Machines

20 pages, 15155 KiB

Open AccessArticle

Nonlinear Vibration of Oblique-Stiffened Multilayer Functionally Graded Cylindrical Shells Under External Excitation with Internal and Superharmonic Resonances

by Kamran Foroutan and Farshid Torabi

Machines 2025, 13(3), 225; https://doi.org/10.3390/machines13030225 - 11 Mar 2025

Abstract

This research examines a semi-analytical approach for analyzing the nonlinear vibration (NV) characteristics of oblique-stiffened multilayer functionally graded (OSMFG) cylindrical shells (CSs) under external excitation. The material’s properties are continuously graded along the thickness direction. The CSs are made up of three layers: [...] Read more.

This research examines a semi-analytical approach for analyzing the nonlinear vibration (NV) characteristics of oblique-stiffened multilayer functionally graded (OSMFG) cylindrical shells (CSs) under external excitation. The material’s properties are continuously graded along the thickness direction. The CSs are made up of three layers: an inner metal-rich layer, an exterior ceramic-rich layer, and a functionally graded (FG) layer in between. The stiffeners’ constitutive material is graded constantly throughout their thicknesses. von Kármán equations, the smeared stiffener technique, and the Galerkin approach are used to address the NV problem. The vibration behavior is investigated via the method of multiple scales (MMSs). The analysis considers an internal resonance of 1:1/3:1/9 as well as a superharmonic resonance of order 3/1. The impacts of various material and geometric characteristics on the NV of OSMF-CSs are thoroughly investigated. Full article

(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)

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22 pages, 11609 KiB

Open AccessArticle

Enhancing Bottleneck Analysis in Ship Manufacturing with Knowledge Graphs and Large Language Models

by Yanjun Ma, Tao Wu, Bin Zhou, Xiaoyang Liang, Jiwang Du and Jinsong Bao

Machines 2025, 13(3), 224; https://doi.org/10.3390/machines13030224 - 10 Mar 2025

Abstract

Ship manufacturing is a critical backbone industry in China, where the nation leads on a global scale in terms of vessel completions and order volumes. However, the high volume of orders often imposes substantial processing loads, increases the risk of equipment failures, and [...] Read more.

Ship manufacturing is a critical backbone industry in China, where the nation leads on a global scale in terms of vessel completions and order volumes. However, the high volume of orders often imposes substantial processing loads, increases the risk of equipment failures, and exacerbates production bottlenecks. Despite the accumulation of significant amounts of data in this field, analyzing bottlenecks remains a persistent challenge, primarily due to the presence of heterogeneous, multi-source data and the lack of effective data integration mechanisms. The traditional approaches are largely limited to bottleneck detection, offering minimal capabilities in terms of deep analysis, traceability, and interpretability, which are crucial for comprehensive bottleneck resolution. Meanwhile, extensive knowledge remains underutilized, leading to analytical results that are overly reliant on expert experience and lacking in interpretability. To address these challenges, this research proposes a graph-retrieval-based bottleneck mining method for ship manufacturing, employing large language models and a knowledge graph. The approach integrates a data-driven “turning point” mechanism for dynamic bottleneck detection and the manufacturing process knowledge graph, consisting of process subgraphs and 5M1E (Man, Machine, Material, Method, Measurement, Environment) specification subgraphs. Furthermore, a question-answering chain is introduced to enhance the interaction between the LLMs and the knowledge graph, improving the retrieval and reasoning capabilities. Using practical production data from a Shanghai ship thin plate production line, our method demonstrates a superior performance compared to that of four existing models, validating its effectiveness in throughput bottleneck analysis. This approach provides a scalable and efficient solution for analyzing complex bottleneck issues in industrial production, contributing to enhanced manufacturing efficiency and digital transformation. Full article

(This article belongs to the Section Advanced Manufacturing)

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22 pages, 7165 KiB

Open AccessArticle

Instantaneous Frequency Analysis Based on High-Order Multisynchrosqueezing Transform on Motor Current and Application to RV Gearbox Fault Diagnosis

by Shiyi Chai and Kai Xu

Machines 2025, 13(3), 223; https://doi.org/10.3390/machines13030223 - 8 Mar 2025

Abstract

Motor current analysis is useful for ensuring the safety and reliability of electromechanical systems. However, for gearboxes, the commonly used methods of detecting faulty frequency sidebands are easily disturbed by installation errors, inherent harmonics, and fundamental frequency with high amplitude. Aiming at this [...] Read more.

