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Volume 13
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Machines, Volume 13, Issue 3 (March 2025) – 86 articles

Cover Story (view full-size image): Robots play a crucial role in the nuclear industry, where human involvement is limited. However, the current nuclear robots lack versatility due to limited autonomy and high costs. This research focuses on transforming the teleoperated Dexter robot into an autonomous manipulator for sorting and segregating nuclear materials. By developing a kinematic model and using convex optimization-based dynamic model identification, Dexter can be controlled autonomously via the Robot Operating System. The integration of vision, AI-based grasp generation, and intelligent radiological surveying enhances Dexter's performance. The framework's efficacy is demonstrated on a mock-up nuclear waste test bed, highlighting its potential in regulated domains, such as the nuclear industry. View this paper
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23 pages, 7045 KiB  
Article
Optimizing Text Recognition in Mechanical Drawings: A Comprehensive Approach
by Javier Villena Toro and Mehdi Tarkian
Machines 2025, 13(3), 254; https://doi.org/10.3390/machines13030254 - 20 Mar 2025
Viewed by 380
Abstract
The digitalization of engineering drawings is a pivotal step toward automating and improving the efficiency of product design and manufacturing systems (PDMSs). This study presents eDOCr2, a framework that combines traditional OCR and image processing to extract structured information from mechanical drawings. It [...] Read more.
The digitalization of engineering drawings is a pivotal step toward automating and improving the efficiency of product design and manufacturing systems (PDMSs). This study presents eDOCr2, a framework that combines traditional OCR and image processing to extract structured information from mechanical drawings. It segments drawings into key elements—such as information blocks, dimensions, and feature control frames—achieving a text recall of 93.75% and a character error rate (CER) below 1% in a benchmark with drawings from different sources. To improve semantic understanding and reasoning, eDOCr2 integrates Vision Language models (Qwen2-VL-7B and GPT-4o) after segmentation to verify, filter, or retrieve information. This integration enables PDMS applications such as automated design validation, quality control, or manufacturing assessment. The code is available on Github. Full article
(This article belongs to the Special Issue Empowering Design and Production Automation with Data-Driven and Machine Learning Approaches)
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16 pages, 3368 KiB  
Article
The Optimized Design and Principal Analysis of a Toe-End Sliding Sleeve
by Wei Li, Fulu Chen, Mengyu Cao, Huan Zhao, Wangluo Ning, Tianchi Ma and Mingxiu Zhang
Machines 2025, 13(3), 253; https://doi.org/10.3390/machines13030253 - 20 Mar 2025
Viewed by 236
Abstract
Through hydraulic control principles, numerical simulation and indoor testing, the opening principle of a toe-end sliding sleeve with a time delay mechanism is explained. Conventional toe-end sliding sleeve in shale oil wells have problems with premature opening and a failure to open, which [...] Read more.
Through hydraulic control principles, numerical simulation and indoor testing, the opening principle of a toe-end sliding sleeve with a time delay mechanism is explained. Conventional toe-end sliding sleeve in shale oil wells have problems with premature opening and a failure to open, which means they cannot ensure the whole-well pressure test process and can cause serious economic losses to the oil and gas industry. In order to solve the above problems, a new type of optimal design for toe-end sliding sleeve with a 30 min delayed opening is proposed. In this paper, based on the principle of hydraulic flow, ABAQUS 2022 numerical simulation software was used to study the influence of different states and the same hydraulic pressure on its internal stress–strain value. A qualitative study of the delayed-opening function was carried out using a pressurized pump unit. In addition, principle tests under different operating parameters were designed to quantitatively analyze the pin shear situation and the delayed opening time of the toe-end sliding sleeve when the tool was fitted with different numbers of pins and when the delay valve was fitted. In addition, the simulation results of the hydraulic fluid’s flow inside the time delay mechanism with different nozzle diameters were compared with the theoretical values, which showed that the hydraulic fluid’s flow rate inside the mechanism increased with the enlargement of the nozzle diameter, and the optimal nozzle diameter was 0.56 mm. The indoor test showed that when the tool was retrofitted with a time delay mechanism, installing six pins was the optimal combination. The field application of the slip-on was able to satisfy an opening time delay of 28.3 with a relative error of only 5.67%. These results complement the research on toe-end sliding sleeve and provide ideas for the optimization of toe-end slipcovers incorporating a time delay mechanism. Full article
(This article belongs to the Special Issue Design Methodology for Soft Mechanisms, Machines, and Robots)
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16 pages, 6873 KiB  
Article
Size Effect on Energy Characteristics of Axial Flow Pump Based on Entropy Production Theory
by Hongliang Wang, Xiaofeng Wu, Xiao Xu, Suhao Bian and Fan Meng
Machines 2025, 13(3), 252; https://doi.org/10.3390/machines13030252 - 20 Mar 2025
Viewed by 223
Abstract
To investigate the size effect on the energy characteristics of axial flow pumps, this study scaled the original model size based on the head similarity principle, resulting in four size schemes (Schemes 2–4 correspond to 3, 5, and 10 times the size of [...] Read more.
