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Volume 13
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Machines, Volume 13, Issue 5 (May 2025) – 97 articles

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26 pages, 3542 KiB  
Article
Hybrid Damping Mode MR Damper: Development and Experimental Validation with Semi-Active Control
by Jeongwoo Lee and Kwangseok Oh
Machines 2025, 13(5), 435; https://doi.org/10.3390/machines13050435 - 20 May 2025
Abstract
This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This [...] Read more.
This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. Experimental validation on a mid-sized sedan demonstrated significant improvements, including a 30–40% reduction in vertical acceleration and pitch/roll rates. These enhancements improve vehicle safety by reducing body motion during critical maneuvers, potentially lowering accident risk and driver fatigue. In addition to performance gains, the simplified MR damper architecture and modular control facilitate easier integration into diverse vehicle platforms, potentially streamlining vehicle design and manufacturing processes and enabling cost-effective adoption in mass-market applications. These findings highlight the potential of MR dampers to support next-generation vehicle architectures with enhanced adaptability and manufacturability. Full article
(This article belongs to the Special Issue Adaptive Control Using Magnetorheological Technology)
18 pages, 4863 KiB  
Article
Fault Diagnosis in a 2 MW Wind Turbine Drive Train by Vibration Analysis: A Case Study
by Rafael Tuirán, Héctor Águila, Esteve Jou, Xavier Escaler and Toufik Mebarki
Machines 2025, 13(5), 434; https://doi.org/10.3390/machines13050434 - 20 May 2025
Abstract
This paper presents a vibration analysis method for detecting typical faults in gears of the drive train of a 2 MW wind turbine. The data were collected over a one-year period from an operating wind turbine with a gearbox composed of one planetary [...] Read more.
This paper presents a vibration analysis method for detecting typical faults in gears of the drive train of a 2 MW wind turbine. The data were collected over a one-year period from an operating wind turbine with a gearbox composed of one planetary stage and two helical gear stages. Failures in two pairs of helical gears were identified: one involving pitting and wear in the gears connecting the intermediate-speed shaft to the low-speed shaft, and another one involving significant material detachment in the gears connecting the intermediate-speed shaft to the high-speed shaft. The continuous evaluation of time signals, frequency spectra, and amplitude modulations allowed the most sensitive sensors and frequencies for predicting surface damage on gear teeth in this type of turbine to be determined. A steady-state frequency analysis was performed, enabling the detection of the aforementioned surface faults. This approach is simpler compared with more complex transient-state techniques. By tracking vibration signals over time, the importance of analyzing gear mesh frequencies and their harmonics was highlighted. Additionally, it was found that the progression of gear damage was dependent on the power output of the wind turbine. As a result, the most appropriate ranges of power were identified, within which the evolution of the vibration measurement was associated with the damage evolution. Since many turbines currently in operation have similar designs and power output levels, the present findings can serve as a guideline for monitoring an extensive number of units. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis, 2nd Edition)
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29 pages, 7349 KiB  
Article
Dynamic Error Compensation for Ball Screw Feed Drive Systems Based on Prediction Model
by Hongda Liu, Yonghao Guo, Jiaming Liu and Wentie Niu
Machines 2025, 13(5), 433; https://doi.org/10.3390/machines13050433 - 20 May 2025
Abstract
The dynamic error is the dominant factor affecting multi-axis CNC machining accuracy. Predicting and compensating for dynamic errors is vital in high-speed machining. This paper proposes a novel prediction-model-based approach to predict and compensate for the ball screw feed system’s dynamic error. Based [...] Read more.
The dynamic error is the dominant factor affecting multi-axis CNC machining accuracy. Predicting and compensating for dynamic errors is vital in high-speed machining. This paper proposes a novel prediction-model-based approach to predict and compensate for the ball screw feed system’s dynamic error. Based on the lumped and distributed mass methods, this method constructs a parameterized dynamic model relying on the moving component’s position for electromechanical coupling modeling. Using Latin Hypercube Sampling and numerical simulation, a sample set containing the input and output of one control cycle is obtained, which is used to train a Cascade-Forward Neural Network to predict dynamic errors. Finally, a feedforward compensation strategy based on the prediction model is proposed to improve tracking performance. The proposed method is applied to a ball screw feed system. Tracking error simulations and experiments are conducted and compared with the transfer function feedforward compensation. Typical trajectories are designed to validate the effectiveness of the electromechanical coupling model, the dynamic error prediction model, and the feedforward compensation strategy. The results show that the prediction model exhibits a maximum prediction deviation of 1.8% for the maximum tracking error and 13% for the average tracking error. The proposed compensation method with friction compensation achieves a maximum reduction rate of 76.7% for the maximum tracking error and 63.7% for the average tracking error. Full article
(This article belongs to the Section Automation and Control Systems)
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19 pages, 3197 KiB  
Article
The Innovative Design and Performance Testing of a Mobile Robot for the Automated Installation of Spacers on Six-Split Transmission Lines
by Jie Pan, Yongfeng Cheng, Chunhua Hu, Ming Jiang, Yong Ma, Fanhao Meng and Qiang Shi
Machines 2025, 13(5), 432; https://doi.org/10.3390/machines13050432 - 19 May 2025
Abstract
The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the [...] Read more.
The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the disadvantage of heavy labor intensity and a high risk factor. The robots that install two-split and four-split spacer bars cannot be applied to the complex operating conditions of six-split transmission lines. In order to improve the installation efficiency of spacers and reduce operating costs and risks, a new type of spacer-installing robot was researched based on the six-split transmission lines in this paper. Through the theoretical analysis of the wire’s arc sag, the moving device of the robot was designed. In order to improve the operating efficiency of the robot, the storage and feeding device of the six spacers was designed. A planar arm with the ability to assemble the spacer was designed. The overall design of the robot was completed by integrating the design of each unit. Through the experimental test, the results indicated that the robot was capable of installing six spacers at once, the maximum moving slope was 15 degrees, and the error rate in the spacer installation was 2.33%, which matched the manual installation of the spacers. The robot provided new ideas for the design of new transmission line engineering equipment and expanded the scope of the application of robots in the power industry. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
26 pages, 7159 KiB  
Article
Methodology for Human–Robot Collaborative Assembly Based on Human Skill Imitation and Learning
by Yixuan Zhou, Naisheng Tang, Ziyi Li and Hanlei Sun
Machines 2025, 13(5), 431; https://doi.org/10.3390/machines13050431 - 19 May 2025
Abstract
With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which [...] Read more.
