060611

Authors: Xiaoliang Chen Zhelu Wang Juntao Yan Gang Liu Yiqing Qiu Nannan Jiang

To address the coupled challenges of tire dynamic load regulation and ride comfort improvement in hydrogen-powered heavy-duty trucks (HPHDTs) under non-stationary road excitations, this study evaluates a magnetorheological (MR) damper-based semi-active front suspension system. A vehicle–road coupled dynamic simulation model was developed in MATLAB/Simulink (R2025b) using a Class C road profile, and three representative driving conditions, namely acceleration, deceleration, and constant-speed driving, were considered. Four control strategies, namely, interval type-2 (IT2) fuzzy control, type-1 (T1) fuzzy control, skyhook control, and PID control, were comparatively investigated. The results indicate that deceleration is the most critical operating condition, resulting in more severe tire–road interactions and poorer ride comfort than the other scenarios. Among the evaluated strategies, IT2 fuzzy control provides the best overall performance. Compared with the passive suspension, it reduces the front-wheel RMS dynamic load by 63.39% and improves ride comfort by 64.67% under deceleration. The T1 fuzzy and PID controllers provide moderate improvements, whereas skyhook control exhibits relatively limited effectiveness. These findings demonstrate that combining MR dampers with IT2 fuzzy control provides a feasible and robust approach for improving road friendliness, ride quality, and operational stability in advanced heavy-duty vehicle suspension design.

Machines doi: 10.3390/machines14060610

Authors: Huilin Tang Xuwen Zhang Boyan Hu Yan Wang Xin Shu

Wind turbine blades are prone to multiscale and weak-feature damage in complex natural environments. Accurate and efficient detection is crucial for ensuring the safe operation of wind turbine units. However, existing models struggle to balance detection precision, robustness, and lightweight deployment requirements. In this paper, we propose a lightweight model, Wind-YOLO, for wind turbine blade defect detection based on YOLOv11, with three core innovations: (1) We design a DynamicC3k2 that adaptively adjusts the convolutional receptive field for feature extraction, enhancing fine-grained feature capture of micro-cracks and weak-texture defects. (2) We construct a Cross-Stage Partial with Focused Linear Attention (C2FLA) that precisely focuses on defect regions via a linear attention mechanism, effectively mitigating complex background and noise interference. (3) We propose a Spatially Guided Gated Feature Pyramid Network (SGG-FPN) that optimizes multiscale feature transmission and aggregation through a gated fusion mechanism, improving adaptability to cross-scale defects from millimeter-level cracks to meter-level spalling. Extensive experiments on a dedicated wind turbine defect dataset show that Wind-YOLO achieves an mAP@0.5 of 80.9% and an mAP@0.5:0.95 of 37.1%, achieving an increase of 3.9 percentage points and 2.4 percentage points, respectively, compared with the baseline YOLOv11. Meanwhile, the model has only 2.34 million parameters (2.34 M) and a computational complexity of 6.0 GFLOPs. It delivers dual improvements in precision and lightweight performance, with superior environmental adaptability for real-time wind turbine inspection.

Machines doi: 10.3390/machines14060609

Authors: Volker Wittstock Burhan Ibrar Martin Dix

Due to their high damping and the associated low dynamic excitation of the machine tool, hydrodynamic guideways are necessary for precision machines such as grinding machines. This article summarizes the development of the measuring system that was integrated into the guiding rail of a linear hydrodynamic bearing and presents the experimental evaluation. The measuring system is aimed at providing a better understanding of the actual transient hydrodynamic pressure and lubrication condition during the reversing sliding motion in the liquid friction range. The system was checked for its frequency response to ensure that the expected pressure rise during the stroke motion can be measured both in relation to the rail width and to the point. The evaluation is based on Reynolds’ analytical hydrodynamic theory, as numerical calculation approaches themselves are also subject to considerable uncertainties, particularly with regard to the actual geometry of the lubrication gap. The novelty of the results lies in the possibility of analyzing the instationary behavior of a reversing linear bearing of a carriage in machine tools at very low pressures as a quasi-2D and 3D pressure curve. Finally, the new possibilities are demonstrated by analyzing the behavior of a carriage with concave sliding surfaces.