To investigate the size effect on the energy characteristics of axial flow pumps, this study scaled the original model size based on the head similarity principle, resulting in four size schemes (Schemes 2–4 correspond to 3, 5, and 10 times the size of Scheme 1, respectively). By solving the unsteady Reynolds-averaged Navier–Stokes (URANS) equations with the Shear Stress Transport (SST) k-omega turbulence model, the external characteristic parameters and internal flow field structures were predicted. Additionally, the spatial distribution of internal hydraulic losses was analyzed using entropy generation theory. The results revealed three key findings: (1) the efficiency of axial flow pumps significantly improves with increasing size ratio, with Scheme 4 exhibiting a 6.1% efficiency increase compared to Scheme 1; (2) as the size ratio increases, the entropy production coefficients of all hydraulic components decrease, with the impeller and guide vanes in Scheme 4 showing reductions of 55.1% and 56.5%, respectively, compared to Scheme 1; (3) the high entropy generation coefficient regions in the impeller and guide vanes are primarily concentrated near the rim, with their area decreasing as the size ratio increases. Specifically, the entropy production coefficients at the rim of impeller and guide vanes in Scheme 4 decreased by 84.85% and 58.2%, respectively, compared to Scheme 1. These findings provide valuable insights for the selection and optimization of axial flow pumps in applications such as cross-regional water transfer, agricultural irrigation, and urban drainage systems. Full article
(This article belongs to the Section Turbomachinery)
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14 pages, 863 KiB  
Article
Surface Classification from Robot Internal Measurement Unit Time-Series Data Using Cascaded and Parallel Deep Learning Fusion Models
by Ghaith Al-refai, Dina Karasneh, Hisham Elmoaqet, Mutaz Ryalat and Natheer Almtireen
Machines 2025, 13(3), 251; https://doi.org/10.3390/machines13030251 - 20 Mar 2025
Viewed by 313
Abstract
Surface classification is critical for ground robots operating in diverse environments, as it improves mobility, stability, and adaptability. This study introduces IMU-based deep learning models for surface classification as a low-cost alternative to computer vision systems. Two feature fusion models were introduced to [...] Read more.
Surface classification is critical for ground robots operating in diverse environments, as it improves mobility, stability, and adaptability. This study introduces IMU-based deep learning models for surface classification as a low-cost alternative to computer vision systems. Two feature fusion models were introduced to classify the surface type using time-series data from an IMU sensor mounted on a ground robot. The first model, a cascaded fusion model, employs a 1-D Convolutional Neural Network (CNN) followed by a Long Short-Term Memory (LSTM) network and then a multi-head attention mechanism. The second model is a parallel fusion model, which processes sensor data through both a CNN and an LSTM simultaneously before concatenating the resulting feature vectors and then passing them to a multi-head attention mechanism. Both models utilize a multi-head attention mechanism to enhance focus on relevant segments of the time-sequence data. The models were trained on a normalized Internal Measurement Unit (IMU) dataset, with hyperparameter tuning achieved via grid search for optimal performance. Results showed that the cascaded model achieved higher accuracy metrics, including a mean Average Precision (mAP) of 0.721 compared to 0.693 for the parallel model. However, the cascaded model incurred a 44.37% increase in processing time, which makes the parallel fusion model more suitable for real-time applications. The multi-head attention mechanism contributed significantly to accuracy improvements, particularly in the cascaded model. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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17 pages, 3912 KiB  
Article
A Novel Compliant Four-Bar Mechanism-Based Universal Joint Design and Production
by Raşit Karakuş
Machines 2025, 13(3), 250; https://doi.org/10.3390/machines13030250 - 20 Mar 2025
Viewed by 260
Abstract
In this study, a novel fully compliant four-bar-based universal joint is introduced. The difference between the angular positions of the input and output shafts is obtained by two equivalent fully compliant four-bar mechanisms that operate simultaneously by sharing the same input link. During [...] Read more.