With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which utilizes electromyography (EMG) signals as an interaction interface and integrates human skill imitation with reinforcement learning. Specifically, to manage the dynamic variation in muscle coordination patterns induced by joint angle changes, a temporal graph neural network enhanced with an Angle-Guided Attention mechanism is developed. This method adaptively models the topological relationships among muscle groups, enabling high-precision three-dimensional dynamic arm force estimation. Furthermore, an expert reward function and a fuzzy experience replay mechanism are introduced in the reinforcement learning model to guide the human skill learning process, thereby enhancing collaborative comfort and smoothness. The proposed approach is validated through a collaborative assembly task. Experimental results show that the proposed arm force estimation model reduces estimation errors by 10.38%, 8.33%, and 11.20% across three spatial directions compared to a conventional Deep Long Short-Term Memory (Deep-LSTM). Moreover, it significantly outperforms state-of-the-art methods, including traditional imitation learning and adaptive admittance control, in terms of collaborative comfort, smoothness, and assembly accuracy. Full article
(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)
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16 pages, 3243 KiB  
Article
Comparative Analysis of Dry, Minimum Quantity Lubrication, and Nano-Reinforced Minimum Quantity Lubrication Environments on the Machining Performance of AZ91D Magnesium Alloy
by Berat Baris Buldum, Kamil Leksycki and Suleyman Cinar Cagan
Machines 2025, 13(5), 430; https://doi.org/10.3390/machines13050430 - 19 May 2025
Abstract
This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting [...] Read more.
This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting speed (150–450 m/min), feed rate (0.05–0.2 mm/rev), and depth of cut (0.5–2 mm). The machining performance was evaluated through cutting force measurements, surface roughness analysis, and tool wear examination using SEM. The results demonstrate that the NanoMQL environment significantly outperforms both dry and conventional MQL conditions, providing a 42.2% improvement in surface quality compared to dry machining and a 33.6% improvement over conventional MQL. Cutting forces were predominantly influenced by the depth of cut and the feed rate, while cutting speed showed variable effects. SEM analysis revealed that the NanoMQL environment substantially reduced built-up edge formation and flank wear, particularly under aggressive cutting conditions. The superior performance of the NanoMQL environment is attributed to the enhanced thermal conductivity and lubrication properties of carbon nanotubes, which form a protective tribofilm at the tool–workpiece interface. This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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32 pages, 1321 KiB  
Review
Advancements in State-of-the-Art Ankle Rehabilitation Robotic Devices: A Review of Design, Actuation and Control Strategies
by Asna Kalsoom, Muhammad Faizan Shah and Muhammad Umer Farooq
Machines 2025, 13(5), 429; https://doi.org/10.3390/machines13050429 - 19 May 2025
Abstract
Neurological disorders like stroke are one of the main causes of motor dysfunction and gait function disabilities in humans. These disorders impact the mobility of patients often leading to weakened and impaired ankle joints which further compromise their balance and walking abilities. Over [...] Read more.
Neurological disorders like stroke are one of the main causes of motor dysfunction and gait function disabilities in humans. These disorders impact the mobility of patients often leading to weakened and impaired ankle joints which further compromise their balance and walking abilities. Over the span of the last twenty years, there has been a rising interest in designing, developing, and using rehabilitative robots for patients suffering from various ankle joint disabilities. These robotic devices are developed by employing diverse mechanical designs, materials, and control strategies. The aim of this study is to provide a detailed overview of the recent developments in mechanical design, actuation, and control strategies of ankle rehabilitation robots. Experimental evaluation of the discussed ankle robots has also been carried out discussing their results and limitations. This article concludes by highlighting future challenges and opportunities for the advancement of ankle rehabilitation robots, stressing the need for robust and effective devices to better serve patients. Full article
(This article belongs to the Special Issue Recent Advances in Medical Robotics)
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19 pages, 2666 KiB  
Article
Conceptual Design and Analysis of a Trans-Domain Aircraft Based on the Camber Morphing Wing
by Mingzhen Wang, Mingxuan Xu, Xing Shen, Zhenyang Lai, Yan Zhao, Chen Wang and Qi Hu
Machines 2025, 13(5), 428; https://doi.org/10.3390/machines13050428 - 19 May 2025
Abstract
Multi-functionality and high mission adaptability are important trends in the development of future aircrafts. Trans-domain aircraft, with their unique take-off and landing capabilities and cross-medium capability, have significant potential in the field of emergency rescue, marine monitoring and tourism. Trans-domain aircraft will meet [...] Read more.
Multi-functionality and high mission adaptability are important trends in the development of future aircrafts. Trans-domain aircraft, with their unique take-off and landing capabilities and cross-medium capability, have significant potential in the field of emergency rescue, marine monitoring and tourism. Trans-domain aircraft will meet various flight conditions in different domains. Therefore, the design of wing structures must consider the mechanical effects of different media on the aircraft. In the current study, a fishbone variable camber wing is proposed based on the concept of a camber morphing wing. The relationship between the actuation force and the trailing edge deflection is analyzed using the fluid–structure interaction. The flight performance of the flight conditions including cruise or climb underneath and cruise above the water can also be evaluated in the design iteration since the load-carrying capability can be satisfied and the structural deformation of the fluid loads and the actuators is taken into account. Finite element analysis is also employed for the structural verification. Finally, a structural model is manufactured, which is tested above and under water by measuring the trailing edge deflection using the digital image correlation technology. Full article
(This article belongs to the Section Machine Design and Theory)
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28 pages, 7860 KiB  
Article
Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator
by Vasileios I. Vlachou and Theoklitos S. Karakatsanis
Machines 2025, 13(5), 427; https://doi.org/10.3390/machines13050427 - 19 May 2025
Abstract
Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous [...] Read more.
Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous Motor (PMSM), which is subject to mechanical and electrical stress during continuous operation. This necessitates advanced monitoring techniques to ensure safety, system reliability, and reduced maintenance costs. In this study, a fault-tolerant PMSM is designed and evaluated through 2D Finite Element Analysis (FEA), optimizing key electromagnetic parameters. The design is validated through experimental testing on a real elevator setup, capturing operational data under various loading conditions. These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. The results confirm that integrating a fault-tolerant PMSM design with real-time data analytics offers a reliable solution for early fault detection, minimizing downtime and enhancing elevator safety. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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21 pages, 16495 KiB  
Article
Tactile Force Sensing for Admittance Control on a Quadruped Robot
by Thijs Van Hauwermeiren, Annelies Coene and Guillaume Crevecoeur
Machines 2025, 13(5), 426; https://doi.org/10.3390/machines13050426 - 19 May 2025
Abstract
Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to [...] Read more.
Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to modeling errors. In contrast, direct sensing of contact forces at the feet provides more accurate and immediate feedback. Beyond force magnitude, tactile sensing also enables richer contact interpretation, such as detecting force direction and surface properties. In this work, we show how tactile sensor information can be used inside the feedback of the control loop to achieve compliance of legged robots during ground contact. The three main contributions are (i) a fast and computationally efficient 3D force reconstruction method tailored for spherical tactile sensors, (ii) a tactile admittance controller that adjusts leg motions to achieve the desired GRFs and compliance, and (iii) experimental validation on a quadruped robot, demonstrating enhanced load distribution and balance during external perturbations and locomotion. The results show that the peak ground reaction forces were reduced by 55% while balancing on a beam. During a locomotion scenario involving sudden touchdown after a fall, the tactile admittance controller reduced oscillations and regained stability compared to proportional–derivative (PD) control. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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20 pages, 5954 KiB  
Article
Research on Vehicle Road Noise Prediction Based on AFW-LSTM
by Yan Ma, Ruxue Dai, Tao Liu, Jian Liu, Shukai Yang and Jingjing Wang
Machines 2025, 13(5), 425; https://doi.org/10.3390/machines13050425 - 19 May 2025
Abstract
The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer [...] Read more.
The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer path analysis) method and CAE (Computer-Aided Engineering) method to analyze the road noise problem has the problems of complex transfer path, difficult acquisition of modeling parameters, long duration and high cost. Therefore, based on the road noise hierarchy constructed according to the road noise transmission path, the LSTM (Long Short-Term Memory) network is introduced to establish a data-driven prediction model, which effectively avoids the defects of the TPA method and CAE in analyzing road noise problems. Based on the LSTM prediction model, the AFW (adaptive feature weight) method is introduced to improve the model’s attention to the key features in the input data and finally improve the accuracy and robustness of the road noise prediction model. The results show that the accuracy (RMSE = 1.74 (dB)) and generalization ability (MAE = 2.60 (dB), R2 = 0.924) of the AFW-LSTM model are better than other models. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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19 pages, 8806 KiB  
Article
An Adaptive Control Strategy with Switching Gain and Forgetting Factor for a Robotic Arm Manipulator
by Mohammed Yousri Silaa, Oscar Barambones, Aissa Bencherif and Ilyas Rougab
Machines 2025, 13(5), 424; https://doi.org/10.3390/machines13050424 - 18 May 2025
Viewed by 54
Abstract
This paper presents an adaptive sliding mode controller (ASMC) with the implication of a forgetting factor for a two-degree-of-freedom (2-DOF) arm robot manipulator trajectory tracking. The proposed approach builds upon conventional sliding mode control (SMC), which is well known for its robustness and [...] Read more.
This paper presents an adaptive sliding mode controller (ASMC) with the implication of a forgetting factor for a two-degree-of-freedom (2-DOF) arm robot manipulator trajectory tracking. The proposed approach builds upon conventional sliding mode control (SMC), which is well known for its robustness and low tracking error. The controller dynamically adjusts this parameter by introducing an adaptive mechanism to enhance trajectory tracking, guarantee high robustness, and reduce chattering effects. In order to mitigate gain drift, a forgetting factor is incorporated into the adaptation law, ensuring stable and reliable control performance. Stability is validated using Lyapunov theory, and the effectiveness of the proposed ASMC is evaluated through numerical simulations. The simulations are conducted in MATLAB R2023b using a dynamic model of the 2-DOF robotic manipulator. Comparative results with conventional SMC confirm that the adaptive approach significantly improves tracking accuracy, noise robustness, and chattering suppression. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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27 pages, 6806 KiB  
Article
Rolling Bearing Fault Diagnosis Based on VMD-DWT and HADS-CNN-BiLSTM Hybrid Model
by Luchuan Shao, Bing Zhao and Xutao Kang
Machines 2025, 13(5), 423; https://doi.org/10.3390/machines13050423 - 17 May 2025
Viewed by 82
Abstract
This study proposes a hybrid framework for rolling bearing fault diagnosis by integrating a Variational Mode Decomposition–Discrete Wavelet Transform (VMD-DWT) with a Hybrid Attention-Based Depthwise Separable CNN-BiLSTM (HADS-CNN-BiLSTM) to address noise interference and low diagnostic accuracy under complex conditions. The vibration signals are [...] Read more.
