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Volume 13
Issue 7
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Machines, Volume 13, Issue 7 (July 2025) – 98 articles

Cover Story (view full-size image): This study proposes a novel machine learning-based framework for real-time fault prediction and condition monitoring in Permanent Magnet Synchronous Motor (PMSM) drives used in elevator systems. Leveraging multimodal sensor data, Positive-Unlabeled (PU) learning, Reinforcement Learning (RL), and GAN-based data augmentation, the methodology enables non-intrusive, scalable, and accurate fault detection in operational elevator environments. The system is validated on a real residential elevator installation, enhancing safety, efficiency, and predictive maintenance capabilities. View this paper
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43 pages, 6462 KiB  
Article
An Integrated Mechanical Fault Diagnosis Framework Using Improved GOOSE-VMD, RobustICA, and CYCBD
by Jingzong Yang and Xuefeng Li
Machines 2025, 13(7), 631; https://doi.org/10.3390/machines13070631 - 21 Jul 2025
Viewed by 267
Abstract
Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak [...] Read more.
Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))
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14 pages, 3123 KiB  
Article
Effect of Surface Modification for Efficient Electroplating of 3D-Printed Components
by Dagmar Klichová, Hana Krupová, Jakub Měsíček, František Botko and Světlana Radchenko
Machines 2025, 13(7), 630; https://doi.org/10.3390/machines13070630 - 21 Jul 2025
Viewed by 207
Abstract
This article explores the issue of surface modification through tumbling and vaporisation of 3D-printed materials, and its impact on the electrolytic deposition of metal coatings on previously non-conductive materials. Plastic materials represent an affordable alternative, but their surface treatment, in the form of [...] Read more.
This article explores the issue of surface modification through tumbling and vaporisation of 3D-printed materials, and its impact on the electrolytic deposition of metal coatings on previously non-conductive materials. Plastic materials represent an affordable alternative, but their surface treatment, in the form of post-coating, achieves properties comparable to those of metal parts while saving expensive metal material. Samples prepared by selective laser sintering (SLS) with different surface treatments were used. Polyamide 12 (PA12) was chosen as the base material and copper (Cu) as the metallic coating. Graphite was sprayed on the samples to ensure conductivity. The Cu coating was electrodeposited from an acidic copper electrolyte. The quantitative analysis of the surface was carried out using standard ISO parameters. The thickness of the deposited copper layer was determined using destructive measurements on a digital microscope. The results show that surface modification has a significant effect on the functional properties of the surface quality and the thickness of the deposited copper layer. Full article
(This article belongs to the Special Issue Surface Engineering Techniques in Advanced Manufacturing)
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22 pages, 2365 KiB  
Article
A Quantum Q-Learning Fault Diagnosis Method for Intelligent Manufacturing Equipment
by Yi Chen, Kai Deng, Xuelin Du, Zichao Chang and Tong Wan
Machines 2025, 13(7), 629; https://doi.org/10.3390/machines13070629 - 21 Jul 2025
Viewed by 254
Abstract
In the era of rapid industrial automation advancements, the complexity of intelligent manufacturing equipment has been steadily escalated. Stringent demands for high-efficiency and high-precision diagnosis are increasingly being unmet by conventional fault diagnosis methods. To address these challenges, a novel fault diagnosis approach [...] Read more.
In the era of rapid industrial automation advancements, the complexity of intelligent manufacturing equipment has been steadily escalated. Stringent demands for high-efficiency and high-precision diagnosis are increasingly being unmet by conventional fault diagnosis methods. To address these challenges, a novel fault diagnosis approach grounded in quantum Q-learning is presented in this paper. The distinct advantages of quantum computing are innovatively integrated with the decision-making framework of Q-learning through this method. By harnessing the multi-information-carrying capacities of qubits, vast amounts of multi-source heterogeneous data generated during equipment operation can be efficiently processed. Latent fault features are thereby rapidly uncovered, significantly reducing the time required for fault-feature extraction. Furthermore, optimal decisions can be dynamically formulated by Q-learning within evolving production environments, leveraging precise analysis outcomes from quantum computing. Real-time equipment status is continuously monitored to accurately identify fault types, pinpoint locations, and promptly generate targeted maintenance strategies. Fault-diagnosis tests conducted on typical industrial intelligent manufacturing equipment demonstrate that the quantum Q-learning method outperforms traditional approaches in terms of diagnosis accuracy, efficiency, and adaptability to complex fault patterns. This breakthrough opens up new frontiers for fault diagnosis in intelligent manufacturing systems. Full article
(This article belongs to the Section Advanced Manufacturing)
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15 pages, 2070 KiB  
Article
Synthesis of Vibration Environment Spectra and Fatigue Assessment for Underfloor Equipment in High-Speed EMU Trains
by Can Chen, Lirong Guo, Guoshun Li, Yongheng Li, Yichao Zhang, Hongwei Zhang and Dao Gong
Machines 2025, 13(7), 628; https://doi.org/10.3390/machines13070628 - 21 Jul 2025
Viewed by 185
Abstract
With the continuous development of high-speed electric multiple units (EMUs), vibration issues of vehicles have become increasingly prominent. During operation, the underfloor equipment installed on the carbody is subjected to random multi-point vibrations transmitted from the carbody, inducing significant fatigue damage. This paper [...] Read more.