Motor current analysis is useful for ensuring the safety and reliability of electromechanical systems. However, for gearboxes, the commonly used methods of detecting faulty frequency sidebands are easily disturbed by installation errors, inherent harmonics, and fundamental frequency with high amplitude. Aiming at this problem, this study presents instantaneous frequency polarview (IFpolarview), which diagnoses faults based on motor angle and motor current frequency modulation (FM) features. Firstly, to address the problem of the limited analysis order of higher-order synchrosqueezing transform (HSST), the higher-order multisynchrosqueezing transform (HMSST) is introduced to improve the instantaneous frequency (IF) estimation accuracy and reveal the transient fault features from the motor current without further increasing the order and algorithm difficulty. Then, based on the motor angle and accurate motor current IF extracted from HMSST, the IFpolarview is proposed to visualize gear faults through detecting the FM of motor current synchronized with the faulty gear mesh. In the simulation, the IF estimation error of HMSST is 2.51%, which is smaller than other methods. The experimental results show that the HMSST has the smallest Rényi entropy value of 9.13, implying that the most aggregated time–frequency representation (TFR) of the energy is obtained. HMSST can enhance the resolution of fault characteristics, and IFpolarview concentrates the abnormal IF fluctuations with periodicity into a small angular interval, which highlights the fault features and demonstrates greater intuitiveness and reliability in comparison to the frequency sideband detection method. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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20 pages, 12256 KiB

Open AccessArticle

Enhanced Seat Suspension Performance Through Positive Real Network Optimization and Skyhook Inertial Control

by Xiaofeng Yang, Rui Sun, Yi Yang, Yanling Liu, Jingchen Hong and Changning Liu

Machines 2025, 13(3), 222; https://doi.org/10.3390/machines13030222 - 8 Mar 2025

Abstract

To solve the low frequency vibration problem faced by heavy truck drivers, a positive real network inertial suspension structure combined with a skyhook inertial control strategy is adopted. This integrated approach effectively reduces low-frequency vibrations at the seat and human body levels. Specifically, [...] Read more.

To solve the low frequency vibration problem faced by heavy truck drivers, a positive real network inertial suspension structure combined with a skyhook inertial control strategy is adopted. This integrated approach effectively reduces low-frequency vibrations at the seat and human body levels. Specifically, this research aims to mitigate the acceleration experienced on the seat surface within the low-frequency range. Firstly, a human–seat dynamics model is established. Subsequently, based on the principles of network synthesis, the derivation of transfer functions for both first- and second-order systems is discussed, and the network parameters are also optimized. This paper further compares the optimization outcomes of first- and second-order skyhook seat inertial suspensions. An adaptive fuzzy sliding-mode controller (AFSMC) has been developed for an electromechanical inerter, ensuring it closely tracks optimal control performance. The findings demonstrate that the new suspension system achieves a 29.9% reduction in the root-mean-square value of seat surface acceleration and a 43.1% decrease in the road-bump peak acceleration compared to a conventional suspension system. The results show that the inertial suspension with skyhook inertial control is highly effective in completely suppressing seat surface acceleration within the low-frequency domain. Full article

(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)

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17 pages, 3791 KiB

Open AccessArticle

Multi-Objective Optimization of Milling Ti-6Al-4V Alloy for Improved Surface Integrity and Sustainability Performance

by Djordje Cica, Sasa Tesic, Milisav Markovic, Branislav Sredanovic, Stevo Borojevic, Milan Zeljkovic, Davorin Kramar and Franci Pušavec

Machines 2025, 13(3), 221; https://doi.org/10.3390/machines13030221 - 8 Mar 2025

Abstract

Ti-6Al-4V is a titanium-based alloy that is widely used in a diverse range of applications, especially in industries such as biomedical and aerospace. Several lubricooling techniques have been introduced to enhance the machinability of these materials. Among them, environmentally friendly strategies are gaining [...] Read more.