Machines doi: 10.3390/machines14060608

Authors: Haowen Xue Xiaoyong Li Shijing Wu Liang Liang

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy.

Machines doi: 10.3390/machines14060607

Authors: Heng Xu Xu Wang Yi Fang En-Ming Zhu Ju Guo Yi-Ming Dai Ji-Chao Li Ji-Qiang Li

The wide application of liquid hydrogen as a key energy carrier is severely limited by the reliability of high-pressure and low-temperature pumps. The traditional research on liquid hydrogen pumps relies on empirical analysis of isolated components, but fails to reveal the fundamental failure mechanism of these pumps. This review argues for a paradigm shift in the understanding and design of liquid hydrogen pumps. We systematically decomposed the failure of the liquid hydrogen pump into a thermodynamic-driven, cross-scale cascading process rather than the failure of isolated components. At the molecular level, the extreme thermal physical properties of liquid hydrogen (ultra-low latent heat and surface tension) can lead to widespread nucleation under slight thermal disturbances. At the mesoscopic scale, the initial perturbation is significantly amplified through the nonlinear dynamics of bubble clusters. This amplification is characterized by intense collapse and strong energy concentration due to the low density and low viscosity of liquid hydrogen. At the component level, this enhanced destructive energy will cause faults similar to phase transitions; namely, the liquid lubrication in the bearings will disappear, the seals will shift from viscous blockage to gas diffusion, and at the same time, the damage caused by low-temperature hydrogen cavitation and corrosion to the materials will also occur simultaneously. At the system level, the strong dynamic coupling among the subsystems has led to a nonlinear performance collapse. This cross-scale failure chain reveals the flaws in the classical cavitation theory, which is based on the assumptions of isothermal and inertia dominance. We have expounded the thermodynamic-dominated cavitation state in liquid hydrogen. This state is quantified by the Σ parameter and governs the multimodal behavior of low-temperature cavitation phenomena. To address this complexity, we have proposed a comprehensive framework that integrates multi-scale collaborative simulation and digital twin, combining molecular dynamics, CFD, system dynamics, and targeted experiments. This review proposes a candidate physical framework for addressing the reliability challenges of liquid hydrogen pumps. It also provides a clear roadmap for the next generation of inherently robust cryogenic fluid machinery, and offers a reference for the design of energy systems under other extreme conditions.

Machines doi: 10.3390/machines14060606

Authors: Vasileios I. Vlachou Karolina Kudelina Dimitrios E. Efstathiou Stavros D. Vologiannidis Tatjana Baraškova Veroonika Shirokova Theoklitos S. Karakatsanis

Induction motors are critical in modern industry, powering over 70% of industrial processes. Reliable operation is essential to minimize downtime and ensure production continuity. This paper proposes an integrated multimodal methodology for fault diagnosis and prognosis in induction motors, based on an extended Pearson and Gain feature fusion framework. The approach preprocesses vibration, current, voltage, torque, and speed signals through denoising, normalization, synchronization, and sliding-window segmentation. Over 200 features per window are extracted across time, frequency, envelope, wavelet, harmonic, slip-based, and MCSA domains. A key innovation is correlation-driven multimodal fusion, combining Pearson correlation, spectral coherence, cross-spectral energy, and mutual information to produce Gain-enhanced features with improved discriminative capability. Fault diagnosis is performed using RF, SVM, XGBoost, and MLP models, with time-aware data splitting to avoid temporal leakage. Prognosis employs a continuous Degradation Index (DI) modeled via Gaussian Process Regression for uncertainty-aware prediction, with failure probability and Remaining Useful Life (RUL) estimated from DI thresholds. Experimental results demonstrate that the proposed methodology achieves diagnostic accuracy above 97%, enhances feature relevance, and provides stable long-term prognostic performance, offering a robust framework for predictive maintenance of induction motors.