With the continuous development of high-speed electric multiple units (EMUs), vibration issues of vehicles have become increasingly prominent. During operation, the underfloor equipment installed on the carbody is subjected to random multi-point vibrations transmitted from the carbody, inducing significant fatigue damage. This paper presents a comprehensive analysis of multi-channel vibration environment data for various underfloor equipment across different operating speeds obtained through on-site measurements. A spectral synthetic method grounded in statistical principles is then proposed to generate vibration environment spectra for diverse underfloor equipment. Finally, utilizing fatigue analysis in the frequency domain, the fatigue damage to underfloor equipment is assessed under different operational environments. The research results show that the vibration environment spectrum of the underfloor equipment in high-speed EMU trains differs significantly from the vibration spectrum specified in the IEC 61373 standard, especially at high frequencies. Despite this difference in spectral characteristics, the overall vibration energy values of the two spectra are comparable. Additionally, the vibration spectra of different underfloor equipment exhibit variations that can be attributed to their installation positions. As operational speed increases, the fatigue damage to the underfloor equipment exhibits exponential growth. However, the total accumulated fatigue damage remains relatively low, consistently staying below a value of 1. Full article
(This article belongs to the Special Issue Research and Application of Rail Vehicle Technology)
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18 pages, 5269 KiB  
Article
Analysis of Flexible Bearing Load Under Various Torque Conditions
by Nanxian Zheng, Jia Wang, Miaojie Wu, Huishan Liu and Yourui Tao
Machines 2025, 13(7), 627; https://doi.org/10.3390/machines13070627 - 21 Jul 2025
Viewed by 184
Abstract
This paper aims to develop a model for calculating the ball load of the thin-walled flexible bearing (FB) in a harmonic drive under various external torque conditions. The effect of the flexspline (FS) on the FB ball load is considered, and the equivalent [...] Read more.
This paper aims to develop a model for calculating the ball load of the thin-walled flexible bearing (FB) in a harmonic drive under various external torque conditions. The effect of the flexspline (FS) on the FB ball load is considered, and the equivalent ring is improved to calculate the ball load of the FB. Then, the accuracy of the proposed model in calculating the ball load is verified using a finite element analysis model. Finally, a fitting formula is obtained to rapidly evaluate the FB ball load via the geometrical parameters of the FB and the FS under various external torques. The results show that the FB ball load is mainly affected by the FB maximum radial deformation under low external torque. When subjected to heavy external torque, the maximum ball load is mainly affected by the FS’s geometric parameters. Full article
(This article belongs to the Special Issue Design and Manufacturing for Lightweight Components and Structures)
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26 pages, 6714 KiB  
Article
End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning
by Jernej Mlinarič, Boštjan Pregelj and Gregor Dolanc
Machines 2025, 13(7), 626; https://doi.org/10.3390/machines13070626 - 21 Jul 2025
Viewed by 216
Abstract
This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration [...] Read more.
This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration and sound signals, recorded during the EoL quality inspection process at the end of an industrial manufacturing line. Recorded signals are transformed directly into Mel-frequency spectrograms (MFS) without pre-processing. To remove non-informative frequency bands and increase data relevance, a six-step data reduction procedure was implemented. Furthermore, to improve fault characterization, a reference spectrogram was generated from healthy motors. The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). Traditional EoL inspection methods often rely on threshold-based criteria and expert analysis, which can be inconsistent, time-consuming, and poorly scalable. These methods struggle to detect complex or subtle patterns associated with early-stage faults. The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. The results confirm that this approach can reduce the need for human expert engagement during commissioning, eliminate redundant inspection steps, and improve fault detection consistency, offering significant production efficiency gains. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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12 pages, 1985 KiB  
Article
The Reliability Analysis of a Turbine Rotor Structure Based on the Kriging Surrogate Model
by Haiwei Lin, Liang Yang, Hong Bao, Feng Zhang, Feifei Zhao and Chaoxin Lu
Machines 2025, 13(7), 625; https://doi.org/10.3390/machines13070625 - 21 Jul 2025
Viewed by 181
Abstract
The turbine rotor is a core component in many energy conversion systems, where it is subjected to loads such as aerodynamic and centrifugal forces that make it highly susceptible to damage. Consequently, the reliability of the turbine rotor ranks among the key aspects [...] Read more.