Ti-6Al-4V is a titanium-based alloy that is widely used in a diverse range of applications, especially in industries such as biomedical and aerospace. Several lubricooling techniques have been introduced to enhance the machinability of these materials. Among them, environmentally friendly strategies are gaining in importance, with sustainability trends rising in manufacturing. The present research investigates the effect of two eco-friendly lubricooling techniques (minimum quantity lubrication and cryogenic cooling), along with other cutting parameters (cutting speed and feed per tooth), on the surface roughness and microhardness of the machined surfaces, which are identified as one of the most frequently implemented indicators of surface integrity in the ball-end milling of the Ti-6Al-4V alloy. In addition, the total electrical energy consumption of the machine tools under different cooling/lubrication conditions was also analyzed. The results obtained showed that cryogenic cooling enhanced milling performance as compared to MQL. Moreover, a multi-objective parameter optimization model integrating the machining responses (surface roughness, microhardness, energy consumption, and productivity) and sustainability metrics (environmental impact, operator’s health and safety, and waste management) was introduced. It was found that cryogenic cooling outperformed the MQL method in terms of both machining performance and environmental impact. An analysis of variance (ANOVA) was carried out to evaluate the significance of each process parameter on the multiple performance index. The results indicate that feed per tooth, cooling method, and cutting speed were significant, with respective contributions of 39.4%, 36.8%, and 22.9%. Finally, the optimal parameter setting was verified through a confirmation test and the results reveal that an improvement was observed in the machining responses and multiple performance index. Full article

(This article belongs to the Section Advanced Manufacturing)

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24 pages, 21713 KiB

Open AccessArticle

Numerical and Experimental Investigation of the Ultra-Low Head Bidirectional Shaft Extension Pump Under Near-Zero Head Conditions

by Fulin Zhang, Yuan Zheng, Gaohui Li and Jing Dai

Machines 2025, 13(3), 220; https://doi.org/10.3390/machines13030220 - 7 Mar 2025

Abstract

Theoretical analysis, numerical simulation, and experimental study are used to investigate the ultra-low head bidirectional shaft extension pump, especially near-zero head conditions. The results show that under forward operation, at low flow and design flow conditions, the closer to the shroud, the closer [...] Read more.

Theoretical analysis, numerical simulation, and experimental study are used to investigate the ultra-low head bidirectional shaft extension pump, especially near-zero head conditions. The results show that under forward operation, at low flow and design flow conditions, the closer to the shroud, the closer the vortex is to the back of the guide vanes, and the vortex area is becoming smaller. The hydraulic loss of the outlet passage is 15% of the operating head at the minimum flow and 170% of the operating head under near-zero head condition. The peak-to-peak (PTP) value of pressure fluctuation increases with the increase in flow rate. The primary frequency (PF) of vibration is strongly related to the primary and secondary frequencies (PSFs) of pressure fluctuation. Under reverse operation, when the flow rate is less than 0.83Q_r0, the uniformity of axial velocity distribution V_u and the velocity-weighted average angle θ show an approximately exponential declining pattern. The hydraulic loss of the outlet passage at the minimum flow rate is 61% of the operating head and 350% of the operating head under near-zero head condition. The exponential fitting can better describe the relationship between circulation and hydraulic loss. As the flow rate decreases, the PF of vibration decreases to rotational frequency. Full article

(This article belongs to the Section Turbomachinery)

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19 pages, 13823 KiB

Open AccessArticle

Autonomous Agricultural Robot Using YOLOv8 and ByteTrack for Weed Detection and Destruction

by Ardin Bajraktari and Hayrettin Toylan

Machines 2025, 13(3), 219; https://doi.org/10.3390/machines13030219 - 7 Mar 2025

Abstract

Automating agricultural machinery presents a significant opportunity to lower costs and enhance efficiency in both current and future field operations. The detection and destruction of weeds in agricultural areas via robots can be given as an example of this process. Deep learning algorithms [...] Read more.