In this study, a novel fully compliant four-bar-based universal joint is introduced. The difference between the angular positions of the input and output shafts is obtained by two equivalent fully compliant four-bar mechanisms that operate simultaneously by sharing the same input link. During the design phase of the mechanism an iterative method for determining the optimum angular position of the links is proposed and applied. The proposed design is a single-piece mechanism that is produced from polypropylene and compatible with both additive manufacturing and injection molding techniques. The scalability of compliant mechanisms allows for a wide range of size options during the design process. An extensive survey of the current literature reveals that the design proposed is without precedent, marking it as both novel and inventive. In this study, the design procedure of the proposed universal joint, stress analysis of the links, the torque capacity of the joint, and an experimental setup are presented. The produced prototype demonstrates the functionality of the proposed design. In addition, it should be noted that the prototype production of the proposed design was conducted using the additive manufacturing method. This production technique is a significant motivation behind the design of the mechanism as a single piece. Additionally, the proposed mechanism in its current form is also suitable for production using the injection molding method which is widely used in the industry. Full article
(This article belongs to the Special Issue Optimization and Design of Compliant Mechanisms)
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21 pages, 3836 KiB  
Review
Current Trends in Monitoring and Analysis of Tool Wear and Delamination in Wood-Based Panels Drilling
by Tomasz Trzepieciński, Krzysztof Szwajka, Joanna Zielińska-Szwajka and Marek Szewczyk
Machines 2025, 13(3), 249; https://doi.org/10.3390/machines13030249 - 20 Mar 2025
Viewed by 310
Abstract
Wood-based panels (WBPs) have versatile structural applications and are a suitable alternative to plastic panels and metallic materials. They have appropriate strength parameters that provide the required stiffness and strength for furniture products and construction applications. WBPs are usually processed by cutting, milling [...] Read more.
Wood-based panels (WBPs) have versatile structural applications and are a suitable alternative to plastic panels and metallic materials. They have appropriate strength parameters that provide the required stiffness and strength for furniture products and construction applications. WBPs are usually processed by cutting, milling and drilling. Especially in the furniture industry, the accuracy of processing is crucial for aesthetic reasons. Ensuring the WBP surface’s high quality in the production cycle is associated with the appropriate selection of processing parameters and tools adapted to the specificity of the processed material (properties of wood, glue, type of resin and possible contamination). Therefore, expert assessment of the durability of WBPs is difficult. The interest in the automatic monitoring of cutting tools in sustainable production, according to the concept of Industry 4.0, is constantly growing. The use of flexible automation in the machining of WBPs is related to the provision of tools monitoring the state of tool wear and surface quality. Drilling is the most common machining process that prepares panels for assembly operations and directly affects the surface quality of holes and the aesthetic appearance of products. This paper aimed to synthesize research findings across Medium-Density Fiberboards (MDFs), particleboards and oriented strand boards (OSBs), highlighting the impact of processing parameters and identifying areas for future investigation. This article presents the research trend in the adoption of the new general methodological assumptions that allow one to define both the drill condition and delamination monitoring in the drilling of the most commonly used wood-based boards, i.e., particleboards, MDFs and OSBs. Full article
(This article belongs to the Special Issue Tool Wear in Machining, 2nd Edition)
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21 pages, 2978 KiB  
Article
A Multi-Strategy Optimized Framework for Health Status Assessment of Air Compressors
by Dali Hou and Xiaoran Wang
Machines 2025, 13(3), 248; https://doi.org/10.3390/machines13030248 - 20 Mar 2025
Viewed by 264
Abstract
Air compressors play a crucial role in industrial production, and accurately assessing their health status is vital for ensuring stable operation. The field of health status assessment has made significant progress; however, challenges such as dataset class imbalance, feature selection, and accuracy improvement [...] Read more.
Air compressors play a crucial role in industrial production, and accurately assessing their health status is vital for ensuring stable operation. The field of health status assessment has made significant progress; however, challenges such as dataset class imbalance, feature selection, and accuracy improvement remain and require further refinement. To address these issues, this paper proposes a novel algorithm based on multi-strategy optimization, using air compressors as the research subject. During data preprocessing, the Synthetic Minority Over-sampling Technique (SMOTE) is introduced to effectively balance class distribution. By integrating the Squeeze-and-Excitation (SE) mechanism with Convolutional Neural Networks (CNNs), key features within the dataset are extracted and emphasized, reducing the impact of irrelevant features on model efficiency. Finally, Bidirectional Long Short-Term Memory (BiLSTM) networks are employed for health status assessment and classification of the air compressor. The Ivy algorithm (IVYA) is introduced to optimize the BiLSTM’s hyperparameters to improve classification accuracy and avoid local optima. Through comparative and ablation experiments, the effectiveness of the proposed SMOTE-IVY-SE-CNN-BiLSTM model is validated, demonstrating its ability to significantly enhance the accuracy of air compressor health status assessment. Full article
(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)
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29 pages, 5686 KiB  
Article
GPTArm: An Autonomous Task Planning Manipulator Grasping System Based on Vision–Language Models
by Jiaqi Zhang, Zinan Wang, Jiaxin Lai and Hongfei Wang
Machines 2025, 13(3), 247; https://doi.org/10.3390/machines13030247 - 19 Mar 2025
Viewed by 420
Abstract
The integration of vision–language models (VLMs) with robotic systems represents a transformative advancement in autonomous task planning and execution. However, traditional robotic arms relying on pre-programmed instructions exhibit limited adaptability in dynamic environments and face semantic gaps between perception and execution, hindering their [...] Read more.