This study proposes a hybrid framework for rolling bearing fault diagnosis by integrating a Variational Mode Decomposition–Discrete Wavelet Transform (VMD-DWT) with a Hybrid Attention-Based Depthwise Separable CNN-BiLSTM (HADS-CNN-BiLSTM) to address noise interference and low diagnostic accuracy under complex conditions. The vibration signals are first reconstructed using a genetic algorithm (GA)-optimized VMD and particle swarm optimization (PSO)-optimized DWT for noise suppression. Subsequently, the denoised signals undergo multimodal feature fusion through depthwise separable convolution, triple attention mechanisms, and BiLSTM temporal modeling. The hybrid model incorporates dynamic learning rate scheduling and a two-stage progressive training strategy to accelerate convergence. The experimental results on the Case Western Reserve University (CWRU) dataset demonstrate 99.58% fault diagnosis accuracy in precision, recall, and the F1 Score, while achieving 100% accuracy on the Xi’an Jiaotong University (XJTU-SY) dataset, confirming superior generalization and robustness under varying signal-to-noise ratios. The framework provides an effective solution for enhancing rolling bearing fault diagnosis technologies. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 3071 KiB  
Article
Geometrical Analysis of 3D-Printed Polymer Spur Gears
by Levente Czégé and Gábor Ruzicska
Machines 2025, 13(5), 422; https://doi.org/10.3390/machines13050422 - 17 May 2025
Viewed by 140
Abstract
In this paper, we are looking for the answer to the following question: what geometric deviations do polymer gears made by 3D printing have from the theoretical geometry? From a practical point of view, the question is whether the currently installed injection-molded gear [...] Read more.
In this paper, we are looking for the answer to the following question: what geometric deviations do polymer gears made by 3D printing have from the theoretical geometry? From a practical point of view, the question is whether the currently installed injection-molded gear can be replaced by a 3D-printed gear. Thus, the measurements are also carried out on the sample gear and the comparison is made with this data as well. Knowing the data of the existing gear wheel, the CAD model was created, and based on this, samples of the gear were printed using various 3D printing machines. The printed gears were then subjected to geometrical analysis. During the inspection, we performed the measurement of the chordal thickness of the gear wheel using a gear tool caliper, instead of pin measurement and span measurement using a special micrometer, and 3D scanning and analysis. A surface roughness measurement was carried out as well. By conducting measurements on the injection-molded and 3D-printed samples, this research seeks to evaluate the reliability and limitations of the 3D-printed gears, providing insights into their industrial use. This study aims to determine whether 3D printing technologies can produce gears with sufficient accuracy and surface quality for practical applications. Based on the conducted analysis, general conclusions were drawn regarding the potential applicability of the 3D-printed gears. The experimental results indicate notable differences in dimensional accuracy between gears manufactured using Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS). In terms of chordal thickness measurements, FDM gears exhibited a mean relative error of 1.96 mm, whereas SLS gears showed a significantly higher average deviation of 5.64 mm. For the pin measurement, the relative error averaged 0.193 mm in the case of FDM gears, compared to 0.616 mm for SLS gears. Similarly, the span over four teeth measurements resulted in an average deviation of 0.153 mm for FDM gears, while SLS gears demonstrated a markedly higher mean error of 0.773 mm. With regard to surface roughness, it can be concluded that SLS-manufactured gears exhibit superior performance compared to FDM gears, with an average Ra value of 2.65 µm versus 9.28 µm, although their surface quality remains inferior to that of the injection-molded gear. In light of the higher relative errors observed in SLS gears compared to FDM gears, the dimensions of the theoretical model should be refined to improve the manufacturing accuracy of SLS-produced gears. Full article
(This article belongs to the Section Advanced Manufacturing)
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25 pages, 5050 KiB  
Article
Development of a Human-Centric Autonomous Heating, Ventilation, and Air Conditioning Control System Enhanced for Industry 5.0 Chemical Fiber Manufacturing
by Madankumar Balasubramani, Jerry Chen, Rick Chang and Jiann-Shing Shieh
Machines 2025, 13(5), 421; https://doi.org/10.3390/machines13050421 - 17 May 2025
Viewed by 204
Abstract
This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor [...] Read more.
This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R2 up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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15 pages, 3923 KiB  
Article
Systematic Optimization Study of Line-Start Synchronous Reluctance Motor Rotor for IE4 Efficiency
by Huai-cong Liu
Machines 2025, 13(5), 420; https://doi.org/10.3390/machines13050420 - 16 May 2025
Viewed by 40
Abstract
With the strengthening of international motor efficiency regulations, the new line-start synchronous reluctance motor (LS-SynRM), which does not require magnets or control units, is being studied to improve the efficiency of motors in industrial applications. However, the LS-SynRM features a complex structure with [...] Read more.
With the strengthening of international motor efficiency regulations, the new line-start synchronous reluctance motor (LS-SynRM), which does not require magnets or control units, is being studied to improve the efficiency of motors in industrial applications. However, the LS-SynRM features a complex structure with numerous design parameters, requiring the consideration of various factors such as electromagnetic performance, mechanical strength, starting capability, and ease of manufacturing. Additionally, starting capability analysis consumes a large amount of transient calculation time. The prototype stage typically comes after all simulation resources have been exhausted. The aim of this paper is to optimize the LS-SynRM by splitting the starting analysis and steady-state analysis, using a metamodel-based optimization method to quickly identify rotors of varying complexity (magnetic barriers and ribs) that meet steady-state efficiency and mechanical strength requirements. Finally, the rotor slot structure for starting is optimized within the magnetic barrier space. This approach significantly reduces the total optimization time from several weeks to just a few days. The final model obtained through the design process is analyzed using finite element analysis (FEA), and the results indicate that the target performance is achieved. To verify the FEA results, the final model is manufactured, and experiments are conducted. Full article
(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)
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18 pages, 8576 KiB  
Article
Kinematics and Dynamics Analysis of a New 5-Degrees of Freedom Parallel Mechanism with Two Double-Driven Chains
by Xingchao Zhang, Yu Rong, Hongbo Wang and Shijun Zhang
Machines 2025, 13(5), 419; https://doi.org/10.3390/machines13050419 - 15 May 2025
Viewed by 88
Abstract
This paper focuses on the design analysis of a novel 5-degrees of freedom (DOF) double-driven parallel mechanism (PM). By arranging two independent actuators on one branch chain, the mechanism can realize the five degrees of freedom of the moving platform only by relying [...] Read more.