Automating agricultural machinery presents a significant opportunity to lower costs and enhance efficiency in both current and future field operations. The detection and destruction of weeds in agricultural areas via robots can be given as an example of this process. Deep learning algorithms can accurately detect weeds in agricultural fields. Additionally, robotic systems can effectively eliminate these weeds. However, the high computational demands of deep learning-based weed detection algorithms pose challenges for their use in real-time applications. This study proposes a vision-based autonomous agricultural robot that leverages the YOLOv8 model in combination with ByteTrack to achieve effective real-time weed detection. A dataset of 4126 images was used to create YOLO models, with 80% of the images designated for training, 10% for validation, and 10% for testing. Six different YOLO object detectors were trained and tested for weed detection. Among these models, YOLOv8 stands out, achieving a precision of 93.8%, a recall of 86.5%, and a [email protected] detection accuracy of 92.1%. With an object detection speed of 18 FPS and the advantages of the ByteTrack integrated object tracking algorithm, YOLOv8 was selected as the most suitable model. Additionally, the YOLOv8-ByteTrack model, developed for weed detection, was deployed on an agricultural robot with autonomous driving capabilities integrated with ROS. This system facilitates real-time weed detection and destruction, enhancing the efficiency of weed management in agricultural practices. Full article

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

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14 pages, 8606 KiB

Open AccessArticle

Predicting the Fatigue Life of a Commercial Vehicle X-EPS Steering Gear with a Rigid–Flexible Coupling Dynamics Method

by Feng Chen, Zhiquan Fu, Baoxiang Qiu, Chenfeng Si, Qizhang Zhu, Chenli Feng, Xiaoqing Sun, Huafang Liang and Lai Yang

Machines 2025, 13(3), 218; https://doi.org/10.3390/machines13030218 - 7 Mar 2025

Abstract

Commercial X-EPS steering gears are characterized by high torque output, torque—with increasing capabilities, high reliability, and excellent handling precision. Among them, the screw–nut pair in the steering gear is subjected to complex working loads, and its raceways are prone to fatigue failure. To [...] Read more.

Commercial X-EPS steering gears are characterized by high torque output, torque—with increasing capabilities, high reliability, and excellent handling precision. Among them, the screw–nut pair in the steering gear is subjected to complex working loads, and its raceways are prone to fatigue failure. To more accurately and effectively predict the fatigue life of the screw–nut pair in the steering gear, a method for dynamic simulation and fatigue life prediction of commercial X-EPS steering gears is proposed based on virtual prototyping technology and finite element theory. That is, a rigid–flexible coupling dynamic model of the X-EPS steering gear is established to obtain the load spectra of the screw and nut, and a finite-element static model is also established. Then, combined with the material S-N curve, the fatigue life is predicted through the NOCDE fatigue “five-block diagram”. The research results show that the screw and nut raceways are the key components prone to fatigue failure in the steering gear. The minimum numbers of fatigue life cycles are 1.028 × 10⁵ times and 2.9695 × 10⁵ times, respectively. Subsequently, a fatigue life bench test was conducted for verification. The results show that the error between the fatigue life analysis model of the XEPS recirculating ball steering gear and the test is less than 5%, meeting the requirements of the fatigue life test standard and design standard. Full article

(This article belongs to the Section Vehicle Engineering)

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19 pages, 6420 KiB

Open AccessArticle

Stationary Type-Approval Test of the Tractor Pneumatic Braking System for Towed Vehicle Control

by Zbigniew Kamiński and Jarosław Czaban

Machines 2025, 13(3), 217; https://doi.org/10.3390/machines13030217 - 7 Mar 2025

Abstract

Agricultural tractors are equipped with air braking systems to supply and control the braking systems of towed vehicles. This system’s functional and operational characteristics significantly impact the compatibility and speed of the braking system of the tractor–trailer combination and are therefore checked during [...] Read more.