The integration of vision–language models (VLMs) with robotic systems represents a transformative advancement in autonomous task planning and execution. However, traditional robotic arms relying on pre-programmed instructions exhibit limited adaptability in dynamic environments and face semantic gaps between perception and execution, hindering their ability to handle complex task demands. This paper introduces GPTArm, an environment-aware robotic arm system driven by GPT-4V, designed to overcome these challenges through hierarchical task decomposition, closed-loop error recovery, and multimodal interaction. The proposed robotic task processing framework (RTPF) integrates real-time visual perception, contextual reasoning, and autonomous strategy planning, enabling robotic arms to interpret natural language commands, decompose user-defined tasks into executable subtasks, and dynamically recover from errors. Experimental evaluations across ten manipulation tasks demonstrate GPTArm’s superior performance, achieving a success rate of up to 91.4% in standardized benchmarks and robust generalization to unseen objects. Leveraging GPT-4V’s reasoning and YOLOv10’s precise small-object localization, the system surpasses existing methods in accuracy and adaptability. Furthermore, GPTArm supports flexible natural language interaction via voice and text, significantly enhancing user experience in human–robot collaboration. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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38 pages, 5185 KiB  
Review
Review of Agricultural Machinery Seat Semi-Active Suspension Systems for Ride Comfort
by Xiaoliang Chen, Zhelu Wang, Haoyou Shi, Nannan Jiang, Sixia Zhao, Yiqing Qiu and Qing Liu
Machines 2025, 13(3), 246; https://doi.org/10.3390/machines13030246 - 18 Mar 2025
Viewed by 292
Abstract
This paper systematically reviews research progress in semi-active suspension systems for agricultural machinery seats, focusing on key technologies and methods to enhance ride comfort. First, through an analysis of the comfort evaluation indicators and constraints of seat suspension systems, the current applications of [...] Read more.
This paper systematically reviews research progress in semi-active suspension systems for agricultural machinery seats, focusing on key technologies and methods to enhance ride comfort. First, through an analysis of the comfort evaluation indicators and constraints of seat suspension systems, the current applications of variable stiffness and damping components, as well as semi-active control technologies, are outlined. Second, a comparative analysis of single control methods (such as PID control, fuzzy control, and sliding mode control) and composite control methods (such as fuzzy PID control, intelligent algorithm-based integrated control, and fuzzy sliding mode control) is conducted, with control mechanisms explained using principle block diagrams. Furthermore, key technical challenges in current research are summarized, including dynamic characteristic optimization design, adaptability to complex operating environments, and the robustness of control algorithms. Further research could explore the refinement of composite control strategies, the integrated application of intelligent materials, and the development of intelligent vibration damping technologies. This paper provides theoretical references for the optimization design and engineering practice of agricultural machinery suspension systems. Full article
(This article belongs to the Special Issue Mastering Vibrations: The Latest Breakthroughs in Control for Mechanical Systems)
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29 pages, 32061 KiB  
Article
Dynamic Characteristics Analysis and Optimization Design of Two-Stage Helix Planetary Reducer for Robots
by Wenzhao Lin, Dongdong Chang, Hao Li, Junhua Chen and Fangping Huang
Machines 2025, 13(3), 245; https://doi.org/10.3390/machines13030245 - 18 Mar 2025
Viewed by 172
Abstract
The dynamic characteristics of high-precision planetary reducers in terms of vibration response and dynamic transmission error have a significant impact on positioning accuracy and service life. However, the dynamics of high-precision two-stage helical planetary reducers have not been studied extensively enough and must [...] Read more.