The turbine rotor is a core component in many energy conversion systems, where it is subjected to loads such as aerodynamic and centrifugal forces that make it highly susceptible to damage. Consequently, the reliability of the turbine rotor ranks among the key aspects of concern. This paper proposes an efficient approach based on the kriging model to conduct the reliability analysis of a turbine rotor. First, a parametric model of the turbine rotor was established. This parametric model was subsequently applied in a multifactor fluid–structure interaction model used to analyze the working performance of the turbine rotor. Finally, a kriging surrogate model was built and applied using these data in combination with various reliability analysis methods to analyze the structural reliability and reliability sensitivities of the turbine rotor. Furthermore, the reliability sensitivity results indicated that the outlet pressure had the greatest impact on rotor reliability. Thus, the proposed method was shown to have practical application value in the reliability analysis of the rotor structure. Full article
(This article belongs to the Special Issue Reliability in Mechanical Systems: Innovations and Applications)
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22 pages, 1066 KiB  
Article
GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management
by Mingjun Kuang, Qingwen Hou, Jindong Wang, Jianping Guo and Zhengjun Wei
Machines 2025, 13(7), 624; https://doi.org/10.3390/machines13070624 - 21 Jul 2025
Viewed by 225
Abstract
This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset [...] Read more.
This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space while ensuring control stability by discarding low-performing gain combinations. The resulting high-quality dataset is used to train the ANFIS model, enabling real-time, adaptive tuning of PID gains. Simulation results demonstrate that the proposed GA-ANFIS-PID controller significantly enhances dynamic response, robustness, and adaptability over both the conventional Ziegler–Nichols PID and GA-only PID schemes. The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms. Full article
(This article belongs to the Section Automation and Control Systems)
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18 pages, 2800 KiB  
Article
Research on Multi-Objective Optimization Design of High-Speed Train Wheel Profile Based on RPSTC-GJO
by Mao Li, Hao Ding, Meiqi Wang, Xingda Yang and Bin Kong
Machines 2025, 13(7), 623; https://doi.org/10.3390/machines13070623 - 19 Jul 2025
Viewed by 207
Abstract
Aiming at the problem that the aggravation of the wheel tread wear of high-speed trains leads to the deterioration of train operation performance and an increase in re-profiling times, a multi-objective data-driven optimization design method for the wheel profile is proposed. Firstly, the [...] Read more.
Aiming at the problem that the aggravation of the wheel tread wear of high-speed trains leads to the deterioration of train operation performance and an increase in re-profiling times, a multi-objective data-driven optimization design method for the wheel profile is proposed. Firstly, the chaotic map is introduced into the population initialization process of the golden jackal algorithm. In the later stage of the algorithm iteration, random disturbance is introduced with optimization algebra as the switching condition to obtain an improved optimization algorithm, and the performance index of the optimization algorithm is verified to be superior to other algorithms. Secondly, the improved multi-objective optimization algorithm and data-driven model are used to optimize the tread coordinates and obtain an optimized profile. The vehicle dynamics performance of the optimized profile and the wheel wear evolution after long-term service are compared. The results show that the tread wear index of the left and right wheels in a straight line is reduced by 62.4% and 62.6%, respectively, and the wear index of the left and right wheels in a curved line is reduced by 26.5% and 5.5%, respectively. The stability and curve passing performance of the optimized profile are improved. Under the long-term service conditions of the train, the wear amount of the optimized profile is greatly reduced. After the wear prediction of 200,000 km, the wear amount of the optimized profile is reduced by 60.1%, and it has better curve-passing performance. Full article
(This article belongs to the Section Vehicle Engineering)
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15 pages, 3342 KiB  
Article
Fault-Tolerant Control of the Electro-Mechanical Compound Transmission System of Tracked Vehicles Based on the Anti-Windup PID Algorithm
by Qingkun Xing, Ziao Zhang, Xueliang Li, Datong Qin and Zengxiong Peng
Machines 2025, 13(7), 622; https://doi.org/10.3390/machines13070622 - 18 Jul 2025
Viewed by 234
Abstract
The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper [...] Read more.