Agricultural tractors are equipped with air braking systems to supply and control the braking systems of towed vehicles. This system’s functional and operational characteristics significantly impact the compatibility and speed of the braking system of the tractor–trailer combination and are therefore checked during approval tests. This paper presents a test methodology and a description of the instrumentation and apparatus used to test the air braking systems of tractors under stationary conditions, as required by EU Regulation 2015/68. Sample test results of the trailer air supply system are included, such as checking the system for leaks, checking the pressure at the coupling heads, checking the compressor flow rate and air reservoir capacity, and checking the response time of the tractor control line. Approval authorities and tractor manufacturers can use the work results for quality control or product qualification tests. Full article

(This article belongs to the Section Vehicle Engineering)

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16 pages, 4154 KiB

Open AccessArticle

A Novel Bearing Fault Diagnosis Method Based on Improved Convolutional Neural Network and Multi-Sensor Fusion

by Zhongyao Wang, Xiao Xu, Dongli Song, Zejun Zheng and Weidong Li

Machines 2025, 13(3), 216; https://doi.org/10.3390/machines13030216 - 7 Mar 2025

Abstract

Bearings are key components of modern mechanical equipment. To address the issue that the limited information contained in the single-source signal of the bearing leads to the limited accuracy of the single-source fault diagnosis method, a multi-sensor fusion fault diagnosis method is proposed [...] Read more.

Bearings are key components of modern mechanical equipment. To address the issue that the limited information contained in the single-source signal of the bearing leads to the limited accuracy of the single-source fault diagnosis method, a multi-sensor fusion fault diagnosis method is proposed to improve the reliability of bearing fault diagnosis. Firstly, the feature extraction process of the convolutional neural network (CNN) is improved based on the theory of variational Bayesian inference, which forms the variational Bayesian inference convolutional neural network (VBICNN). VBICNN is used to obtain preliminary diagnosis results of single-channel signals. Secondly, considering the redundancy of information contained in multi-channel signals, a voting strategy is used to fuse the preliminary diagnosis results of the single-channel model to obtain the final results. Finally, the proposed method is evaluated by an experimental dataset of the axlebox bearing of a high-speed train. The results show that the average diagnosis accuracy of the proposed method can reach more than 99% and has favorable stability. Full article

(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)

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23 pages, 1962 KiB

Open AccessArticle

Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps

by Marc Vila Forteza, Diego Galar, Uday Kumar and Kai Goebel

Machines 2025, 13(3), 215; https://doi.org/10.3390/machines13030215 - 7 Mar 2025

Abstract

This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from [...] Read more.

This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery was used to fit statistical models. Parametric and non-parametric transformations and restricted cubic splines were used to fit the covariates, thereby relaxing linearity assumptions and potentiating predictors with strong nonlinear effects on the outcome. Standard Principal Component Analysis (PCA) and sparse robust PCA methods were used for data reduction to simplify the fitted models and minimize overfitting. Models fitted with sparse robust PCA on non-parametrically transformed variables using an additive variance stabilizing (AVAS) method are suggested for further investigation. The complexity of the fitted models was reduced by 85% while at the same time providing for a more robust model as indicated by an improvement of the calibration slope from 0.830 to 0.936 with an essentially stable Akaike information criterion (AIC) (0.34% increase). Full article

(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)

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25 pages, 13905 KiB

Open AccessArticle

A Framework for Real-Time Autonomous Robotic Sorting and Segregation of Nuclear Waste: Modelling, Identification and Control of Dexter^TM Robot

by Mithun Poozhiyil, Omer F. Argin, Mini Rai, Amir G. Esfahani, Marc Hanheide, Ryan King, Phil Saunderson, Mike Moulin-Ramsden, Wen Yang, Laura Palacio García, Iain Mackay, Abhishek Mishra, Sho Okamoto and Kelvin Yeung

Machines 2025, 13(3), 214; https://doi.org/10.3390/machines13030214 - 6 Mar 2025

Abstract

Robots are essential for carrying out tasks, for example, in a nuclear industry, where direct human involvement is limited. However, present-day nuclear robots are not versatile due to limited autonomy and higher costs. This research presents a merely teleoperated Dexter^TM nuclear robot’s [...] Read more.