The dynamic characteristics of high-precision planetary reducers in terms of vibration response and dynamic transmission error have a significant impact on positioning accuracy and service life. However, the dynamics of high-precision two-stage helical planetary reducers have not been studied extensively enough and must be studied in depth. In this paper, the dynamic characteristics of the high-precision two-stage helical planetary reducer are investigated in combination with simulation tests, and the microscopic modification of the gears is optimized by the helix modification with drums, with the objective of reducing the vibration response and dynamic transmission error. Considering the time-varying meshing stiffness of gears and transmission errors, a translation–torsion coupled dynamics model of a two-stage helical planetary gear drive is established based on the Lagrange equations by using the centralized parameter method for analyzing the dynamic characteristics of the reducer. The differential equations of the system were derived by analyzing the relative displacement relationship between the components. On this basis, a finite element model of a certain type of high-precision reducer was established, and factors such as rotate speed and load were investigated through simulation and experimental comparison to quantify or characterize their effects on the dynamic behavior and transmission accuracy. Based on the combined modification method of helix modification with drum shape, the optimized design of this type of reducer is carried out, and the dynamic characteristics of the reducer before and after modification are compared and analyzed. The results show that the adopted modification optimization method is effective in reducing the vibration amplitude and transmission error amplitude of the reducer. The peak-to-peak value of transmission error of the reducer is reduced by 19.87%; the peak value of vibration acceleration is reduced by 14.29%; and the RMS value is reduced by 21.05% under the input speed of 500 r/min and the load of 50 N·m. The research results can provide a theoretical basis for the study of dynamic characteristics, fault diagnosis, optimization of meshing parameters, and structural optimization of planetary reducers. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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21 pages, 8619 KiB  
Article
Crone Ground Hook Suspension
by Fouad Farah, Xavier Moreau and Roy Abi Zeid Daou
Machines 2025, 13(3), 244; https://doi.org/10.3390/machines13030244 - 18 Mar 2025
Viewed by 245
Abstract
The work presented in this paper is to be read within the context of a connected autonomous vehicle (CAV). This context makes it possible to consider dividing the overall operational domain (operational design domain: ODD) of the vehicle into three sub-domains, relating to [...] Read more.
The work presented in this paper is to be read within the context of a connected autonomous vehicle (CAV). This context makes it possible to consider dividing the overall operational domain (operational design domain: ODD) of the vehicle into three sub-domains, relating to the areas of comfort (ODD1), road-holding (ODD2), and emergency situations (ODD3). Thus, based on information from the CAV’s proprioceptive and exteroceptive sensors, in addition to information from the infrastructure and other vehicles, supervision makes it possible, at any time, to identify the ODD in which the vehicle is located and to propose the most appropriate strategy, particularly for suspension control. Work already carried out by the authors made it possible to determine a crone sky hook (CSH) strategy for suspension control, 100% comfort-oriented for ODD1, a mixed crone sky hook—crone ground hook (CSH-CGH) strategy, oriented towards road-holding for ODD2, and a CGH strategy oriented towards safety for ODD3. In this paper, a comparative study focusing on security (ODD3) is presented. It concerns two versions of the CGH strategy (nominal CGHN and generalized CGHG). More precisely, for the comparative study to be meaningful, the control loops of the two versions have the same speed (iso-speed constraint), and the performance indices are normalized with respect to the values obtained in fault mode when the actuator is faulty. Notably, the CGHG version is part of the dynamics of fractional systems. Full article
(This article belongs to the Section Vehicle Engineering)
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16 pages, 8013 KiB  
Article
Development and Validation of a Large Strain Flow Curve Model for High-Silicon Steel to Predict Roll Forces in Cold Rolling
by Yong-Hoon Roh, Dongyun Lee, Seok-Eui Lee, Seong-Gi Kim and Youngseog Lee
Machines 2025, 13(3), 243; https://doi.org/10.3390/machines13030243 - 17 Mar 2025
Viewed by 185
Abstract
Accurately modeling the flow curve over a large strain range is crucial for predicting the flow stress behavior of high silicon steel undergoing strain hardening in the continuous cold rolling process. This study proposes a large strain flow curve model for high-silicon steel, [...] Read more.
Accurately modeling the flow curve over a large strain range is crucial for predicting the flow stress behavior of high silicon steel undergoing strain hardening in the continuous cold rolling process. This study proposes a large strain flow curve model for high-silicon steel, a material commonly used in the cores of electromagnetic devices such as electric motors, generators, and transformers. This model was developed through a series of tensile tests on homogenously pre-strained specimens. Pilot cold rolling was performed at various thickness reduction ratios to impart different magnitudes of pre-strain to sheet-type tensile specimens. The proposed flow curve model was implemented in a VUHARD user-defined subroutine within Abaqus/Explicit, and the predicted roll separating forces were compared with those measured from the pilot cold rolling tests. The comparison demonstrated that the proposed flow curve model accurately captures the flow stress behavior of high-silicon steel at different strain rates over a large strain range, with an R-squared value of 0.9932. The predicted roll separating forces closely matched the measurements from the pilot cold rolling tests, with an average difference of 5.1%. Full article
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27 pages, 3508 KiB  
Article
A Bayesian FMEA-Based Method for Critical Fault Identification in Stacker-Automated Stereoscopic Warehouses
by Xinyue Ma and Mengyao Gu
Machines 2025, 13(3), 242; https://doi.org/10.3390/machines13030242 - 17 Mar 2025
Viewed by 191
Abstract
This study proposes a Bayesian failure mode and effects analysis (FMEA)-based method for identifying critical faults and guiding maintenance decisions in stacker-automated stereoscopic warehouses, addressing the limited research on whole-machine systems and the interactions among fault modes. First, the hesitant fuzzy evaluation method [...] Read more.