This paper focuses on the design analysis of a novel 5-degrees of freedom (DOF) double-driven parallel mechanism (PM). By arranging two independent actuators on one branch chain, the mechanism can realize the five degrees of freedom of the moving platform only by relying on three branch chains, which have the characteristics of a compact structure and large workspace. Subsequently, the kinematic model of the mechanism is established, and the workspace, dexterity, and singularity characteristics are analyzed based on the derived model. Additionally, an explicit dynamic model of the mechanism is established based on the principle of virtual work. Finally, based on the dynamic model, the manipulability ellipsoid index and the inertial coupling strength index are proposed, and the distribution of these two kinds of dynamic performance indexes in the workspace is studied. Full article
(This article belongs to the Section Machine Design and Theory)
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28 pages, 8860 KiB  
Article
Active Torsional Vibration Suppression Strategy for Power-Split-HEV Driveline System Based on Dual-Loop Control
by Wei Zhang, Xiaocong Liang, Zhengda Han, Lei Bu, Jingang Liu, Bing Fu and Mozhang Jiang
Machines 2025, 13(5), 418; https://doi.org/10.3390/machines13050418 - 15 May 2025
Viewed by 100
Abstract
Power-split hybrid electric vehicles (power-split-HEVs) exhibit significant engine torque fluctuations due to their mechanical coupling with the driveline, leading to pronounced torsional vibration issues in the drive shaft. This study investigates an active torsional vibration suppression strategy based on drive motor control. First, [...] Read more.
Power-split hybrid electric vehicles (power-split-HEVs) exhibit significant engine torque fluctuations due to their mechanical coupling with the driveline, leading to pronounced torsional vibration issues in the drive shaft. This study investigates an active torsional vibration suppression strategy based on drive motor control. First, a dynamic model of the power-split-HEV driveline is established, and its intrinsic characteristics are analyzed. Subsequently, an engine excitation torque model is developed to identify the dominant response orders, while a vehicle dynamics model is constructed to elucidate the torsional vibration mechanisms in both hybrid and pure electric driving modes. Next, a torsional vibration feedback control framework is proposed, utilizing the electric motor as a secondary-channel torque disturbance compensator. Furthermore, a novel frequency-decoupled dual-loop control framework is proposed, with rigorous derivation of the sufficient conditions for decoupling. Based on this framework, two distinct vibration suppression algorithms are developed for the secondary-loop controller, each tailored for specific operational modes. Finally, the proposed algorithms are validated through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate a torque fluctuation suppression ratio of up to 72.2%, confirming that the active suppression algorithm effectively mitigates driveline torsional vibration induced by engine harmonic torque disturbances. Full article
(This article belongs to the Special Issue Advances in Dynamic Analysis of Multibody Mechanical Systems)
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34 pages, 14771 KiB  
Article
Research on Intelligent Planning Method for Turning Machining Process Based on Knowledge Base
by Yante Li and Tingting Zhou
Machines 2025, 13(5), 417; https://doi.org/10.3390/machines13050417 - 15 May 2025
Viewed by 142
Abstract
Against the backdrop of accelerating transformation in traditional mechanical manufacturing toward intelligent production models integrating mechanical, electronic, and information technologies, coupled with increasing demands for mass customization, conventional machining methods are proving inadequate to meet modern manufacturing requirements. To address these challenges, this [...] Read more.
Against the backdrop of accelerating transformation in traditional mechanical manufacturing toward intelligent production models integrating mechanical, electronic, and information technologies, coupled with increasing demands for mass customization, conventional machining methods are proving inadequate to meet modern manufacturing requirements. To address these challenges, this study proposes a knowledge-based intelligent process planning system. First, to address the heterogeneity issues in knowledge aggregation during machining processes, a process knowledge model comprising three sub-models was designed. Using ontological analysis methods with OWL language, inter-model relationships were formally expressed, achieving structured knowledge representation. Furthermore, to meet the system’s substantial knowledge demands, a MySQL-based knowledge framework was developed, enabling distributed storage and the intelligent retrieval of process planning knowledge. Second, to overcome limitations like low openness and decision-making rigidity in traditional process planning, a hybrid reasoning mechanism was proposed: on the one hand, an instance and rule-based reasoning system ensures adaptability to parameter variations; on the other hand, Generative Adversarial Networks are introduced to transcend the completeness limitations of traditional knowledge reasoning, enabling the dynamic evolution of process knowledge. Finally, the intelligent process planning system was implemented in Python on the VSCode platform. Validation via typical turning cases demonstrates the system’s autonomous process planning and execution capabilities. Full article
(This article belongs to the Section Advanced Manufacturing)
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23 pages, 7958 KiB  
Article
Modeling and Dynamic Characteristic Analysis of a Rigid–Flexible Coupling Multi-Stage Gear Transmission System for High-Power-Density Diesel Engines
by Chenkun Yi, Huihua Feng, Ziqing Zhu, Peirong Ren, Zhongwei Zhang and Qidi Zhou
Machines 2025, 13(5), 416; https://doi.org/10.3390/machines13050416 - 15 May 2025
Viewed by 131
Abstract
To investigate the mechanisms of unexpected failures in a multi-stage gear transmission system under a relatively low load, a rigid–flexible coupled multi-body dynamics model with 10 spur gears and 12 helical gears is established. The dynamic condensation theory is applied to improve computational [...] Read more.
