Machines

23 pages, 3549 KB

Open AccessArticle

Multi-Agent Reinforcement Learning for Multi-UAV Pursuit with Full Planar Motion and a Limited Detectable Region

by Soobin Huh, Sungwon Lim, Hyeokjae Jang, Woohyun Byun, Suhyeong Yu and Woochul Nam

Machines 2026, 14(4), 413; https://doi.org/10.3390/machines14040413 (registering DOI) - 8 Apr 2026

Although previous studies have considered sensing constraints and UAV dynamics, most of them have used unrealistic sensing limitations and simplified dynamic models. Thus, these approaches can suffer from a significant discrepancy between simulation results and real-world deployment. To address this issue, this study [...] Read more.

Although previous studies have considered sensing constraints and UAV dynamics, most of them have used unrealistic sensing limitations and simplified dynamic models. Thus, these approaches can suffer from a significant discrepancy between simulation results and real-world deployment. To address this issue, this study incorporates high-fidelity sensing constraints and UAV dynamics into a multi-agent reinforcement learning approach, focusing on the practical interplay between FOV limitations and pursuit strategies. First, the proposed reward considers the sensing constraints via a gaze-alignment reward, which varies with the field-of-view condition, and a capturability reward that encourages transitions toward a capturable region. Second, realistic UAV dynamics, including lateral motion, forward motion, and yawing, are modeled in a simulation environment to reduce the sim-to-real gap. Quantitative evaluations demonstrated that the proposed formulation significantly improved the capture performance under diverse sensing conditions. The capturability reward increases the capture success rate by 11.4%. When the maximum speed of the evading UAV was 2 m/s faster than that of the pursuing UAVs, all capture trials failed when lateral motion was not used. However, when lateral motion was enabled, the success rate increased to 99.2%, highlighting the need for lateral motion. Full article

(This article belongs to the Special Issue Advanced Planning, Perception, and Control for Autonomous Vehicles and Robots)

40 pages, 1847 KB

Open AccessReview

A Review of Domain-Adaptive Continual Deep Learning Remaining Useful Life Estimation for Bearing Fault Prognosis Under Evolving Data Distributions

by Stamatis Apeiranthitis, Christos Drosos, Avraam Chatzopoulos, Michail Papoutsidakis and Evangellos Pallis

Machines 2026, 14(4), 412; https://doi.org/10.3390/machines14040412 (registering DOI) - 8 Apr 2026

Abstract

Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world [...] Read more.

Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven methods for bearing fault prognosis and remaining useful life (RUL) prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyses how these approaches have been individually and implicitly combined to cope with non-stationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions. Full article

(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)

16 pages, 18971 KB

Open AccessArticle

Enhancement of Surface Finish on FDM-Printed PCL via Robotic Burnishing for Biomedical Applications: An Indirect Measurement Approach

by Gabriele Scordamaglia, Carmine Borgia, Michele Perrelli, Francesco Gagliardi, Luigi De Napoli and Domenico Mundo

Machines 2026, 14(4), 411; https://doi.org/10.3390/machines14040411 (registering DOI) - 8 Apr 2026

Abstract

The Fused Deposition Modeling (FDM) process often produces parts with high surface roughness, limiting their end-use applications, especially in the biomedical field. This paper presents an experimental study on improving the surface finish of 3D-printed polycaprolactone (PCL) samples using a robotic burnishing process. [...] Read more.

The Fused Deposition Modeling (FDM) process often produces parts with high surface roughness, limiting their end-use applications, especially in the biomedical field. This paper presents an experimental study on improving the surface finish of 3D-printed polycaprolactone (PCL) samples using a robotic burnishing process. A key innovation is the development of a low-cost sensorless setup using a 5-DOF manipulator, which controls the applied force by correlating a precise robotic displacement with the known stiffness of springs via Hooke’s law. Ten PCL samples were tested using two burnishing directions: 90° (perpendicular) and 0° (parallel) relative to the printing orientation. The as-printed samples showed a highly anisotropic surface. The 90° trajectory (group 1) proved to be more effective in reducing primary roughness (

R_{a_{⊥}}

), lowering the mean

R_{a}

from

2.11 μ m

to

1.44 μ m

(a mean reduction of 29.9%). In contrast, the 0° trajectory (group 2) was more effective in reducing roughness