Robots are essential for carrying out tasks, for example, in a nuclear industry, where direct human involvement is limited. However, present-day nuclear robots are not versatile due to limited autonomy and higher costs. This research presents a merely teleoperated Dexter^TM nuclear robot’s transformation into an autonomous manipulator for nuclear sort and segregation tasks. The Dexter^TM system comprises a arm client manipulator designed to operate in extreme radiation environments and a similar single/dual-arm local manipulator. In this paper, initially, a kinematic model and convex optimization-based dynamic model identification of a single-arm Dexter^TM manipulator is presented. This model is used for autonomous Dexter^TM control through Robot Operating System (ROS). A new integration framework incorporating vision, AI-based grasp generation and an intelligent radiological surveying method for enhancing the performance of autonomous Dexter^TM is presented. The efficacy of the framework is demonstrated on a mock-up nuclear waste test-bed using similar waste materials found in the nuclear industry. The experiments performed show potency, generality and applicability of the proposed framework in overcoming the entry barriers for autonomous systems in regulated domains like the nuclear industry. Full article

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

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19 pages, 17382 KiB

Open AccessArticle

Speed–Pressure Compound Control of Thrust System Based on the Adaptive Sliding Mode Control Strategy

by Tong Xing, Hong Liu, Zhe Zheng, Lianhui Jia, Lijie Jiang, Guofang Gong, Huayong Yang and Dong Han

Machines 2025, 13(3), 213; https://doi.org/10.3390/machines13030213 - 6 Mar 2025

Abstract

The thrust system, an important subsystem of a tunnel boring machine (TBM), primarily provides thrust force and adjusts TBM’s attitude in real time. In the tunneling process, only controlling the thrust speed causes pressure oscillations, increases soil deformation, and leads to surface subsidence [...] Read more.

The thrust system, an important subsystem of a tunnel boring machine (TBM), primarily provides thrust force and adjusts TBM’s attitude in real time. In the tunneling process, only controlling the thrust speed causes pressure oscillations, increases soil deformation, and leads to surface subsidence or upheaval. Conversely, solely relying on pressure control causes fluctuations in speed, making it difficult to ensure that the deviation between the designed tunneling axis (DTA) and the actual tunneling axis (ATA) remains within the permissible range. Due to the increase in geological complexity and higher construction quality standards, primarily relying on single-mode speed or pressure control has become inadequate to meet operational demands. Therefore, to realize higher safety and precise trajectory tracking, it is necessary to ensure speed and pressure compound control for thrust systems. This paper proposes a novel adaptive sliding mode control (ASMC) strategy for thrust systems, which is composed of a proportional pressure relief valve (PPRV) and a proportional flow control valve (PFCV). Firstly, PPRV and PFCV are modeled as a second-order system and an ASMC is employed to control the pressure and speed. Next, to assess the performance of the ASMC controller, simulation experiments were conducted under various conditions, including speed regulation, sudden changed load, and disturbed load. The simulation results indicate that compared to the Proportion–Integral–Differential (PID) controller, the ASMC controller shows almost no overshoot in speed and pressure control during the initial stages, with the response time reduced by approximately 70%. During speed regulation process and sudden changed load process, the response time for both speed and pressure control is shortened by about 80%. In the disturbed load process, the ASMC controller maintains pressure stability. In conclusion, the ASMC controller significantly improves the response speed and stability of the thrust system, exhibiting better control performance under various operating conditions. Full article

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

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18 pages, 4613 KiB

Open AccessArticle

Virtual and Real Occlusion Processing Method of Monocular Visual Assembly Scene Based on ORB-SLAM3

by Hanzhong Xu, Chunping Chen, Qingqing Yin, Chao Ma and Feiyan Guo

Machines 2025, 13(3), 212; https://doi.org/10.3390/machines13030212 - 6 Mar 2025

Abstract

Addressing the challenge of acquiring depth information in aero-engine assembly scenes using monocular vision, which complicates mixed reality (MR) virtual and real occlusion processing, we propose an ORB-SLAM3-based monocular vision assembly scene virtual and real occlusion processing method. The method proposes optimizing ORB-SLAM3 [...] Read more.