The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper proposes three fault-tolerant control methods for three typical fault scenarios of the electromechanical composite transmission system (ECTS) to ensure the normal operation of tracked vehicles. Firstly, an ECTS and the electromechanical coupling dynamics model of the tracked vehicle are established. Moreover, a double-layer anti-windup PID control for motors and an instantaneous optimal control strategy for the engine are proposed in the fault-free case. Secondly, an anti-windup PID control law for motors and an engine control strategy considering the state of charge (SOC) and driving demands are developed in the case of single-side drive motor failure. Thirdly, a B4 clutch control strategy during starting and a steering brake control strategy are proposed in the case of electric drive system failure. Finally, in the straight-driving condition of the tracked vehicle, the throttle opening is set as 0.6, and the motor failure is triggered at 15 s during the acceleration process. Numerical simulations verify the fault-tolerant control strategies’ feasibility, using the tracked vehicle’s maximum speed and acceleration at 30 s as indicators for dynamic performance evaluation. The simulation results show that under single-motor fault, its straight-line driving power drops by 33.37%; with electric drive failure, the drop reaches 43.86%. The vehicle can still maintain normal straight-line driving and steering under fault conditions. Full article
(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)
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27 pages, 11254 KiB  
Article
Improved RRT-Based Obstacle-Avoidance Path Planning for Dual-Arm Robots in Complex Environments
by Jing Wang, Genliang Xiong, Bowen Dang, Jianli Chen, Jixian Zhang and Hui Xie
Machines 2025, 13(7), 621; https://doi.org/10.3390/machines13070621 - 18 Jul 2025
Viewed by 397
Abstract
To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a [...] Read more.
To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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22 pages, 11043 KiB  
Article
Digital Twin-Enabled Adaptive Robotics: Leveraging Large Language Models in Isaac Sim for Unstructured Environments
by Sanjay Nambiar, Rahul Chiramel Paul, Oscar Chigozie Ikechukwu, Marie Jonsson and Mehdi Tarkian
Machines 2025, 13(7), 620; https://doi.org/10.3390/machines13070620 - 17 Jul 2025
Viewed by 437
Abstract
As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems [...] Read more.
As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation. Full article
(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)
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24 pages, 2394 KiB  
Article
Improving the Reliability of Safety Instrumented Systems Under Degradation with an Alternating Testing Strategy
by Walid Mechri and Christophe Simon
Machines 2025, 13(7), 619; https://doi.org/10.3390/machines13070619 - 17 Jul 2025
Viewed by 276
Abstract
This paper presents an alternating testing strategy to improve the reliability of multi-state safety instrumented systems (SISs) under degradation conditions. A dynamic Bayesian network (DBN) model is developed to assess SIS unavailability, integrating proof-testing parameters and capturing multi-state component behavior. Applied initially to [...] Read more.
This paper presents an alternating testing strategy to improve the reliability of multi-state safety instrumented systems (SISs) under degradation conditions. A dynamic Bayesian network (DBN) model is developed to assess SIS unavailability, integrating proof-testing parameters and capturing multi-state component behavior. Applied initially to the actuator layer of a SIS with a 1oo3 (one-out-of-three) redundancy structure, the study examines the impact of extended test durations, showing that the alternating strategy reduces non-zero test durations compared to the simultaneous test strategy. The approach is then extended to a complete SIS, with a case study demonstrating its potential to enhance system reliability and optimize maintenance management by considering degradation and redundancy factors. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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24 pages, 3474 KiB  
Article
Research on Unsupervised Domain Adaptive Bearing Fault Diagnosis Method Based on Migration Learning Using MSACNN-IJMMD-DANN
by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Qinghua Li, Xuelian Yu and Jiaming Chen
Machines 2025, 13(7), 618; https://doi.org/10.3390/machines13070618 - 17 Jul 2025
Viewed by 305
Abstract
To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional [...] Read more.
To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. Firstly, in order to extract fault-type features from the source domain and target domain, this paper establishes a MSACNN based on multi-scale and attention mechanisms. Secondly, to reduce the feature distribution difference between the source and target domains and address the issue of domain distribution differences, the joint maximum mean discrepancy and correlation alignment approaches are used to create the metric criterion. Then, the adversarial loss mechanism in DANN is introduced to reduce the interference of weakly correlated domain features for better fault diagnosis and identification. Finally, the method is validated using bearing datasets from Case Western Reserve University, Jiangnan University, and our laboratory. The experimental results demonstrated that the method achieved higher accuracy across different migration tasks, providing an effective solution for bearing fault diagnosis in industrial environments with varying operating conditions. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))
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22 pages, 9880 KiB  
Article
Dynamic Correction of Preview Weighting in the Driver Model Inspired by Human Brain Memory Mechanisms
by Chang Li, Hengyu Wang, Bo Yang, Haotian Luo, Jianjin Liu and Wei Zheng
Machines 2025, 13(7), 617; https://doi.org/10.3390/machines13070617 - 17 Jul 2025
Viewed by 282
Abstract
Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and [...] Read more.
Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control, 2nd Edition)
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29 pages, 4633 KiB  
Article
Failure Detection of Laser Welding Seam for Electric Automotive Brake Joints Based on Image Feature Extraction
by Diqing Fan, Chenjiang Yu, Ling Sha, Haifeng Zhang and Xintian Liu
Machines 2025, 13(7), 616; https://doi.org/10.3390/machines13070616 - 17 Jul 2025
Viewed by 269
Abstract
As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the [...] Read more.