Machines doi: 10.3390/machines14060605

Authors: Jingjia Teng Wenjie Huang Shijie Yuan Manjiang Hu Hongmao Qin Yang Li Yougang Bian Bai Li

Reinforcement learning (RL) has been widely utilized for decision-making in highway on-ramp merging scenarios. However, most existing methods incorporate safety through reward functions, which may allow autonomous vehicles to trade safety for higher cumulative rewards. Moreover, personalized human risk preferences are rarely considered, making the learned policies difficult to adapt to heterogeneous user-specific risk requirements and potentially resulting in overly conservative or insufficiently cautious behaviors. To address these issues, this paper proposes a Risk-Aware Personal Preference-Based Safe Reinforcement Learning framework (RAPRL), for autonomous decision-making in on-ramp merging scenarios. Specifically, the high-level decision-making problem is formulated as a constrained Markov decision process (CMDP), in which safety requirements are explicitly represented as constraints rather than reward terms. To enable personalized safety regulation, a fuzzy logic mechanism is developed to adaptively determine the constraint cost limit according to the driver’s risk preference and the surrounding traffic density. The resulting safe RL problem is solved using a Lagrangian-based soft actor-critic algorithm (SAC). Furthermore, an Action Shielding Mechanism is designed to assess the potential risk of candidate actions before execution and replace unsafe or infeasible actions, thereby improving safety during both policy learning and execution. Theoretical analysis shows that the proposed shielding mechanism can reduce unsafe exploration and improve sample efficiency. Extensive simulations in on-ramp merging scenarios demonstrate that RAPRL effectively reduces safety violations while maintaining driving efficiency. Compared with the SAC Discrete method, the proposed method improves the success rate by 4.76% and reduces the collision ratio by 70%, indicating a better safety–efficiency trade-off.

Machines doi: 10.3390/machines14060604

Authors: Luca Zerbato Enrico Galvagno Antonio Tota Mauro Velardocchia

This paper proposes a calibration-oriented framework for cooperative adaptive cruise control based on a linear quadratic regulator formulation. A simulation-based architecture is developed by integrating the controller with a nonlinear longitudinal platoon model that explicitly accounts for actuator saturation and tyre–road friction limits, enabling the analysis of platoon behaviour under realistic operating conditions. A systematic offline calibration methodology is introduced based on multidimensional performance maps, relating key performance indicators associated with collision avoidance, comfort, and energy efficiency to controller and spacing-policy tuning parameters. The map-based approach enables a structured exploration of competing objectives and provides a quantitative assessment of controller sensitivity. The results show that the proposed framework can identify calibration regions that preserve collision-free operation in safety-critical manoeuvres while maintaining satisfactory tracking and comfort-related performance. In addition, the off-nominal model parameters analysis confirms that the proposed calibration approach remains effective under heterogeneous operating conditions, including vehicle parametric variation of mass, rolling resistance coefficient and drag. Overall, the results support the use of the proposed methodology as a practical tool for robust and performance-oriented controller calibration.

Machines doi: 10.3390/machines14060603

Authors: Yulong Xing Kun Li Xiaoshuai Li Congcong Liu Qi Wang Cong Peng Zisheng Wang

To address the challenges of severe multi-source coupling, easily masked spiking features, and limited selection of key responses in compound fault signals, this paper proposes a compound fault detection method based on a spiking attention residual network (SARN). This method uses the original time-domain vibration signal as input and constructs an end-to-end spiking neural network framework. A hierarchical spiking attention module is designed to enhance multi-level spiking features from both temporal response and feature channel perspectives, thereby highlighting fault-sensitive information and suppressing redundant responses. Furthermore, a cross-layer spiking residual gating mechanism is introduced to mitigate effective information attenuation in spiking neural networks and improve the representation capability of weak fault features. Simultaneously, a multi-label detection strategy is employed to jointly identify multiple fault attributes, thereby improving the recognition rate of coupled compound fault modes. Verification results show that the proposed method achieves high performance in compound fault detection tasks, and compared with other popular methods, it exhibits better feature separability and detection stability.