This study proposes a Bayesian failure mode and effects analysis (FMEA)-based method for identifying critical faults and guiding maintenance decisions in stacker-automated stereoscopic warehouses, addressing the limited research on whole-machine systems and the interactions among fault modes. First, the hesitant fuzzy evaluation method was utilized to assess the influences of risk factors and fault modes in a stacker-automated stereoscopic warehouse. A hesitant fuzzy design structure matrix (DSM) was then constructed to quantify their interaction strengths. Second, leveraging the interaction strengths and causal relationships between severity, detection, risk factors, and fault modes, a Bayesian network model was developed to compute the probabilities of fault modes under varying severity and detection levels. FMEA was subsequently applied to evaluate fault risks based on severity and detection scores. Following this, fault risk ranking was conducted to identify critical fault modes and formulate targeted maintenance strategies. The proposed method was validated through a case study of Company A’s stacker-automated stereoscopic warehouse. The results demonstrate that the proposed approach can more objectively identify critical fault modes and develop more precise maintenance strategies. Furthermore, the Bayesian FMEA method provides a more objective and accurate reflection of fault risk rankings. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 8195 KiB  
Article
Cage Strength Analysis and Improvement of High-Speed Deep Groove Ball Bearings
by Wenhu Zhang, Shengjie Du, Heng Tian and Li Huang
Machines 2025, 13(3), 241; https://doi.org/10.3390/machines13030241 - 17 Mar 2025
Viewed by 301
Abstract
The cage strength is a critical factor that constrains performance of high-speed deep groove ball bearing (DGBB) used in the drive motor of new energy vehicles. This paper presents a rigid-flexible coupling dynamic model for high-speed DGBBs, based on interactions dynamic of the [...] Read more.
The cage strength is a critical factor that constrains performance of high-speed deep groove ball bearing (DGBB) used in the drive motor of new energy vehicles. This paper presents a rigid-flexible coupling dynamic model for high-speed DGBBs, based on interactions dynamic of the flexible crown cage, balls, and rings. This study systematically analyzed the cage weaknesses in strength, and explored how factors such as the pocket clearance, claw length, modification radius and bottom thickness influence cage strength. In addition, an improved design aimed at enhancing cage strength was proposed. The results indicate that the cage strength is more sensitive to the inner-ring speed. Particularly, both the maximum stress and deformation in the radial direction increase sharply when the speed exceeds a threshold of 18,000 r/min. Additionally, an increase in the bearing rotational acceleration leads to a 45.7% rise in the cage stress. Furthermore, the sensitivity of the cage strength to temperature also escalates with bearing speed; the maximum stress and deformation increase by 5% to 16% at 80 °C compared to that obtained at 25 °C. Based on the structural influence on the cage strength, a structural improvement is proposed. With a pocket clearance of 0.23 mm, a claw length of 2.3 mm, a bottom thickness of 2.4 mm, and a shaping radius of 7.0 mm, the strength of the cage was evaluated both before and after the improvements. The results indicated that the enhanced cage exhibited superior strength. Full article
(This article belongs to the Section Electrical Machines and Drives)
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19 pages, 4917 KiB  
Article
Biomimetic Origami: Planar Single-Vertex Multi-Crease Mechanism Design and Optimization
by Yihang Wang, Yongsheng Zhao, Bo Han, Jinming Dong, Meng Han and Jiantao Yao
Machines 2025, 13(3), 240; https://doi.org/10.3390/machines13030240 - 17 Mar 2025
Viewed by 448
Abstract
Space exploration and satellite communication demand lightweight, large-scale, and highly deployable structures. Inspired by the folding mechanism of frilled lizards and origami mechanisms, this study explores a deployable structure based on the single-vertex multi-crease origami (SVMCO) concept. The design focuses on crease distribution [...] Read more.