To investigate the mechanisms of unexpected failures in a multi-stage gear transmission system under a relatively low load, a rigid–flexible coupled multi-body dynamics model with 10 spur gears and 12 helical gears is established. The dynamic condensation theory is applied to improve computational efficiency. The construction of this model incorporates critical nonlinear factors, ensuring high precision and reliability. Based on the proposed model, four critical dynamic parameters, including acceleration, mesh stiffness, dynamic transmission error, and vibration displacement, are analyzed. This research systematically reveals the nonlinear dynamic mechanism under the multi-gear coupling effect. The spectrum of the gears exhibits prominent low-frequency peaks at 320 Hz and 750 Hz. Notably, alternate load-dominated gears show a shift in prominent low-frequency peaks. The phenomenon of marked oscillations in mesh stiffness suggests a potential risk of localized weakening in the system’s load-carrying capacity. Critically, alternating torques induce periodic double-tooth contact regions in the gear at specific time points (0.115 s and 0.137 s), which are identified as critical factors leading to gear transmission system failures. The variation characteristics of the dynamic transmission error (DTE) demonstrate that the DTE is strongly correlated with the meshing state. The analysis of vibration displacement further indicates that the alternating external loads are the dominant excitation source of vibrations, noise, and failures in the gear transmission system. Full article
(This article belongs to the Section Machine Design and Theory)
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34 pages, 16960 KiB  
Article
Hollow-Type Integrated Assembly Design and Performance Validation of Conductive Slip Rings via Simulation-Driven Optimization
by Zhiyuan Qian, Chao Han, Nianhuan Li, Gongqiang Tian, Junye Li and Haihong Wu
Machines 2025, 13(5), 415; https://doi.org/10.3390/machines13050415 - 15 May 2025
Viewed by 103
Abstract
Conductive slip rings (CSRs) are precision components critical to industrial equipment, yet they face challenges such as unstable signal transmission, limited functionality, and difficulties in operational monitoring due to assembly-induced inaccuracies. This study proposes a hollow-type integrated assembly solution, incorporating optimized transmission, clamping, [...] Read more.
Conductive slip rings (CSRs) are precision components critical to industrial equipment, yet they face challenges such as unstable signal transmission, limited functionality, and difficulties in operational monitoring due to assembly-induced inaccuracies. This study proposes a hollow-type integrated assembly solution, incorporating optimized transmission, clamping, and protection modules through structural design and modular analysis. Static and dynamic simulations identify the optimal assembly angle and connector configuration (hollow-type outperforming flange-type), ensuring reliability and stability. A high-precision universal assembly platform is designed, and an R-axis rotary table-based testing method is developed to evaluate transmission and fixation modes. Results demonstrate the superiority of sleeve couplings and hollow connectors, with the assembled system achieving contact resistance fluctuations below 10 mΩ, angular repeatability under 500″, and accuracy within 720″, meeting all design specifications. The proposed framework combines simulation-driven design with experimental validation, offering a robust approach to enhance the performance of CSRs in industrial applications. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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20 pages, 6282 KiB  
Article
Neural-Network-Driven Intention Recognition for Enhanced Human–Robot Interaction: A Virtual-Reality-Driven Approach
by Ali Kamali Mohammadzadeh, Elnaz Alinezhad and Sara Masoud
Machines 2025, 13(5), 414; https://doi.org/10.3390/machines13050414 - 15 May 2025
Viewed by 194
Abstract
Intention recognition in Human–Robot Interaction (HRI) is critical for enabling robots to anticipate and respond to human actions effectively. This study explores the application of deep learning techniques for the classification of human intentions in HRI, utilizing data collected from Virtual Reality (VR) [...] Read more.
Intention recognition in Human–Robot Interaction (HRI) is critical for enabling robots to anticipate and respond to human actions effectively. This study explores the application of deep learning techniques for the classification of human intentions in HRI, utilizing data collected from Virtual Reality (VR) environments. By leveraging VR, a controlled and immersive space is created, where human behaviors can be closely monitored and recorded. Ensemble deep learning models, particularly Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformers, are trained on this rich dataset to recognize and predict human intentions with high accuracy. While CNN and CNN-LSTM models yielded high accuracy rates, they encountered difficulties in accurately identifying certain intentions (e.g., standing and walking). In contrast, the CNN-Transformer model outshone its counterparts, achieving near-perfect precision, recall, and F1-scores. The proposed approach demonstrates the potential for enhancing HRI by providing robots with the ability to anticipate and act on human intentions in real time, leading to more intuitive and effective collaboration between humans and robots. Experimental results highlight the effectiveness of VR as a data collection tool and the promise of deep learning in advancing intention recognition in HRI. Full article
(This article belongs to the Section Advanced Manufacturing)
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19 pages, 2256 KiB  
Article
Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection
by Yanping Zhu, Wenlong Chen, Sen Yan, Jianqiang Zhang, Chenyang Zhu, Fang Wang and Qi Chen
Machines 2025, 13(5), 413; https://doi.org/10.3390/machines13050413 - 14 May 2025
Viewed by 124
Abstract
This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. The model combines multi-scale residuals, hybrid attention mechanisms, [...] Read more.
This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. The model combines multi-scale residuals, hybrid attention mechanisms, and dual global pooling to enhance the performance. Convolutional layers efficiently extract features, while hybrid attention mechanisms strengthen the feature representation. The multi-scale residual network structure captures features at various scales, and fault classification is performed using global average and max pooling. The model was trained with the Adam optimizer and sparse categorical cross-entropy loss by incorporating a learning rate decay mechanism to refine the training process. Experiments on the University of Paderborn bearing dataset across four conditions showed that the model had superior performance, where it achieved a diagnostic accuracy of 99.7%, which surpassed traditional models, like AMCNN, LeNet5, and AlexNet. Comparative experiments on rolling bearing vibration and motor current datasets across four bearing conditions highlighted the model’s effectiveness and broad applicability in motor fault detection. Its robust feature extraction and classification capabilities make it a reliable solution for motor bearing fault diagnosis, with significant potential for real-world applications. This makes it a reliable solution for motor bearing fault diagnosis with significant potential for practical applications. Full article
(This article belongs to the Section Electrical Machines and Drives)
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19 pages, 3235 KiB  
Article
Analytical Research Regarding the Implementation of a Prescriptive Maintenance System Applied to IT&C Equipment
by Ana-Diana Pop-Suărășan, Nicolae Stelian Ungureanu and Adrian Petrovan
Machines 2025, 13(5), 412; https://doi.org/10.3390/machines13050412 - 14 May 2025
Viewed by 174
Abstract
In the era of digitalization, prescriptive maintenance and equipment condition monitoring are essential for ensuring the continuity of operations in various industries. The proposed software solution provides an integrated solution for monitoring and maintaining equipment, facilitating the collection, processing and interpretation of data [...] Read more.