R_{a_{‖}}

, lowering its mean

R_{a}

from

0.225 μ m

to

0.144 μ m

(a mean reduction of 34.0%). The results demonstrate that the proposed sensorless system is a valid method for surface post-processing of FDM parts when the required forces fall below a specific threshold, ensuring a significant reduction in roughness without damaging the samples. The lower surface roughness obtained with the proposed post-processing strategy may represent a promising approach for improving the surface characteristics of FDM-fabricated polymer scaffolds intended for biomedical applications. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

16 pages, 3060 KB

Open AccessArticle

Friction Compensation Method Based on a Dual-Segment Simplified Static–Dynamic Friction Model

by Yukun Chen, Xuewei Li, Taihao Zhang, Enzhao Cui and Zhewei Wang

Machines 2026, 14(4), 410; https://doi.org/10.3390/machines14040410 (registering DOI) - 8 Apr 2026

Abstract

Nonlinear friction in the mechanical transmission system of machine tools induces transient stagnation of the feed axis as its velocity crosses zero, thereby giving rise to contouring errors in multi-axis machining and significantly degrading machining accuracy. To address this issue, a feedforward compensation [...] Read more.

Nonlinear friction in the mechanical transmission system of machine tools induces transient stagnation of the feed axis as its velocity crosses zero, thereby giving rise to contouring errors in multi-axis machining and significantly degrading machining accuracy. To address this issue, a feedforward compensation strategy is proposed based on a simplified static friction model (SSFM) with dual-segment and dual-parameter characteristics. The nonlinear friction is represented by a combination of a linear segment and an exponential segment, while the model incorporates two essential parameters that characterize the maximum friction force and the negative damping effect. Experimental results from two-axis circular trajectory tests show that the proposed SSFM reduces contour errors by approximately 73.4% and 79.2% at 600 mm/min and 2100 mm/min, respectively. To improve compensation under high-speed conditions, an acceleration-dependent dynamic correction is further introduced to establish the SDFM. The results show that the maximum contour error is further reduced to 1.44 μm and 1.49 μm at 3600 mm/min and 5000 mm/min, respectively. Compared with many existing reduced-order or hybrid friction models that rely on more parameters or more complex identification procedures, the proposed method provides a more compact and compensation-oriented modeling strategy for the velocity-reversal region of CNC feed systems. Full article

(This article belongs to the Section Automation and Control Systems)

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22 pages, 1860 KB

Open AccessArticle

Identification of Position-Independent Geometric Error in Five-Axis Machine Tools Using ANN Surrogate and Optimal Measurement Planning

by Seth Osei, Wei Wang, Qicheng Ding and Debora Nkhata

Machines 2026, 14(4), 409; https://doi.org/10.3390/machines14040409 (registering DOI) - 8 Apr 2026

Abstract

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic [...] Read more.

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic sampling of measurement poses, or computationally expensive global optimization procedures, which collectively limit their effectiveness in industrial environments. This study presents a unified identification framework that overcomes these limitations; it incorporates 3D offset parameters to enhance the decoupling of true geometric errors from non-PIGEs, an observability-driven measurement pose selection strategy to maximize the parameter sensitivity, and an ANN-surrogate model to accelerate high-dimensional global optimization. A genetic algorithm is used to optimize the measurement points based on the observability index of the machine tool. The ANN-surrogate model enhances the identification accuracy of error parameters (11 PIGEs + 3 offsets) through precise kinematic models, global exploration, and final refinement. Experimental validation on a five-axis machine tool demonstrates a volumetric error reduction of 88.615% after compensation, with RMSE decreasing to 0.4337 μm. Sensitivity analysis reveals that PIGEs contribute up to 75.26% of the total inaccuracy, while offset parameters capture 24.74% of the error from thermal and non-PIGE sources. The results confirm the method’s superiority over other techniques in terms of identification accuracy, efficiency, and robustness, providing a practical solution for high-precision applications in the manufacturing industries. Full article

(This article belongs to the Section Advanced Manufacturing)

29 pages, 5362 KB

Open AccessArticle

Multi-Objective Design Optimization of an MW Machine Using Hybrid Evolutionary Algorithm and Artificial Neural Networks

by Srikanth Pillai, Islam Zaher, Mohamed Abdalmagid and Ali Emadi

Machines 2026, 14(4), 408; https://doi.org/10.3390/machines14040408 (registering DOI) - 8 Apr 2026

Abstract

In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1

M

W

power, >13 kW kg⁻¹ [...] Read more.