Space exploration and satellite communication demand lightweight, large-scale, and highly deployable structures. Inspired by the folding mechanism of frilled lizards and origami mechanisms, this study explores a deployable structure based on the single-vertex multi-crease origami (SVMCO) concept. The design focuses on crease distribution optimization to enhance deployment efficiency. A mathematical model analyzes the relationship between sector angles of three types of facets and structural performances, providing guidelines for achieving optimal deployment. Drawing from the rib patterns of frilled lizards, a rib support system for thick-panel mechanisms was designed and verified through a physical prototype. The structure achieves smooth-surface deployment with fewer supports, offering a lightweight and efficient solution for deployable systems. Full article
(This article belongs to the Section Machine Design and Theory)
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32 pages, 13506 KiB  
Article
VR Co-Lab: A Virtual Reality Platform for Human–Robot Disassembly Training and Synthetic Data Generation
by Yashwanth Maddipatla, Sibo Tian, Xiao Liang, Minghui Zheng and Beiwen Li
Machines 2025, 13(3), 239; https://doi.org/10.3390/machines13030239 - 17 Mar 2025
Viewed by 614
Abstract
This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages [...] Read more.
This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages Quest Pro’s body-tracking capabilities to enable ergonomic, immersive interactions with planned eye-tracking integration for improved interactivity and accuracy. The Niryo One robot aids users in hands-on disassembly while generating synthetic data to refine robot motion planning models. A Robot Operating System (ROS) bridge enables the seamless simulation and control of various robotic platforms using Unified Robotics Description Format (URDF) files, bridging virtual and physical training environments. A Long Short-Term Memory (LSTM) model predicts user interactions and robotic motions, optimizing trajectory planning and minimizing errors. Monte Carlo dropout-based uncertainty estimation enhances prediction reliability, ensuring adaptability to dynamic user behavior. Initial technical validation demonstrates the platform’s potential, with preliminary testing showing promising results in task execution efficiency and human–robot motion alignment, though comprehensive user studies remain for future work. Limitations include the lack of multi-user scenarios, potential tracking inaccuracies, and the need for further real-world validation. This system establishes a sandbox training framework for HRC in disassembly, leveraging VR and AI-driven feedback to improve skill acquisition, task efficiency, and training scalability across industrial applications. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)
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21 pages, 1908 KiB  
Article
Rolling Mill Looper-Tension Control for Suppression of Strip Thickness Deviation by Adaptive PI Controller with Uncertain Forward/Backward Slip
by Yu-Chan Huang and Chao-Chung Peng
Machines 2025, 13(3), 238; https://doi.org/10.3390/machines13030238 - 16 Mar 2025
Viewed by 289
Abstract
The looper-tension control is a crucial aspect of a hot strip finishing mill. It involves a highly nonlinear system with strong states coupling and uncertainty, and the performance directly impacts the thickness deviation, which is the most critical product index. From the system [...] Read more.
The looper-tension control is a crucial aspect of a hot strip finishing mill. It involves a highly nonlinear system with strong states coupling and uncertainty, and the performance directly impacts the thickness deviation, which is the most critical product index. From the system dynamics, it is known that tension is highly sensitive to the strip velocity variation, which is typically unmeasurable. Instead, it needs to be calculated through work roll speed and strip slip which contains uncertainties, negatively affecting tension control performance. First, a feedback linearization-based proportional–integral (PI) controller design approach is proposed for the hot rolling looper-tension system. Second, to reduce the impact of speed uncertainties and enhance thickness response, an adaptive PI controller is introduced. Validation was conducted by numerical simulations; the result indicates that an adaptive PI controller reduces the magnitude of thickness variation and shortens the duration of its impact, verifying the consistency between theoretical derivation. The proposed control method effectively addresses the impact of uncertainties encountered in real-world applications. Additionally, it simplifies control parameter adjustment in practical use, reduces testing time, and improves product quality. Full article
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)
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24 pages, 8949 KiB  
Article
Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model
by Arumugam Balasuadhakar, Sundaresan Thirumalai Kumaran and Saood Ali
Machines 2025, 13(3), 237; https://doi.org/10.3390/machines13030237 - 14 Mar 2025
Viewed by 370
Abstract
In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its [...] Read more.