In the era of digitalization, prescriptive maintenance and equipment condition monitoring are essential for ensuring the continuity of operations in various industries. The proposed software solution provides an integrated solution for monitoring and maintaining equipment, facilitating the collection, processing and interpretation of data related to their performance. Prioritization of prescriptive maintenance recommendations for alerts within the application is based on a set of well-defined criteria that integrate both the analysis of physical sensor data and computer logs. This is achieved through a hierarchical classification mechanism, combining predefined thresholds, prescriptive assessments and severity levels. The proposed project is a prescriptive equipment monitoring and maintenance application focused on the collection and analysis of sensor data to identify and prevent potential failures. The application structure integrates components such as data processing, machine learning, databases and recommendation generation, each of which has a specific role in the project workflow. The integration of sensor data and logs enhances robustness, scalability and efficiency, ensuring both high performance and adaptability to the varying needs of users. The proposed system processes and analyzes large data volumes. It combines threshold-based logic with machine learning to enhance adaptability. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 7737 KiB  
Article
Battery Electric Vehicles: A Study on State of Charge and Cost-Effective Solutions for Addressing Range Anxiety
by Jason Pollock, Perk Lin Chong, Manu Ramegowda, Nashwan Dawood, Hossein Habibi, Zhonglan Hou, Foad Faraji and Pengyan Guo
Machines 2025, 13(5), 411; https://doi.org/10.3390/machines13050411 - 14 May 2025
Viewed by 179
Abstract
While Battery Electric Vehicles (BEVs) offer environmental benefits by reducing carbon emissions during use, their range remains limited compared to conventionally fuelled vehicles. This paper focuses on identifying factors that directly influence BEV range and explores strategies to mitigate range anxiety among potential [...] Read more.
While Battery Electric Vehicles (BEVs) offer environmental benefits by reducing carbon emissions during use, their range remains limited compared to conventionally fuelled vehicles. This paper focuses on identifying factors that directly influence BEV range and explores strategies to mitigate range anxiety among potential users. Specifically, it reviews the impact of battery cell characteristics and vehicle lightweighting. Using the WLTP Class 3B drive cycle, energy consumption and Depth of Discharge (DoD) were evaluated across various battery capacities. Multiple Lithium-Ion battery models were simulated to analyse discharge behaviour, while vehicle mass composition was examined to assess the effectiveness of lightweighting in extending driving range. A lower initial State of Charge (SoC) and a standard discharge rate were used to estimate the remaining range, highlighting an approximate gain of up to 6 km at lower DoD levels. This work aims to accurately demonstrate how battery technology and structural weight impact energy consumption and usable range in BEVs. Current modelling approaches often overlook the relationship between driver discomfort and battery performance metrics. The main contribution is to address the gap by integrating Li-ion discharge modelling with vehicle dynamics to estimate range and compare cell characteristics. The ultimate goal is to support cost-effective strategies for increasing BEV usability, aligning them more closely with conventional vehicle expectations and enhancing journey flexibility. Full article
(This article belongs to the Special Issue Advances in Vehicle Dynamics)
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22 pages, 4842 KiB  
Article
Research on the Multi-Objective Optimal Design of Adjusting Mechanisms Considering Force Transmission Performance
by Qi Yang, Mingxin Shan, Yangli Tian, Boyang Guan, Jingyu Zhai and Wei Sun
Machines 2025, 13(5), 410; https://doi.org/10.3390/machines13050410 - 14 May 2025
Viewed by 95
Abstract
For the guide vane adjusting mechanism, precision represents the primary design requirement. Meanwhile, due to the presence of aerodynamic loads under actual operating conditions, stagnation forces emerge that affect the mechanism motion characteristics, including the response speed and precision. This paper establishes kinematic [...] Read more.
For the guide vane adjusting mechanism, precision represents the primary design requirement. Meanwhile, due to the presence of aerodynamic loads under actual operating conditions, stagnation forces emerge that affect the mechanism motion characteristics, including the response speed and precision. This paper establishes kinematic and static analysis models of the guide vane adjusting mechanism through analytical modeling methods, investigates analytical approaches for mechanism adjustment precision and stagnation force, and conducts error and sensitivity analyses of the mechanism parameters based on these analytical models. Building upon this foundation, an optimization design method integrating adjustment precision and force transmission performance is proposed using a multi-objective genetic algorithm. Optimizing the critical design parameters, such as the mechanism dimensions and positions, can enhance both the adjustment precision and force transmission performance. Through case studies, significant reductions in motion precision errors and the peak stagnation force and maximum differences in stagnation force were achieved, validating the feasibility of this optimization design approach. Full article
(This article belongs to the Special Issue Dynamic Performance Analysis and Control of Engines for Aerospace)
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21 pages, 7387 KiB  
Article
Transient High-Frequency Electromagnetic Force Calculation for Linear Induction Motors Under Pulse Width Modulation Current Excitation
by Mingke Li, Jin Xu, Junjie Zhu, Yuhu Wang and Tairan Chen
Machines 2025, 13(5), 409; https://doi.org/10.3390/machines13050409 - 14 May 2025
Viewed by 146
Abstract
Because of their transient working mode and end effects, it is particularly difficult to compute high-frequency electromagnetic forces on linear induction motors under PWM current simulation. The current methods for computing high-frequency electromagnetic forces in transient operating conditions are computationally expensive and have [...] Read more.