In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1

M

W

power, >13 kW kg⁻¹ of power density, and efficiency >96%. To address these requirements, this paper proposes an electromagnetic design of a high-speed, power-dense, 1

M

W

radial-flux Permanent Magnet Synchronous Machine (PMSM) for aerospace propulsion applications that achieves NASA targets. Achieving high-specific-power objectives necessitates geometry optimization that simultaneously minimizes motor mass while maximizing output power. This paper presents a faster optimization algorithm that hybridizes Genetic Algorithm and Artificial Neural Network (ANN)-based surrogate modeling to optimize the motor for multi-objective goals. The proposed framework employs a multi-objective approach targeting maximum torque output and efficiency within a minimum motor mass. This approach, using an ANN-based surrogate, significantly reduces optimization time by saving 95% of the time compared to FEM simulations. The optimized 1

M

W

motor attains 98% efficiency and an active power density of 24.87 kW kg⁻¹. The various stages of the optimization are presented in detail and a comparison of the time saving using the proposed algorithm is outlined. To demonstrate the feasibility of design, a detailed electromagnetic analysis, stator thermal analysis with a jet impingement design, and magnet demagnetization risk analysis were also presented. Full article

(This article belongs to the Special Issue Advanced Estimation, Control, and Optimization Techniques for Synchronous Machines in Next-Generation Electrified Systems)

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17 pages, 9930 KB

Open AccessArticle

Research on the Influence of Key Parameters of High-Speed Hairpin Permanent-Magnet Motors for Electric Vehicles on Electromagnetic Performance

by Li Zhai, Liyu Yang, Ange Liu and Jianghaoyu Yan

Machines 2026, 14(4), 407; https://doi.org/10.3390/machines14040407 - 8 Apr 2026

Abstract

High-speed operation is a key pathway to higher power density in modern EV traction systems, and multi-parameter optimization is essential for enhancing its high-speed performance. This study investigates a 20,000 r/min interior double-V permanent-magnet flat-wire motor via finite-element simulations to systematically examine the [...] Read more.

High-speed operation is a key pathway to higher power density in modern EV traction systems, and multi-parameter optimization is essential for enhancing its high-speed performance. This study investigates a 20,000 r/min interior double-V permanent-magnet flat-wire motor via finite-element simulations to systematically examine the effects of multiple interacting parameters—including flat-wire layer number, stator slot geometry, magnet grade, and rotor magnetic barrier angle—on the electromagnetic performance under high-speed operating conditions. The results indicate that increasing winding layers significantly reduces high-speed torque; an eight-layer design decreases torque by about 50% compared to a four-layer one, while a six-layer arrangement offers a favorable torque-loss trade-off. Wider slots lower the average torque but reduce torque ripple by approximately 27%, whereas deeper slots increase tooth flux density and reduce efficiency. Higher-grade magnets enhance air-gap flux and torque at elevated cost. Rotor magnet angle optimization reveals a trade-off between peak torque and ripple, with a symmetric 100°/100° design achieving balanced performance. These findings clarify structural–control interactions and support the multi-objective design of high-speed flat-wire permanent-magnet motors. Full article

(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)

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17 pages, 1190 KB

Open AccessFeature PaperArticle

Energy-Based Trajectory Tracking Control of a Six-DOF Robotic Manipulator Using the Port-Hamiltonian Framework

by Zhiheng Lin, Junqi Wang, Xindan Hu, Tong Wang and Weijun Zhou

Machines 2026, 14(4), 406; https://doi.org/10.3390/machines14040406 - 7 Apr 2026

Abstract

Structure-preserving trajectory tracking control for a six-degree-of-freedom robotic manipulator is developed within the port-Hamiltonian framework. Error Hamiltonian is constructed by incorporating configuration and momentum tracking errors into the system energy. Based on this formulation, a momentum-based tracking controller with feedforward compensation and damping [...] Read more.