As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article
(This article belongs to the Special Issue Recent Analysis and Research in the Field of Vehicle Traffic Safety, 2nd Edition)
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27 pages, 49290 KiB  
Review
AI-Driven Robotics: Innovations in Design, Perception, and Decision-Making
by Lei Li, Li Li, Mantian Li and Ke Liang
Machines 2025, 13(7), 615; https://doi.org/10.3390/machines13070615 - 17 Jul 2025
Viewed by 648
Abstract
Robots are increasingly being used across industries, healthcare, and service sectors to perform a wide range of tasks. However, as these tasks become more complex and environments more unpredictable, the need for adaptable robots continues to grow—bringing with it greater technological challenges. Artificial [...] Read more.
Robots are increasingly being used across industries, healthcare, and service sectors to perform a wide range of tasks. However, as these tasks become more complex and environments more unpredictable, the need for adaptable robots continues to grow—bringing with it greater technological challenges. Artificial intelligence (AI), driven by large datasets and advanced algorithms, plays a pivotal role in addressing these challenges and advancing robotics. AI enhances robot design by making it more intelligent and flexible, significantly improving robot perception to better understand and respond to surrounding environments and empowering more intelligent control and decision-making. In summary, AI contributes to robotics through design optimization, environmental perception, and intelligent decision-making. This article explores the driving role of AI in robotics and presents detailed examples of its integration with fields such as embodied intelligence, humanoid robots, big data, and large AI models, while also discussing future prospects and challenges in this rapidly evolving field. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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17 pages, 2823 KiB  
Article
Information Reuse Methods for Multi-Dimensional Models in Discrete Workshops
by Ruiping Luo and Jiaxing Zhu
Machines 2025, 13(7), 614; https://doi.org/10.3390/machines13070614 - 17 Jul 2025
Viewed by 212
Abstract
With the gradual development of digital twin technology from theory to practice, the importance of the efficient reuse of existing digital twin models has become increasingly prominent in order to reduce the waste of resources and additional costs caused by repeated modeling. To [...] Read more.
With the gradual development of digital twin technology from theory to practice, the importance of the efficient reuse of existing digital twin models has become increasingly prominent in order to reduce the waste of resources and additional costs caused by repeated modeling. To address the difficulty of reusing multi-dimensional model information (MMI) in existing digital twin models during the conversion process from geometric models to digital twin models, this paper proposes a method for reusing MMI in discrete workshops. First, MMI and its representations are defined and constructed. Subsequently, a model-matching approach is introduced to identify appropriate MMIs for geometric models. Following this, a reuse strategy for workshop MMIs is thoroughly explained. Finally, the effectiveness of the proposed method is validated through case studies in the arc-welding workshop. The accuracy of single-model matching remains consistently at 1 across all model tests, and the proposed method reduces the total number of operations by 126 (94.7%) compared to existing methods in multi-device model construction. The results show that this method can effectively organize the workshop digital twin model, compensate for the shortage of digital twin model reuse, and help engineers reuse the existing MMI to build a digital twin model. Full article
(This article belongs to the Section Industrial Systems)
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16 pages, 3251 KiB  
Article
Vibration Fatigue Characteristics of a High-Speed Train Bogie and Traction Motor Based on Field Measurement and Spectrum Synthesis
by Lirong Guo, Guoshun Li, Can Chen, Yichao Zhang, Hongwei Zhang and Dao Gong
Machines 2025, 13(7), 613; https://doi.org/10.3390/machines13070613 - 16 Jul 2025
Viewed by 214
Abstract
In this study, the fatigue behavior in high-speed train bogie frames and mounted traction motors was investigated through dynamic stress measurements and vibration analysis. A spectrum synthesis method was developed to integrate multipoint random vibrations from the bogie frame into a unified excitation [...] Read more.
In this study, the fatigue behavior in high-speed train bogie frames and mounted traction motors was investigated through dynamic stress measurements and vibration analysis. A spectrum synthesis method was developed to integrate multipoint random vibrations from the bogie frame into a unified excitation spectrum for motor fatigue assessment. The results demonstrate that fatigue damage in the bogie frame progresses linearly with increasing speed, with critical stress concentrations being identified at the motor base weld seams (41.4 MPa equivalent stress at 400 km/h). Traction motor vibration spectra were found to deviate substantially from IEC 61373 standards, leading to higher fatigue damage that follows an exponential growth pattern relative to speed increases. The proposed methodology provides direct experimental validation of component-specific fatigue mechanisms under operational loading conditions. Full article
(This article belongs to the Special Issue Research and Application of Rail Vehicle Technology)
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24 pages, 6089 KiB  
Article
An Optimized 1-D CNN-LSTM Approach for Fault Diagnosis of Rolling Bearings Considering Epistemic Uncertainty
by Onur Can Kalay
Machines 2025, 13(7), 612; https://doi.org/10.3390/machines13070612 - 16 Jul 2025
Viewed by 284
Abstract
Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and [...] Read more.
Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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23 pages, 951 KiB  
Article
Multi-Objective Evolution and Swarm-Integrated Optimization of Manufacturing Processes in Simulation-Based Environments
by Panagiotis D. Paraschos, Georgios Papadopoulos and Dimitrios E. Koulouriotis
Machines 2025, 13(7), 611; https://doi.org/10.3390/machines13070611 - 16 Jul 2025
Viewed by 369
Abstract
This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data [...] Read more.
This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article
(This article belongs to the Section Advanced Manufacturing)
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12 pages, 2871 KiB  
Article
Multi-Objective Optimization Design of Low-Frequency Band Gap for Local Resonance Acoustic Metamaterials Based on Genetic Algorithm
by Jianjiao Deng, Yunuo Qin, Xi Chen, Yanyong He, Yu Song, Xinpeng Zhang, Wenting Ma, Shoukui Li and Yudong Wu
Machines 2025, 13(7), 610; https://doi.org/10.3390/machines13070610 - 16 Jul 2025
Viewed by 294
Abstract
Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for [...] Read more.
Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for local resonance acoustic metamaterials (LRAMs) based on a multi-objective genetic algorithm. Within a COMSOL Multiphysics 6.2 with MATLAB R2024b co-simulation framework, a parameterized unit cell model of the metamaterial is constructed. The optimization process targets two objectives: minimizing the band gap’s deviation from the target and reducing the structural mass. A multi-objective fitness function is formulated by incorporating the band gap deviation and structural mass constraints, and non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform a global search over the geometric parameters of the resonant unit. The resulting Pareto-optimal solution set achieves a unit cell mass as low as 26.49 g under the constraint that the band gap deviation does not exceed 2 Hz. The results of experimental validation show that the optimized metamaterial configuration reduces the peak of the low-frequency frequency response function (FRF) at 63 Hz by up to 75% in a car door structure. Furthermore, the simulation predictions exhibit good agreement with the experimental measurements, confirming the effectiveness and reliability of the proposed method in engineering applications. The proposed multi-objective optimization framework is highly general and extensible and capable of effectively balancing between the acoustic performance and structural mass, thus providing an efficient engineering solution for low-frequency noise control problems. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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28 pages, 9135 KiB  
Article
Performance Analysis of a Reciprocating Refrigeration Compressor Under Variable Operating Speeds
by Willian T. F. D. da Silva, Vitor M. Braga and Cesar J. Deschamps
Machines 2025, 13(7), 609; https://doi.org/10.3390/machines13070609 - 15 Jul 2025
Viewed by 323
Abstract
Variable-speed reciprocating compressors (VSRCs) have been increasingly used in domestic refrigeration due to their ability to modulate cooling capacity and reduce energy consumption. A detailed understanding of performance-limiting factors such as volumetric and exergetic inefficiencies is essential for optimizing their operation. An experimentally [...] Read more.
Variable-speed reciprocating compressors (VSRCs) have been increasingly used in domestic refrigeration due to their ability to modulate cooling capacity and reduce energy consumption. A detailed understanding of performance-limiting factors such as volumetric and exergetic inefficiencies is essential for optimizing their operation. An experimentally validated simulation model was developed using GT-SUITE to analyze a VSRC operating with R-600a across speeds from 1800 to 6300 rpm. Volumetric inefficiencies were quantified using a stratification methodology, while an exergy-based approach was adopted to assess the main sources of thermodynamic inefficiency in the compressor. Unlike traditional energy analysis, exergy analysis reveals where and why irreversibilities occur, linking them directly to power consumption and providing a framework for optimizing design. Results reveal that neither volumetric nor exergy efficiency varies monotonically with compressor speed. At low speeds, exergetic losses are dominated by the electrical motor (up to 19% of input power) and heat transfer (up to 13.5%). Conversely, at high speeds, irreversibilities from fluid dynamics become critical, with losses from discharge valve throttling reaching 5.8% and bearing friction increasing to 6.5%. Additionally, key volumetric inefficiencies arise from piston–cylinder leakage, which causes up to a 4.5% loss at low speeds, and discharge valve backflow, causing over a 5% loss at certain resonant speeds. The results reveal complex speed-dependent interactions between dynamic and thermodynamic loss mechanisms in VSRCs. The integrated modeling approach offers a robust framework for diagnosing inefficiencies and supports the development of more energy-efficient compressor designs. Full article
(This article belongs to the Special Issue Theoretical and Experimental Study on Compressor Performance)
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18 pages, 8131 KiB  
Article
Rapid Dismantling of Aluminum Stranded Conductors: An Automated Approach
by Zhinan Cao, Jie Feng, Shijun Xie, Qian Peng, Jiahui Chen, Cheng Wen and Jigang Huang
Machines 2025, 13(7), 608; https://doi.org/10.3390/machines13070608 - 15 Jul 2025
Viewed by 278
Abstract
Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To [...] Read more.
Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To demonstrate the proposed method, a modular prototype machine that includes four main functional modules (transmission, untwisting, separation, and shearing) was developed. Experimental results from the prototype dismantling machine demonstrated that when processing JL/G3A-500/65 conductors (Sichuan Star Cable Co., Ltd., Leshan, China) under the following operational parameters—0.5 rad/s rotational speed, 10 cm extension length, 2400 N clamping force, and 40 N·m torque application—the system achieved a single-layer dismantling efficiency exceeding 98%. This represents a significant improvement in operational speed compared to traditional manual methods. The developed machine achieved collaborative control of axial feed, reverse untwisting, and automatic shearing, elevating the untwisting qualification rate to 95%. This solution provides an efficient and safe approach to conductor inspection, demonstrating substantial engineering application value. Full article
(This article belongs to the Special Issue Design and Experimental Activity of Testing Machines and Mechanical Test Rigs)
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18 pages, 4099 KiB  
Article
Numerical Study of the Effect of Unsteady Aerodynamic Forces on the Fatigue Load of Yawed Wind Turbines
by Dereje Haile Hirgeto, Guo-Wei Qian, Xuan-Yi Zhou and Wei Wang
Machines 2025, 13(7), 607; https://doi.org/10.3390/machines13070607 - 15 Jul 2025
Viewed by 292
Abstract
The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw [...] Read more.
The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw offsets using blade element momentum theory, dynamic blade element momentum, and the converging Lagrange filaments vortex method, all implemented in OpenFAST. Simulations employed yaw angles from −40° to 40°, with turbulent inflow generated by TurbSim, an OpenFAST tool for realistic wind conditions. Fatigue loads were calculated according to IEC 61400-1 design load case 1.2 standards, using thirty simulations per yaw angle across five wind speed bins. Damage equivalent load was evaluated via rainflow counting, Miner’s rule, and Goodman correction. Results showed that the free vortex method, by modeling unsteady aerodynamic forces, yielded distinct differences in damage equivalent load compared to the blade element method in yawed conditions. The free vortex method predicted lower damage equivalent load for the low-speed shaft bending moment at negative yaw offsets, attributed to its improved handling of unsteady effects that reduce load variations. Conversely, for yaw offsets above 20°, the free vortex method indicated higher damage equivalent for low-speed shaft torque, reflecting its accurate capture of dynamic inflow and unsteady loading. These findings highlight the critical role of unsteady aerodynamics in fatigue load predictions and demonstrate the free vortex method’s value within OpenFAST for realistic damage equivalent load estimates in yawed turbines. The results emphasize the need to incorporate unsteady aerodynamic models like the free vortex method to accurately assess yaw offset impacts on wind turbine component fatigue. Full article
(This article belongs to the Special Issue Aerodynamic Analysis of Wind Turbine Blades)
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23 pages, 9638 KiB  
Article
A Study on the Influence Mechanism of the Oil Injection Distance on the Oil Film Distribution Characteristics of the Gear Meshing Zone
by Wentao Zhao, Lin Li and Gaoan Zheng
Machines 2025, 13(7), 606; https://doi.org/10.3390/machines13070606 - 14 Jul 2025
Viewed by 309
Abstract
Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational [...] Read more.
Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational fluid dynamics (CFD) and dynamic mesh technology. By implementing the volume of fluid (VOF) multiphase flow model and the standard k-ω turbulence model, the study simulates the dynamic distribution of lubricant in gear meshing zones and analyzes critical parameters such as the oil volume fraction, eddy viscosity, and turbulent kinetic energy. The results show that reducing the oil injection distance significantly enhances lubricant coverage and continuity: as the injection distance increases from 4.8 mm to 24 mm, the lubricant shifts from discrete droplets to a dense wedge-shaped film, mitigating lubrication failure risks from secondary atomization and energy loss. The optimized injection distance also improves the spatial stability of eddy viscosity and suppresses excessive dissipation of turbulent kinetic energy, enhancing both the shear-load capacity and thermal management. Dynamic data from monitoring point P show that reducing the injection distance stabilizes lubricant velocity and promotes more consistent oil film formation and heat transfer. Through multiphysics simulations and parametric analysis, this study elucidates the interaction between geometric parameters and hydrodynamic behaviors in jet lubrication systems. The findings provide quantitative evaluation methods for structural optimization and energy control in gear lubrication systems, offering theoretical insights for thermal management and reliability enhancement in high-speed transmission. These results contribute to the lightweight design and sustainable development of industrial equipment. Full article
(This article belongs to the Section Friction and Tribology)
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24 pages, 1076 KiB  
Article
Visual–Tactile Fusion and SAC-Based Learning for Robot Peg-in-Hole Assembly in Uncertain Environments
by Jiaxian Tang, Xiaogang Yuan and Shaodong Li
Machines 2025, 13(7), 605; https://doi.org/10.3390/machines13070605 - 14 Jul 2025
Viewed by 377
Abstract
Robotic assembly, particularly peg-in-hole tasks, presents significant challenges in uncertain environments where pose deviations, varying peg shapes, and environmental noise can undermine performance. To address these issues, this paper proposes a novel approach combining visual–tactile fusion with reinforcement learning. By integrating multimodal data [...] Read more.