Addressing the challenge of acquiring depth information in aero-engine assembly scenes using monocular vision, which complicates mixed reality (MR) virtual and real occlusion processing, we propose an ORB-SLAM3-based monocular vision assembly scene virtual and real occlusion processing method. The method proposes optimizing ORB-SLAM3 for matching and depth point reconstruction using the MNSTF algorithm. MNSTF can solve the problems of feature point extraction and matching in weakly textured and texture-less scenes by expressing the structure and texture information of the local images. It is then proposed to densify the sparse depth map using the double-three interpolation method, and the complete depth map of the real scene is created by combining the 3D model depth information in the process model. Finally, by comparing the depth values of each pixel point in the real and virtual scene depth maps, the virtual occlusion relationship of the assembly scene is correctly displayed. Experimental validation was performed with an aero-engine piping connector assembly scenario and by comparing it with Holynski’s and Kinect’s methods. The results showed that in terms of virtual and real occlusion accuracy, the average improvement was 2.2 and 3.4 pixel points, respectively. In terms of real-time performance, the real-time frame rate of this paper’s method can reach 42.4 FPS, an improvement of 77.4% and 87.6%, respectively. This shows that the method in this paper has good performance in terms of the accuracy and timeliness of virtual and real occlusion. This study further demonstrates that the proposed method can effectively address the challenges of virtual and real occlusion processing in monocular vision within the context of mixed reality-assisted assembly processes. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)

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18 pages, 4666 KiB

Open AccessArticle

A Novel Lateral Control System for Autonomous Vehicles: A Look-Down Strategy

by Farzad Nadiri and Ahmad B. Rad

Machines 2025, 13(3), 211; https://doi.org/10.3390/machines13030211 - 6 Mar 2025

Abstract

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, [...] Read more.

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping. Full article

(This article belongs to the Special Issue Trajectory Planning for Autonomous Vehicles: State of the Art)

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25 pages, 11963 KiB

Open AccessArticle

Early-Fault Feature Extraction for Rolling Bearings Based on Parameter-Optimized Variation Mode Decomposition

by Junjie Ni, Gangjin Huang, Jing Yang, Nan Wang and Junheng Fu

Machines 2025, 13(3), 210; https://doi.org/10.3390/machines13030210 - 5 Mar 2025

Abstract

Bearing-vibration signals, characterized by strong non-stationarity, typically consist of multiple components. The periodic pulses related to bearing faults are frequently obscured by surrounding noise, and early bearing-fault vibrations are feeble, which complicates the extraction of inherent fault characteristics. The aim of this research [...] Read more.

Bearing-vibration signals, characterized by strong non-stationarity, typically consist of multiple components. The periodic pulses related to bearing faults are frequently obscured by surrounding noise, and early bearing-fault vibrations are feeble, which complicates the extraction of inherent fault characteristics. The aim of this research is to develop an effective method for extracting early-fault characteristic frequencies in rolling bearings. VMD, short for variational mode decomposition, is an innovative technique rooted in the classical Wiener filter for analyzing signals that include multiple components. However, applying VMD to process real non-stationary signals still poses several challenges. A key challenge is that the internal parameters of VMD require manual setting prior to use. Aiming to mitigate this limitation, this paper introduces an enhanced variational mode decomposition approach utilizing the Chaotic Harris Hawk Optimization (CHHO) method. Average energy entropy is used as the optimization criterion in the CHHO–VMD algorithm to ascertain both the ideal mode count and its corresponding penalty factor. The original signal is further broken down into intrinsic mode functions (IMFs), with each IMF corresponding to a different frequency interval. In addition, IMF components are selected based on kurtosis and cross-correlation criteria to reconstruct fault signals. Finally, envelope demodulation is performed to reveal the fault characteristic frequencies. Experimental findings demonstrate that, as opposed to alternative techniques, this approach achieves superior performance in extracting early-fault frequencies in rolling bearings, offering a novel solution for early-fault feature extraction. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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19 pages, 5385 KiB

Open AccessArticle

Dynamics and Failure Analysis on Rigid–Flexible Coupling Structure to Bucket Wheel Stacker Reclaimer

by Xiaozhen Wang, Yixiao Qin and Lin Chen

Machines 2025, 13(3), 209; https://doi.org/10.3390/machines13030209 - 5 Mar 2025

Abstract

The adjustment of counterweights in bucket wheel stacker reclaimers is crucial for the equipment’s load-bearing capacity, vibration, and overall stability. To enhance operational reliability and safety while reducing failure rates and maintenance costs, this study employs finite element analysis (FEA) software and multibody [...] Read more.