Because of their transient working mode and end effects, it is particularly difficult to compute high-frequency electromagnetic forces on linear induction motors under PWM current simulation. The current methods for computing high-frequency electromagnetic forces in transient operating conditions are computationally expensive and have limited practicality. To deal with these issues, this paper introduces a non-periodic transient high-frequency electromagnetic force calculation model. Firstly, an examination of a linear induction motor under PWM currents demonstrates that the transient magnetic field calculation issue in a linear induction motor can be simplified to a periodic boundary steady-state magnetic field calculation problem. Based on this, a 2D magnetic field analytical model is established for high-frequency magnetic field calculation. Subsequently, a hybrid approach employing both finite element analysis and analytical methods is employed to compute the transient magnetic field. Finally, electromagnetic forces are calculated across the entire frequency spectrum, and the correctness of the model is validated indirectly through motor vibration experiments. This model offers faster and more accurate results than finite element analysis, making it suitable for application in the iterative stages of motor optimization design and applicable to rotary induction motors. Full article
(This article belongs to the Section Electrical Machines and Drives)
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16 pages, 7130 KiB  
Article
Inverter-Fed Motor Stator Insulation System and Partial Discharge-Free Design: Can We Refer to Measurements Under AC Sinusoidal Voltage?
by Gian Carlo Montanari, Muhammad Shafiq, Sukesh Babu Myneni and Zhaowen Chen
Machines 2025, 13(5), 408; https://doi.org/10.3390/machines13050408 - 14 May 2025
Viewed by 205
Abstract
In light of the large and fast-growing use of power electronics in electrical generation, distribution and utilization systems, and with the focus on electrified transportation, evaluating the significance of testing insulation systems for design and quality control under AC sinusoidal or power electronics [...] Read more.
In light of the large and fast-growing use of power electronics in electrical generation, distribution and utilization systems, and with the focus on electrified transportation, evaluating the significance of testing insulation systems for design and quality control under AC sinusoidal or power electronics waveforms is a due knowledge step. This paper has a twofold aim. One is presenting a procedure for the comparison between two insulation system solutions for partial discharge, PD, free design, referring to motorettes of a MV speed-controlled motor. The other is to carry out an evaluation of the most effective testing waveform, from AC sinusoidal to AC modulated (PWM), varying the number of inverter levels and switching the slew rate. It is shown that AC sinusoidal is effective for a qualitative evaluation of insulation system design as regards partial discharge risk, but PD inception voltage can be significantly dependent on supply voltage waveforms. Hence, if quantitative estimation of partial discharge inception voltage is requested, for design and quality control purposes, PWM waveforms as close as possible to those planned under operation should be used. Full article
(This article belongs to the Special Issue New Trends in Reliability and Lifetime Improvement in Power-Electronic-Controlled Machines and Devices)
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23 pages, 7292 KiB  
Article
Feasibility Assessment of Novel Multi-Mode Camshaft Design Through Modal Analysis
by Merve Üngör, Osman Taha Sen, Akif Yavuz and Cemal Baykara
Machines 2025, 13(5), 407; https://doi.org/10.3390/machines13050407 - 14 May 2025
Viewed by 170
Abstract
Camshafts in internal combustion engines are critical components not only for mechanical functionality but also for dynamic performance and vibration characteristics. This study aims to present a comprehensive modal analysis of the camshaft by integrating both computational and experimental approaches, followed by the [...] Read more.
Camshafts in internal combustion engines are critical components not only for mechanical functionality but also for dynamic performance and vibration characteristics. This study aims to present a comprehensive modal analysis of the camshaft by integrating both computational and experimental approaches, followed by the design of an innovative multi-mode camshaft mechanism that enhances fuel efficiency and optimizes engine performance. First, a computational model of the camshaft is built with a proper mesh structure with a mesh size of 2 mm. Modal analysis is performed on the computational model, and the critical modal parameters of the camshaft are obtained. Second, modal tests are performed on the camshaft, which reveal an error of 15% on the computationally predicted natural frequencies. Third, a model updating procedure is applied to improve the accuracy of the computational model. The critical material properties are determined based on a sensitivity analysis, and the structural optimization process is performed accordingly. The optimized model solution is compared to the experimental data, and the computational model is validated based on both natural frequencies and mode shapes. The comparison of experimentally and computationally estimated natural frequencies reveal a difference below 2%. Mode shapes are compared based on Modal Assurance Criteria (MAC), and it is determined that the values of the elements in the main diagonal of the MAC matrix are around 0.9. Finally, the validated model is used as a basis for an innovative camshaft mechanism. The proposed mechanism offers enhanced flexibility by integrating multiple valve actuation methods, including Variable Valve Timing (VVT), Variable Valve Lift (VVL), Variable Valve Duration (VVD), Cylinder Deactivation, and Skip Cycle methods into a single camshaft for the first time in the literature. Modal analysis is performed on the proposed multi-mode design, and it is observed that the modified design resembles the modal properties of the original design. Full article
(This article belongs to the Section Vehicle Engineering)
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17 pages, 10262 KiB  
Article
Structural Design and Random Vibration Analysis of a High-Stability Momentum Wheel
by Yukuan Xie, Yuan Xue, Hongxiang Wang and Yinjin Sun
Machines 2025, 13(5), 406; https://doi.org/10.3390/machines13050406 - 13 May 2025
Viewed by 200
Abstract
A novel expandable wheel body assembly is designed in this paper. The wheel body assembly utilizes the elastic deformation of the hub to clamp the bearing assembly. The gaps between them are effectively eliminated by this structure, improving the rotational precision and operational [...] Read more.
A novel expandable wheel body assembly is designed in this paper. The wheel body assembly utilizes the elastic deformation of the hub to clamp the bearing assembly. The gaps between them are effectively eliminated by this structure, improving the rotational precision and operational stability of the momentum wheel. A finite element model of the momentum wheel was established, and stress analysis was conducted using finite element analysis (FEA) software ANSYS 2025R1 to validate its mechanical integrity. The results demonstrate that the designed momentum wheel meets the required strength specifications. Additionally, the random vibration characteristics of the momentum wheel in a space environment were analyzed using ANSYS simulations, and a corresponding random vibration test was conducted. The good agreement between simulation and experimental results validates the reliability of the finite element model. The results indicate that this new type of momentum wheel can work reliably in aerospace conditions. Full article
(This article belongs to the Section Machine Design and Theory)
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