Machines doi: 10.3390/machines14060602

Authors: Qiuyang Zhou Xiaoyu Xian Lei Yan Yuming Fan Kexin Yin

Reliable and intelligent fault diagnosis of rotating machinery is crucial for the safety and stability of industrial systems. Nevertheless, the acquisition of labeled fault data is often difficult in practical applications because of the high cost of maintenance, the rarity of fault events, and the inherent safety risks associated with fault induction experiments. As a result, most real-world datasets consist mainly of healthy operating samples, which makes bearing fault diagnosis under fault-free training conditions particularly challenging. The objective of this study was to develop a simulation-driven diagnostic framework capable of identifying real bearing faults without using real fault samples during model training. To achieve this objective, pseudo-fault data were generated by superimposing periodic impulse–resonance responses, governed by theoretical bearing fault characteristic frequencies, onto healthy vibration signals. The synthesized dataset was further analyzed using wavelet packet decomposition and envelope spectrum analysis to extract discriminative time–frequency features. These features were then fed into the proposed Hierarchical Convolutional Attention Network (HCANet), which captured hierarchical multi-scale representations while emphasizing fault-related components. Furthermore, a Central Clustering Loss was employed to encourage intra-class compactness and enhance inter-class separability, thereby improving the generalization capability of the diagnostic model. Experimental validation on two bearing datasets showed that the proposed method achieved high diagnostic accuracy when tested on real fault samples, despite being trained exclusively on healthy signals and synthesized pseudo-fault samples. These results demonstrated the effectiveness of the proposed simulation-driven strategy and highlighted its potential as a practical solution for bearing fault diagnosis in zero-real-fault-data scenarios.

Machines doi: 10.3390/machines14060601

Authors: István Sztankovics

This study investigates the performance of tangential turning in machining two industrially relevant materials, 42CrMo4 low-alloy steel and X5CrNi18-10 austenitic stainless steel. A full factorial experimental design was employed to evaluate the effects of cutting speed, feed, and depth of cut on cutting force components, areal surface roughness parameters, and derived performance indicators. Regression models were developed to describe the relationships between process parameters and machining responses, resulting high coefficients of determination (0.935–0.996 for force components and 0.869–0.961 for surface parameters). Response surface analysis revealed that feed and depth of cut dominate cutting force behavior, while feed and cutting speed primarily influence surface roughness. Material-dependent differences were clearly observed. 42CrMo4 exhibited 10–30% higher cutting forces and higher roughness values, while X5CrNi18-10 showed lower forces but more variable surface characteristics due to strain hardening effects. Pareto front analysis demonstrated that 42CrMo4 enables simultaneous improvement of productivity and surface quality, whereas X5CrNi18-10 shows weaker coupling between these objectives. A composite sustainability index was introduced to integrate mechanical load, productivity, efficiency, and surface integrity. The results indicate that optimal conditions for 42CrMo4 reduce the sustainability index by up to 65%, while X5CrNi18-10 exhibits 20–40% higher index values under comparable conditions. The study highlights the importance of material-dependent analysis and multi-objective optimization for sustainable machining of advanced materials.

Machines doi: 10.3390/machines14060600

Authors: Adam Rose Spencer Stowell Eli Francom Audrey Christiansen Nathan Usevitch Larry L. Howell

The compliant rolling-contact element (CORE) bearing is a compliant mechanism similar to a planetary gear that provides customizable rotational torque while maintaining high radial stiffness, enabling it to simultaneously function as a parallel elastic element and a bearing replacement. This work reexamines the CORE bearing as a combined spring and bearing element for parallel elastic actuator systems. It introduces alternative CORE bearing designs, evaluates the accuracy of a previous constant-torque model proposed in the literature, describes a finite element analysis to corroborate run-up behavior, presents an optimization tool for generating bearing geometry, and includes radial stiffness experiments to assess the consequences of different fabrication methods. Together, these results provide design guidance for determining the suitability of CORE bearings for parallel elastic systems and for selecting appropriate parameters.