Structure-preserving trajectory tracking control for a six-degree-of-freedom robotic manipulator is developed within the port-Hamiltonian framework. Error Hamiltonian is constructed by incorporating configuration and momentum tracking errors into the system energy. Based on this formulation, a momentum-based tracking controller with feedforward compensation and damping injection is derived without coordinate transformations or matching conditions. A disturbance estimator is further introduced to compensate unknown external torques. Energy-based analysis proves nominal closed-loop stability and uniform ultimate boundedness in the presence of estimation errors. Simulation results on a full rigid-body manipulator demonstrate accurate trajectory tracking under coupled and high-speed joint motions. Full article

(This article belongs to the Special Issue Interactive Manipulation of Mobile Manipulators)

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24 pages, 57857 KB

Open AccessArticle

Aerodynamic Matching Optimization of the Second-Stage Stator of Centrifugal Compressor

by Qinglong Liu, Hang Lv, Lingang Shen, Xiaofang Wang and Haitao Liu

Machines 2026, 14(4), 405; https://doi.org/10.3390/machines14040405 - 7 Apr 2026

Abstract

This paper presents a parametric modeling and aerodynamic matching optimization methodology for the second-stage stator of a multi-stage centrifugal compressor. Firstly, based on the geometric configuration of the two-stage components, a flexible parametric template is established for the second-stage stator. Secondly, numerical simulations [...] Read more.

This paper presents a parametric modeling and aerodynamic matching optimization methodology for the second-stage stator of a multi-stage centrifugal compressor. Firstly, based on the geometric configuration of the two-stage components, a flexible parametric template is established for the second-stage stator. Secondly, numerical simulations are conducted to analyze the internal flow field and evaluate the performance of the initial design of this compressor, revealing performance deficits such as significant vortex-induced losses and a large outlet circumferential flow angle (−12.138°). Thirdly, an aerodynamic optimization framework integrating a Kriging surrogate model and a Genetic Algorithm (GA) is applied to the second-stage stator, targeting at the aerodynamic matching optimization under multiple operating conditions. The optimization objectives include maximizing the overall polytropic efficiency of compressor and the static pressure ratio of second-stage stator, as well as minimizing the total pressure loss coefficient and the outlet circumferential flow angle of second-stage stator. The results demonstrate that the optimized design achieves a 2.17% improvement in the overall polytropic efficiency and a 12.01% improvement in the static pressure recovery coefficient at the design condition, along with a notable reduction in the outlet circumferential flow angle to 0.663°. Under multi-condition operation, the optimized stator exhibits enhanced performance stability. The overall polytropic efficiency is improved by 2.06% and the static pressure recovery coefficient is improved by 23.31% at the low-flow condition, confirming the effectiveness of the employed parametric modeling and sequential optimization approach. Full article

(This article belongs to the Section Turbomachinery)

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24 pages, 67497 KB

Open AccessArticle

A Physics-Guided Dual-Stream Vibration Feature Fusion Network for Chatter-Induced Surface Mark Diagnosis in Wafer Thinning

by Heng Li, Hua Liu, Liang Zhu, Xiangyu Zhao, Lemiao Qiu and Shuyou Zhang

Machines 2026, 14(4), 404; https://doi.org/10.3390/machines14040404 - 7 Apr 2026

Abstract

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided [...] Read more.

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

(This article belongs to the Special Issue Monitoring and Control of Machining Processes)

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26 pages, 38704 KB

Open AccessArticle

Adaptive Allocation of Steering Control Weights for Intelligent Vehicles Based on a Human–Machine Non-Cooperative Game

by Haobin Jiang, Dechen Kong, Yixiao Chen and Bin Tang

Machines 2026, 14(4), 403; https://doi.org/10.3390/machines14040403 - 7 Apr 2026

Abstract

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg [...] Read more.

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

(This article belongs to the Special Issue New Journeys in Vehicle System Dynamics and Control)

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19 pages, 745 KB

Open AccessReview

Reclined Seating Postures on Passive Safety Performance in Automotive Seats: A Review

by Nuno Carmo, João Milho and Marta Carvalho

Machines 2026, 14(4), 402; https://doi.org/10.3390/machines14040402 - 7 Apr 2026

Abstract

The increasing adoption of reclined seating postures in modern vehicle interiors challenges the assumptions underpinning current passive safety systems and occupant protection assessment frameworks. While restraint technologies and certification protocols have historically been developed for upright configurations, emerging trends in autonomous driving and [...] Read more.