The adjustment of counterweights in bucket wheel stacker reclaimers is crucial for the equipment’s load-bearing capacity, vibration, and overall stability. To enhance operational reliability and safety while reducing failure rates and maintenance costs, this study employs finite element analysis (FEA) software and multibody dynamics (MBD) software to develop a rigid–flexible coupling model of the bucket wheel stacker reclaimer. By simulating the excavation forces generated by different materials, the dynamic response of the equipment during operation was analyzed. The results indicate that during the initial startup phase, significant fluctuations in the system’s parameters occur due to vibrations, but these stabilize after 40 s of damping. Comparative analysis of four excavation forces and various counterweight values during the reclaiming process identifies the optimal counterweight as 170 t. The study further reveals that under rotary working conditions, as the excavation force increases, the failure counterweight value increases by 8.3%. This research provides a theoretical basis for optimizing the adjustment of counterweights in bucket wheel stacker reclaimers, guiding operational practices under actual working conditions, ensuring efficient operation across different scenarios, and extending the equipment’s service life. Full article

(This article belongs to the Section Machine Design and Theory)

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2 pages, 141 KiB

Open AccessEditorial

Selected Papers from MEDER 2024: Advances of Mechanism Design for Robotic Machines

by Marco Ceccarelli and Erwin Christian Lovasz

Machines 2025, 13(3), 208; https://doi.org/10.3390/machines13030208 - 5 Mar 2025

Abstract

This Special Issue aims to promote and circulate the recent developments and achievements of the international community in the field of Robot Design within Mechanism and Machine Science, ranging from theoretical contributions to experimental and practical applications [...] Full article

(This article belongs to the Special Issue Selected Papers from MEDER 2024: Advances of Mechanism Design for Robotic Machines)

38 pages, 8562 KiB

Open AccessReview

Research on Control Strategy Technology of Upper Limb Exoskeleton Robots: Review

by Libing Song, Chen Ju, Hengrui Cui, Yonggang Qu, Xin Xu and Changbing Chen

Machines 2025, 13(3), 207; https://doi.org/10.3390/machines13030207 - 3 Mar 2025

Abstract

Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, human performance enhancement, and occupational safety and health. The rapid advancement of high-precision, low-noise acquisition devices and intelligent motion intention recognition algorithms has [...] Read more.

Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, human performance enhancement, and occupational safety and health. The rapid advancement of high-precision, low-noise acquisition devices and intelligent motion intention recognition algorithms has led to a growing demand for more rational and reliable control strategies. Consequently, the control systems and strategies of exoskeleton robots are becoming increasingly prominent. This paper innovatively takes the hierarchical control system of exoskeleton robots as the entry point and comprehensively compares the current control strategies and intelligent technologies for upper limb exoskeleton robots, analyzing their applicable scenarios and limitations. The current research still faces challenges such as the insufficient real-time performance of algorithms and limited individualized adaptation capabilities. It is recognized that no single traditional control algorithm can fully meet the intelligent interaction requirements between exoskeletons and the human body. The integration of many advanced artificial intelligence algorithms into intelligent control systems remains restricted. Meanwhile, the quality of control is closely related to the perception and decision-making system. Therefore, the combination of multi-source information fusion and cooperative control methods is expected to enhance efficient human–robot interaction and personalized rehabilitation. Transfer learning and edge computing technologies are expected to enable lightweight deployment, ultimately improving the work efficiency and quality of life of end-users. Full article

(This article belongs to the Special Issue Advances and Challenges in Wearable Robotics)

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