Machines doi: 10.3390/machines14060599

Authors: Runqing Ni Chengxin Zhong Sa Zhu

A multi-objective optimization framework is developed in this work to improve the driving cycle efficiency of IPMSMs, into which a fast-computational approach for PWM-induced harmonic losses is embedded. At a set of characteristic operating points, iron losses are first evaluated under sinusoidal current source (SCS) excitation using Computationally Efficient Finite Element Analysis (CE-FEA), while a Time-Stepping Finite Element Analysis (TS-FEA) spanning one quarter of the electrical period provides the copper losses. For scenarios where the machine is driven by a pulse-width modulation (PWM) voltage source inverter, the harmonic losses arising from modulation are quickly assessed through a small-signal time-harmonic finite element method (THFEA)-based model. The resulting optimization procedure seeks a trade-off between two conflicting goals: minimizing overall losses and reducing material cost. Given an equal cost level, incorporating PWM-related harmonic losses into the design loop cuts down the total loss by 3.11% relative to a baseline that only considers SCS-supply losses. The extra computational burden amounts to 17 h, representing a time rise of roughly 22.65%.

Machines doi: 10.3390/machines14060598

Authors: Guangyu Zhou Wenqian Xu Zhaosheng Meng Qingliang Zeng Qi Wang

To address the decline in detection accuracy caused by the degradation of grayscale features under environmental interference, a lightweight detection model driven by grayscale characterization, YOLOv8n-CoalGangue, is proposed based on an in-depth analysis of the dynamic variations exhibited by grayscale features. First, grayscale histograms are used to quantitatively evaluate the effects of illumination changes and moisture conditions on feature distributions, revealing that global grayscale aliasing and local texture degradation are the key visual feature bottlenecks. Guided by these unique findings, targeted technological innovations are integrated into the developed architecture. HGNetV2-G, which incorporates the GhostNet principle, is used as the backbone to reduce the incurred computational cost while preserving the core feature extraction ability of the model. A mixed local channel attention (MLCA) mechanism is introduced in the neck to filter background noise and focus on local high-frequency features, which helps overcome global grayscale aliasing issues. In addition, a DGFPN-based feature fusion network is constructed by combining RepGFPN and DySample, together with lightweight shared convolution detection (LSCD), which compensates for the loss of multiscale grayscale details without increasing the imposed parameter burden. Furthermore, the PIoUv2 loss function improves the bounding-box regression process in dense overlapping scenarios. Experimental results show that the proposed model achieves an mAP@50 of 97.2% with a 32% reduction in the number of parameters required (only 2.1 M). It also demonstrates strong robustness under six extreme industrial conditions, such as low illumination and coal dust occlusion, confirming the effectiveness of the design driven by grayscale characterization for practical green mining applications.

Machines doi: 10.3390/machines14060597

Authors: Yuankun Pei Liang He Jibing Chen

This paper reviews recent advances in additive manufacturing (AM) of aluminum alloys and proposes an integrated framework of materials, processes, microstructure, properties, and applications. Focusing on Laser Powder Bed Fusion (L-PBF) and Directed Energy Deposition (DED), it summarizes the major challenges of aluminum alloy AM, including hot cracking, porosity, and anisotropy, together with corresponding optimization strategies. The paper particularly highlights three additive manufacturing-specific alloy systems—Sc/Zr microalloyed, heat-resistant eutectic, and transition-metal-strengthened aluminum alloys—and clarifies their composition design and strengthening mechanisms. Finally, future trends in intelligent manufacturing, integrated alloy process design, and green development are discussed, emphasizing the importance of interdisciplinary integration for large-scale industrial applications.

Machines doi: 10.3390/machines14060596

Authors: Raul D. S. G. Campilho

Machine design, automation, and robotics are central pillars of modern manufacturing and engineering practice [...]