Robotic assembly, particularly peg-in-hole tasks, presents significant challenges in uncertain environments where pose deviations, varying peg shapes, and environmental noise can undermine performance. To address these issues, this paper proposes a novel approach combining visual–tactile fusion with reinforcement learning. By integrating multimodal data (RGB image, depth map, tactile force information, and robot body pose data) via a fusion network based on the autoencoder, we provide the robot with a more comprehensive perception of its environment. Furthermore, we enhance the robot’s assembly skill ability by using the Soft Actor–Critic (SAC) reinforcement learning algorithm, which allows the robot to adapt its actions to dynamic environments. We evaluate our method through experiments, which showed clear improvements in three key aspects: higher assembly success rates, reduced task completion times, and better generalization across diverse peg shapes and environmental conditions. The results suggest that the combination of visual and tactile feedback with SAC-based learning provides a viable and robust solution for robotic assembly in uncertain environments, paving the way for scalable and adaptable industrial robots. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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58 pages, 38117 KiB  
Article
Multi-Disciplinary Investigations on the Best Flying Wing Configuration for Hybrid Unmanned Aerial Vehicles: A New Approach to Design
by Janani Priyadharshini Veeraperumal Senthil Nathan, Martin Navamani Chellapandian, Vijayanandh Raja, Parvathy Rajendran, It Ee Lee, Naveen Kumar Kulandaiyappan, Beena Stanislaus Arputharaj, Subhav Singh and Deekshant Varshney
Machines 2025, 13(7), 604; https://doi.org/10.3390/machines13070604 - 14 Jul 2025
Viewed by 431
Abstract
Flying wing Unmanned Aerial Vehicles (UAVs) are an interesting flight configuration, considering its benefits over aerodynamic, structural and added stealth aspects. The existing configurations are thoroughly studied from the literature survey and useful observations with respect to design and analysis are obtained. The [...] Read more.
Flying wing Unmanned Aerial Vehicles (UAVs) are an interesting flight configuration, considering its benefits over aerodynamic, structural and added stealth aspects. The existing configurations are thoroughly studied from the literature survey and useful observations with respect to design and analysis are obtained. The proposed design method includes distinct calculations of the UAV and modelling using 3D experience. The created innovative models are simulated with the help of computational fluid dynamics techniques in ANSYS Fluent to obtain the aerodynamic parameters such as forces, pressure and velocity. The optimization process continues to add more desired modifications to the model, to finalize the best design of flying wing frame for the chosen application and mission profile. In total, nine models are developed starting with the base model, then leading to the conventional, advanced and nature inspired configurations such as the falcon and dragonfly models, as it has an added advantage of producing high maneuverability and lift. Following this, fluid structure interaction analysis has been performed for the best performing configurations, resulting in the determination of variations in the structural behavior with the imposition of advanced composite materials, namely, boron, Kevlar, glass and carbon fiber-reinforced polymers. In addition to this, a hybrid material is designed by combining two composites that resulted in superior material performance when imposed. Control dynamic study is performed for the maneuvers planned as per mission profile, to ensure stability during flight. All the resulting parameters obtained are compared with one another to choose the best frame of the flying wing body, along with the optimum material to be utilized for future analysis and development. Full article
(This article belongs to the Special Issue Design and Application of Bionic Robots)
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26 pages, 3701 KiB  
Article
Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP
by Yongjuan Zhao, Chaozhe Guo, Jiangyong Mi, Lijin Wang, Haidi Wang and Hailong Zhang
Machines 2025, 13(7), 603; https://doi.org/10.3390/machines13070603 - 12 Jul 2025
Viewed by 341
Abstract
Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective [...] Read more.
Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively. Full article
(This article belongs to the Section Vehicle Engineering)
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19 pages, 2299 KiB  
Article
A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application
by Valentina De Simone, Valentina Di Pasquale, Joanna Calabrese, Salvatore Miranda and Raffaele Iannone
Machines 2025, 13(7), 602; https://doi.org/10.3390/machines13070602 - 12 Jul 2025
Viewed by 374
Abstract
Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to [...] Read more.
Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs. Full article
(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)
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