The increasing adoption of reclined seating postures in modern vehicle interiors challenges the assumptions underpinning current passive safety systems and occupant protection assessment frameworks. While restraint technologies and certification protocols have historically been developed for upright configurations, emerging trends in autonomous driving and comfort-oriented designs promote relaxed postures that fundamentally alter occupant kinematics, loading path, and consequently the injury mechanisms. This review critically synthesizes experimental and numerical studies addressing occupant biomechanics, restraint system performance, and injury risk in reclined seating. Evidence from crash tests using Anthropomorphic Test Devices and Post-Mortem Human Surrogates, alongside high-fidelity numerical Human Body Models, is analyzed to identify consistent trends and methodological limitations. The results highlight increased forward excursion, elevated submarining propensity, and posture-dependent abdominal and lumbar loading as critical consequences of increased seatback recline. Furthermore, this review discusses the effectiveness of adaptive restraint strategies, including active repositioning and modified airbag–belt integration. By identifying existing research gaps and regulatory limitations, this work aims to provide a roadmap for the development of future safety systems that ensure robust protection for all occupants in the era of automated mobility. Full article

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18 pages, 9525 KB

Open AccessArticle

Electrified Airpath and Fueling Synergies for Cleaner Transients in an OP2S Diesel Engine: An Experimental Study

by Ankur Bhatt, Aditya Datar, Brian Gainey and Benjamin Lawler

Machines 2026, 14(4), 401; https://doi.org/10.3390/machines14040401 - 7 Apr 2026

Abstract

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel [...] Read more.

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

(This article belongs to the Section Turbomachinery)

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19 pages, 2237 KB

Open AccessArticle

Electric Contact Resistance of 3D-Printed Al5086 Aluminum

by Martin Ralchev, Valentin Mateev and Iliana Marinova

Machines 2026, 14(4), 400; https://doi.org/10.3390/machines14040400 - 6 Apr 2026

Abstract

Additive manufacturing by Selective Laser Melting (SLM) or, precisely, Laser Powder Bed Fusion (L-PBF), offers new opportunities for producing electrically functional metal components with tailored geometric designs and material properties. In this study, the electrical contact resistance and related properties of 3D-printed samples [...] Read more.

Additive manufacturing by Selective Laser Melting (SLM) or, precisely, Laser Powder Bed Fusion (L-PBF), offers new opportunities for producing electrically functional metal components with tailored geometric designs and material properties. In this study, the electrical contact resistance and related properties of 3D-printed samples made from Al5086 aluminum alloy are tested. The benefits of Al5086 include flexibility without cracking, welding ability and exceptional resistance to corrosion in saltwater and industrial environments. This makes it an excellent candidate for power electric applications due to its good electrical conductivity and corrosion resistance. In this study, an analysis is performed to assess the impact of internal volumetric properties and surface parameters on general contact resistance performance. This analysis combines advanced testing procedures and parameter identification of the electric contact resistance model. This study investigates how these parameters affect contact resistance, which is a critical factor in the reliability of electrical devices. Electrical contact resistance was measured using a dedicated test setup that applied consistent pressure and maintained directional alignment. The results show that the printing direction of the samples slightly affects resistance values due to the continuity of current paths along the build direction, likely due to homogenous inter-layer boundaries and mechanical stress distribution. These findings suggest that both print orientation and internal structure must be considered when designing 3D-printed contact elements for electrical applications. Overall, this study demonstrates the feasibility of using L-PBF-fabricated aluminum components in electric applications where both electrical and structural performances are essential. Full article

(This article belongs to the Special Issue Advances in 3D Printing Technologies: Materials, Processes, and Applications, 2nd Edition)

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24 pages, 13317 KB

Open AccessArticle

Hybrid-Mechanism Deep Learning Modeling for Machine Tool Thermal Error: Robust Prediction via Few-Sample Learning

by Hongru Chen, Yubin Huang, Chaochao Qiu, Xueyan Ning, Pingjiang Wang and Ke Yang

Machines 2026, 14(4), 399; https://doi.org/10.3390/machines14040399 - 6 Apr 2026

Abstract

To address spindle thermal error in precision machining, this study proposes a hybrid modeling method. It combines a physical model for linear deformation with a GAT-LSTM network. Experiments show the hybrid model achieved RMSE/MAE of 4.6/4.0 µm under full training (12 conditions), 5.5/4.9 [...] Read more.