Machines doi: 10.3390/machines14060595

Authors: Alexios Papacharalampopoulos Panagiotis Stavropoulos

Digital twins enable closed-loop process control in smart manufacturing, yet no quantitative mapping exists between controller computational complexity and achievable real-time performance class. This paper aims to establish a quantitative mapping between controller computational complexity and achievable real-time performance class in digital twin-based process control, providing evidence-based deployment guidance for smart manufacturing. Three controller architectures—proportional–integral–derivative, model predictive control, and its robust variant—are implemented and timed on a finite-difference state-space model of a 1 mm steel slab under boundary heat flux, representative of laser-based and induction heating in manufacturing. Per-cycle latency is characterized through time series, cumulative distribution analysis, and deadline-miss rate on standard hardware without real-time operating system support. The proportional–integral–derivative controller satisfies hard real-time constraints with sub-0.05 ms latency; model predictive control with warm-starting achieves a 99th-percentile latency of 2.43 ms against a 10 ms deadline with zero misses across all tested prediction horizons. Robust model predictive control yields a mean latency of 770 ms—154 times the 5 ms control period—placing it firmly in the near-real-time class. A robust linear matrix inequality delay-margin analysis certifies closed-loop stability bounds across three uncertainty scenarios as a function of actuation delay; a finite-horizon induced-gain metric reveals a worst-case disturbance amplification peak near 100 control steps. Model predictive control is shown to compensate for actuation delays up to 50 ms that destabilize proportional–integral control, establishing it as the preferred architecture in latency-constrained digital twin deployments.

Machines doi: 10.3390/machines14060594

Authors: Dongting Ge Fan Bu Yufeng Zhuang Haoyuan Ni

Heterogeneous airground robotic systems, which integrate unmanned ground vehicles and unmanned aerial vehicles, have shown significant potential in complex autonomous missions. However, when deployed in urban canyons, dense high-rise buildings impose severe communication constraints on ground vehicles, necessitating the introduction of aerial vehicles as relays to maintain reliable connectivity. The resulting cooperative trajectory planning problem is challenging for three reasons. First, the kinematic and communication constraints are tightly coupled. Second, the optimization landscape is highly non-convex and non-differentiable. Third, the planner must balance topological exploration with real-time efficiency. To address these challenges, we propose a hierarchical cooperative trajectory planning framework for an air–ground robotic system. Specifically, in the upper layer, a heuristic-search-guided reinforcement learning mechanism is employed to narrow the search space and circumvent the sparse reward problem, rapidly generating an initial solution. Subsequently, the lower-layer planner utilizes an optimization-based solver, together with a corridor-based constraint formulation method, to refine the initial solution into a kinematically feasible cooperative trajectory. Ultimately, this strategy improves real-time efficiency while improving the quality of feasible cooperative trajectories. Extensive ablation studies and comparative experiments with representative baselines demonstrate that the proposed framework improves collision avoidance, communication reliability, trajectory smoothness, and computational efficiency in the tested urban canyon scenarios.

Machines doi: 10.3390/machines14060593

Authors: Yajie Li Ruilong Wu

With the continuous advancement of industrial intelligence, the application of hydraulic robots is becoming increasingly widespread, and the demand for their health diagnosis and maintenance is becoming more urgent. By integrating digital twin (DT) and deep learning technologies, this paper presents an intelligent fault diagnosis method for hydraulic robots based on multi-source information fusion. Firstly, a fault diagnosis architecture and solution for hydraulic robots based on DT technology are proposed. Secondly, a DT model of the hydraulic robot, which incorporates a 3D model and an attribute model with virtual–physical synchronization capabilities, is established, and a calibration method for the twin model is explored. Next, for four typical faults—leakage in the hydraulic system, valve sticking, damping hole blockage, and filter blockage—fault mechanism analysis and evolution process simulation are conducted on the established DT model. A multi-source high-quality dataset, covering normal operating conditions and multiple fault scenarios, is constructed to drive the data twin model. Finally, a feature extraction method combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanisms is proposed. This is followed by using a Random Forest (RF) classifier to achieve accurate fault diagnosis for various hydraulic system failures. The experimental results validate the effectiveness and practicality of this method.

Machines doi: 10.3390/machines14060592

Authors: Rafael Lara-Lopez Ignacio Gonzalez-Prieto Juan Carrillo-Rios Juan Jose Aciego Pablo Mora-Moreno Mario J. Duran Angel Gonzalez-Prieto

Multiphase electric drives have gained significant attention in recent years due to their enhanced efficiency and inherent fault-tolerant capability, making them a promising solution for modern high-performance applications. In this context, finite control set model predictive control (FCS-MPC) has emerged as an effective control strategy due to its flexibility in handling multivariable systems and multiple control objectives. Among its recent developments, variable-sampling-time approaches introduce an additional degree of freedom that enables more efficient adaptation of the control action, particularly reducing switching frequency. This variant of FCS-MPC schemes is based on a sequential structure, in which the direction of the desired current response is prioritized over its magnitude, even when implementation constraints limit its achievement. This work proposes an adaptive sampling time multivector model predictive control strategy (AST-MPC) for six-phase induction motor (6ph-IM) drives. The proposed AST-MPC combines multivector control actions with a threshold-based mechanism to incorporate magnitude information into the selection of control actions, typically governed by directional criteria. The designed approach is experimentally validated and compared under steady-state and transient conditions using multiple performance metrics. Results demonstrate that AST-MPC achieves improved current quality and reduced switching frequency, maintaining suitable dynamic performance and providing natural fault tolerance.

Machines doi: 10.3390/machines14060591

Authors: Ivaylo Nikolaev Ivan Georgiev Slavi Georgiev Georgi Iliev

The present Part I of the comprehensive study is dedicated to establishing the fundamental mathematical and experimental apparatus required for the multi-criteria optimization of the geometric parameters of an innovative three-rotor hydraulic pump with bilateral lantern meshing, subjected to the actual volumetric flow rate Q and the volumetric efficiency ηv. A complex approach integrating similarity theory, dimensional analysis, and mathematical modeling is employed to define and refine the two objective functions subject to optimization. Based on the area of geometric existence of the gearing and additionally imposed geometric and operational constraints, the exact domain for seeking the optima of Q and ηv is defined. Based on the statistical processing of experimental data, empirical dependencies of the objective functions are derived, accounting for the influence of the pump’s geometric parameters, the operational conditions and the physical properties of the fluid. The criterion equation of the volumetric efficiency, approximated using all experimental data, was obtained with a very high coefficient of determination R2=0.9898. The rest of the study, related to parameter optimization, is contained in Part II. In it, through numerical investigation and analytical proof, the universal optimal parametric values of the dimensionless geometric coefficients (the relative lantern radius rc,opt* and the shortening coefficient λopt) are identified to achieve maximum flow rate and volumetric efficiency. Furthermore, in Part II, a multi-criteria Pareto optimization (MINLP) is conducted to resolve the engineering conflict regarding the number of teeth z, and a direct simple algebraic dependency z=fp,n. The generalized results from both parts provide a methodological toolkit and recommendations for the optimal selection of the geometric parameters in the design of pumps of this type with respect to the actual flow rate and volumetric efficiency, in accordance with the operating conditions and regimes.

Machines doi: 10.3390/machines14060590

Authors: Yongkang Yang Qing Ye Yuchen Ding Ruochen Wang

This paper addresses the impact of complex mining environments and the nonlinear dynamics of hinged mining trucks on reverse path tracking control for autonomous mining trucks. We propose a neural-network-based sliding mode control (NN-SMC)-based control strategy for reverse motion to improve tracking accuracy and robustness. First, a tractor–trailer dynamic model is built, and the force characteristics at the coupling joint are analyzed to derive the reverse interaction forces, which simplifies trailer modeling and avoids the influence of uncertain tractor parameters. Next, a control scheme matching the simplified model is developed, where an optimized sliding surface is designed and a neural network adaptively tunes control parameters to reduce chattering and improve adaptability to challenging conditions. Finally, hardware-in-the-loop tests validate the simulation results. Both simulation and experiments show that, compared with conventional SMC, the proposed method reduces lateral displacement error by 13.98% and heading error by 18.96%, demonstrating the effectiveness of the control approach.