To address spindle thermal error in precision machining, this study proposes a hybrid modeling method. It combines a physical model for linear deformation with a GAT-LSTM network. Experiments show the hybrid model achieved RMSE/MAE of 4.6/4.0 µm under full training (12 conditions), 5.5/4.9 µm under 3 training condition and 4.8/4.3 µm under 1 training condition, substantially reducing the data requirements for thermal error modeling. The compensation experiment conducted using a high real-time surrogate-model-based architecture reduced thermal error by 78% (from 54 µm to 12 µm), demonstrating high precision and minimal data requirements suitable for real-time applications. Full article

(This article belongs to the Special Issue Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance)

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20 pages, 3653 KB

Open AccessArticle

Constrained Multibody Dynamic Modeling and Power Benchmarking of a Three-Omni-Wheel Mobile Robot

by Iosif-Adrian Maroșan, Sever-Gabriel Racz, Radu-Eugen Breaz, Alexandru Bârsan, Claudia-Emilia Gîrjob, Mihai Crenganiș, Cristina-Maria Biriș and Anca-Lucia Chicea

Machines 2026, 14(4), 398; https://doi.org/10.3390/machines14040398 - 5 Apr 2026

Abstract

Omnidirectional mobile robots are increasingly used in industrial and service applications due to their high maneuverability and ability to perform combined translational and rotational motions in confined spaces. However, these locomotion advantages are often accompanied by additional wheel–ground interaction losses, making power consumption [...] Read more.

Omnidirectional mobile robots are increasingly used in industrial and service applications due to their high maneuverability and ability to perform combined translational and rotational motions in confined spaces. However, these locomotion advantages are often accompanied by additional wheel–ground interaction losses, making power consumption an important design criterion in the design of efficient mobile platforms. This study presents a dynamic modeling and experimental-power benchmarking framework for a modular mobile robot equipped with three omnidirectional wheels, using a four-omni-wheel configuration as a baseline reference for comparison. A CAD-consistent multibody dynamic model of the three-wheel architecture is developed in the MATLAB/Simulink–Simscape Multibody R2024benvironment to estimate motor currents and electrical-power demand during motion. Experimental validation is carried out on the physical prototype using Hall-effect current sensors integrated into the drive modules, enabling real-time current acquisition for each motor. Both the simulation and experiments are performed on a standardized 1 m square-path benchmark at a constant 12 V supply. The results show that the proposed three-omni-wheel configuration reaches a total measured power of 14.43 W and a simulated power of 12.72 W, corresponding to a robot-level deviation of 11.85%. By comparison, the four-omni-wheel baseline exhibits a total measured power of 25.75 W and a simulated power of 24.92 W. Therefore, the proposed three-wheel architecture reduces the measured power demand by approximately 43.96% relative to the baseline, while the four-wheel configuration provides higher model fidelity. The proposed methodology supports power-oriented evaluation and informed design selection of omnidirectional locomotion architectures for modular mobile robots intended for industrial applications. Full article

(This article belongs to the Special Issue New Trends in Industrial Robots)

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24 pages, 4411 KB

Open AccessArticle

GT-TD3: A Kinematics-Aware Graph-Transformer Framework for Stable Trajectory Tracking of High-Degree-of-Freedom (DOF) Manipulators

by Hanwen Miao, Haoran Hou, Zhaopeng Zhu, Zheng Chao and Rui Zhang

Machines 2026, 14(4), 397; https://doi.org/10.3390/machines14040397 - 5 Apr 2026

Abstract

Accurate trajectory tracking of redundant manipulators is difficult because the controller must simultaneously model local couplings between adjacent joints and global dependencies across the whole kinematic chain. Existing reinforcement learning methods typically employ multilayer perceptrons, which do not explicitly exploit manipulator structure and [...] Read more.

Accurate trajectory tracking of redundant manipulators is difficult because the controller must simultaneously model local couplings between adjacent joints and global dependencies across the whole kinematic chain. Existing reinforcement learning methods typically employ multilayer perceptrons, which do not explicitly exploit manipulator structure and therefore show limited stability and representation ability in high-dimensional continuous control tasks. This paper proposes GT-TD3, a Graph Transformer-enhanced-Twin Delayed Deep Deterministic Policy Gradient framework, for redundant manipulator trajectory tracking. The proposed actor first converts the raw system state into joint-level node features and uses a graph neural network to extract local kinematic coupling information. A Transformer is then employed to capture long-range dependencies among joints. To strengthen the use of structural priors, topology- and distance-related bias terms are incorporated into the attention mechanism, enabling the network to encode manipulator structure during global feature learning. Experiments on a 7-DoF KUKA iiwa manipulator in PyBullet demonstrate that GT-TD3 outperforms MLP, pure GNN, and pure Transformer baselines in tracking performance. The proposed method achieves more stable training, faster convergence, and smoother and more accurate end-effector motion. The results show that the integration of local graph modeling and structure-aware global attention provides an effective solution for high-precision trajectory tracking of redundant manipulators. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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46 pages, 3809 KB