Machines doi: 10.3390/machines14060589

Authors: Garvit Singh Ankit Agarwal Kaushal A. Desai Laine Mears

Tool wear monitoring is essential for ensuring machining efficiency and product quality, particularly for difficult-to-machine materials such as Inconel 718 (IN718). Traditional deep learning models, such as Conventional Convolutional Neural Networks (CNNs), often struggle to capture complex wear patterns and lack accuracy across varying machining conditions while developing image-based tool wear identification systems. To address these limitations, this paper presents a Vision Transformer (ViT) model for identifying tool-wear categories during end-milling of IN718. The performance of the ViT-based model is systematically compared with a CNN-based EfficientNet-b0 model. The robustness and generalization of the ViT-based model are validated on two previously unseen image datasets: one with conditions similar to those of the training data and another acquired under varying lighting conditions. The results indicate that the ViT model outperforms the EfficientNet-b0 model in terms of classification accuracy and computational efficiency. The ViT model achieves higher accuracy with fewer training epochs and faster convergence. Furthermore, it exhibits strong generalization across different lighting conditions, demonstrating robustness to variations in the machining environment. The findings presented in this work clearly demonstrate ViT’s effectiveness in tool wear classification and its potential as a reliable, efficient algorithm for developing tool wear monitoring systems for practical machining applications.

Machines doi: 10.3390/machines14060588

Authors: Wenbin Tang Xingchen Jiang Jingzhe Wang

Part surface topography is characterized by complex multi-scale and multi-feature coupling, and accurate topography modeling is essential for predicting assembly precision in high-performance mechanical systems. Gaussian Process Regression (GPR) offers a principled, probabilistic framework for surface modeling from sparse measurements, but its performance depends critically on kernel function selection. A fixed single kernel lacks the flexibility to represent surfaces that simultaneously exhibit smooth trends, periodic textures, and linear drift. To address this limitation, an adaptive composite kernel method is proposed. Initial GPR residuals are analyzed through statistical hypothesis tests and spectral decomposition to identify which geometric features are present; matching base kernels—Squared Exponential (SE), Periodic (PER), and Linear (LIN)—are then selected and combined additively or multiplicatively. Experiments on three representative synthetic surfaces show that the composite kernels reduce RMSE by up to 95.09% relative to the single SE kernel. Validation on a machined part confirms that the method successfully transfers to real measured data, achieving a 30.65% RMSE reduction and raising R2 from 0.9536 to 0.9777. The results demonstrate that residual-analysis-driven kernel selection yields physically interpretable models with substantially improved reconstruction accuracy.

Machines doi: 10.3390/machines14060587

Authors: Yuchao Fan Bingyan Feng Feng Wei Yubin Huang Jian Li

Geometric errors in CNC machine tools accumulate along the tool path and directly affect machining accuracy. Traditional serial-chain-based volumetric error models, such as those based on the homogeneous transformation matrix (HTM) or screw theory, often exhibit ambiguous geometric definitions, weak traceability to measurement points, and increased computational cost due to repeated coordinate transformations and inverse mappings, limiting their suitability for real-time control. To overcome these challenges, this study proposes a parallel-type unified error vector transfer (EVT) framework, based on the Abbe and Bryan principles. In this framework, axis error motions are directly expressed as vectors and transferred to the tool center point (TCP), where they are superimposed to obtain total error contributions. Building on this principle, a unified normal volumetric error model (NVEM) is formulated using survival and sign factors. The unified NVEM is applicable to various types of three-axis machining centers, including horizontal configurations. In other words, differences in coordinate system definitions can be reconciled through coordinate transformation, allowing the unified NVEM to be consistently applied. Furthermore, a real-time error compensation controller (RECC) is embedded into the CNC kernel to compute compensation values within each interpolation cycle, ensuring deterministic and low-latency operation without external computation. Experimental validations on an XYFZ-type vertical machining center demonstrate that the proposed framework improves positioning accuracy by more than 72% and machining accuracy by 60.4%. These results confirm the feasibility, efficiency, and universality of the parallel-type unified EVT framework for real-time volumetric error compensation. Here, ‘parallel-type’ denotes the parallel superposition of independent error vector contributions, rather than a parallel kinematic mechanism.