Open AccessReview

Overview on Predictive Maintenance Techniques for Turbomachinery

by Pierpaolo Dini, Damiano Nardi and Sergio Saponara

Machines 2026, 14(4), 396; https://doi.org/10.3390/machines14040396 - 5 Apr 2026

Abstract

Within the Industry 5.0 paradigm, the management of critical assets requires advanced digital architectures capable of ensuring resilience and operational sustainability. The present systematic review analyzes the state of the art in predictive maintenance (PdM) technologies for turbines and turbomachinery, providing a technical [...] Read more.

Within the Industry 5.0 paradigm, the management of critical assets requires advanced digital architectures capable of ensuring resilience and operational sustainability. The present systematic review analyzes the state of the art in predictive maintenance (PdM) technologies for turbines and turbomachinery, providing a technical examination of anomaly and fault detection frameworks, extended to remaining useful life (RUL) estimation and root cause analysis (RCA). The work addresses inherent sectoral challenges, ranging from the processing of high-dimensional multivariate time series (MTS) from Supervisory Control and Data Acquisition (SCADA) systems to labeled data scarcity and signal non-stationarity in real-world environments. Both purely data-driven frameworks and hybrid physics-informed models, such as Physics-Informed Neural Networks (PINNs), are critically evaluated against performance indicators. A significant contribution of this study lies in the classification of methodologies based on their readiness for real-time inference, emphasizing the role of Explainable AI (XAI) in providing transparent insights to domain experts, who remain central to decision-making processes. The primary objective of this review is to offer an analytical overview of progress to date against current technological gaps, tracing a clear trajectory for future developments. In this regard, the adoption of Generative AI and Large Language Models (LLMs) is identified as a fundamental step toward evolving into interactive, human-centric decision support systems. Full article

(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)

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30 pages, 6490 KB

Open AccessArticle

A Closed-Form Inverse Kinematic Analytical Method for a Humanoid Seven-DOF Redundant Manipulator

by Guojun Zhao, Ben Ye, Yunlong Tian, Juntong Yun, Du Jiang and Bo Tao

Machines 2026, 14(4), 395; https://doi.org/10.3390/machines14040395 - 4 Apr 2026

Abstract

Humanoid manipulators with kinematic redundancy offer enhanced dexterity and adaptability to complex environments. Solving their inverse kinematics (IK) is fundamental to trajectory tracking, motion planning, and real-time control. Conventional Jacobian-based iterative methods are widely used, but they are often sensitive to the initial [...] Read more.

Humanoid manipulators with kinematic redundancy offer enhanced dexterity and adaptability to complex environments. Solving their inverse kinematics (IK) is fundamental to trajectory tracking, motion planning, and real-time control. Conventional Jacobian-based iterative methods are widely used, but they are often sensitive to the initial guess, computationally expensive, and less effective in handling strict constraints. Arm-angle-based analytical parameterization reduces redundancy resolution to a single parameter. However, joint limits may lead to multiple disconnected feasible arm-angle intervals. Many existing methods still depend on a numerical search or intelligent optimization to select the arm angle. This lowers computational efficiency and provides less explicit control over branch and configuration selection. To address these issues, this paper extends the arm-angle analytical IK framework. It introduces global configuration parameters to explicitly control the shoulder-elbow-wrist configuration. It also completes the analytical derivation of the rotational relationships of the first three joints in the reference plane. In addition, a feasibility determination and modeling scheme for the arm-angle domain is established, which covers disconnected feasible intervals. The IK problem is then reformulated as a one-dimensional optimization over the feasible domain. An efficient interval-based search is employed to determine the optimal arm angle. Experimental results demonstrate high accuracy and interference-free trajectory tracking. Comparative tests on randomly sampled target poses are also performed. The results show more concentrated error distributions, shorter average computation time, and higher success rates. These results confirm the advantages of the proposed method in accuracy, robustness, and real-time performance. Full article

(This article belongs to the Special Issue Advances in Robot Kinematics and Dynamics: Innovations, Control Strategies, and Practical Applications)

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