In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its cost-effectiveness and improved cooling efficiency compared to conventional flood cooling. This study investigates the end milling of AISI H11 die steel, utilizing a cooling system that involves a mixture of graphene nanoparticles (Gnps) and sesame oil for MQL. The experimental framework is based on a Taguchi L36 orthogonal array, with key parameters including feed rate, cutting speed, cooling condition, and air pressure. The resulting outcomes for cutting zone temperature and surface roughness were analyzed using the Taguchi Signal-to-Noise ratio and Analysis of Variance (ANOVA). Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model was developed to assess the impact of process parameters on cutting temperature and surface quality. The optimal cutting parameters were found to be a cutting speed of 40 m/min, a feed rate of 0.01 mm/rev, a jet pressure of 4 bar, and a nano-based MQL cooling environment. The adoption of these optimal parameters resulted in a substantial 62.5% reduction in cutting temperature and a 68.6% decrease in surface roughness. Furthermore, the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process. Full article
(This article belongs to the Special Issue Surface Engineering Techniques in Advanced Manufacturing)
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12 pages, 10470 KiB  
Article
Analysis of Rotor Lamination Sleeve Loss in High-Speed Permanent Magnet Synchronous Motor
by Yiming Tian, Shiqiang Liang, Fukang Wang, Jiahao Tian, Kai Chen and Shi Liu
Machines 2025, 13(3), 236; https://doi.org/10.3390/machines13030236 - 14 Mar 2025
Viewed by 390
Abstract
This study addressed the challenges of excessive eddy current losses and elevated thermal risks to permanent magnets in titanium alloy rotor sleeves for high-speed permanent magnet synchronous motors (HSPMSMs). Focusing on a 10 kW, 30,000 rpm high-speed motor, we innovatively propose incorporating insulating [...] Read more.
This study addressed the challenges of excessive eddy current losses and elevated thermal risks to permanent magnets in titanium alloy rotor sleeves for high-speed permanent magnet synchronous motors (HSPMSMs). Focusing on a 10 kW, 30,000 rpm high-speed motor, we innovatively propose incorporating insulating layers between axially laminated sleeve structures. Current research primarily mitigates eddy currents through the limited axial segmentation of sleeves/permanent magnets or radial shielding layers, while the technical approach of applying insulating coatings between laminated sleeves remains unexplored. This investigation demonstrated that compared with conventional solid sleeves, segmented sleeves, and carbon fibre sleeves, the laminated structure with a coordinated design of aluminium oxide and epoxy resin insulating layers effectively blocked the eddy current paths to achieve a substantial reduction in the sleeve eddy current density. This research concurrently highlights that the dynamic stress response and long-term operational reliability require further experimental validation. Subsequent investigations could explore optimised lamination patterns, parameter matching of insulating layers, and integration with emerging cooling technologies, thereby advancing synergistic breakthroughs in lightweight design and thermal management for high-speed motor rotors. Full article
(This article belongs to the Special Issue Analysis, Control and Design of Permanent Magnet Machines)
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27 pages, 7641 KiB  
Article
Generating Synthetic Datasets with Deep Learning Models for Human Physical Fatigue Analysis
by Arsalan Lambay, Ying Liu, Phillip Morgan and Ze Ji
Machines 2025, 13(3), 235; https://doi.org/10.3390/machines13030235 - 13 Mar 2025
Viewed by 661
Abstract
There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that [...] Read more.
There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that combine technological innovation with human-centric development. By analysing real-world data, machine learning (ML) models can detect physical fatigue. However, sensor-based data collection is frequently used, which is often expensive and constrained. To overcome this gap, synthetic data generation (SDG) uses methods such as tabular generative adversarial networks (GANs) to produce statistically realistic datasets that improve machine learning model training while providing scalability and cost-effectiveness. This study presents an innovative approach utilising conditional GAN with auxiliary conditioning to generate synthetic datasets with essential features for detecting human physical fatigue in industrial scenarios. This approach allows us to enhance the SDG process by effectively handling the heterogeneous and imbalanced nature of human fatigue data, which includes tabular, categorical, and time-series data points. These generated datasets will be used to train specialised ML models, such as ensemble models, to learn from the original dataset from the extracted feature and then identify signs of physical fatigue. The trained ML model will undergo rigorous testing using authentic, real-world data to evaluate its sensitivity and specificity in recognising how closely generated data match with actual human physical fatigue within industrial settings. This research aims to provide researchers with an innovative method to tackle data-driven ML challenges of data scarcity and further enhance ML technology’s efficiency through training on SD. This study not only provides an approach to create complex realistic datasets but also helps in bridging the gap of Industry 5.0 data challenges for the purpose of innovations and worker well-being by improving detection capabilities. Full article
(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)
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31 pages, 10107 KiB  
Article
Mechanical Characterization and Feasibility Analysis of PolyJet™ Materials in Tissue-Mimicking Applications
by Yash Soni, Paul Rothweiler and Arthur G. Erdman
Machines 2025, 13(3), 234; https://doi.org/10.3390/machines13030234 - 13 Mar 2025
Viewed by 592
Abstract
PolyJet™ 3D printing is an additive manufacturing (AM) technology from StratasysTM. It has been used for applications such as tissue mimicking, printing anatomical models, and surgical planning. The materials available from StratasysTM have the inherent capabilities of producing a number [...] Read more.
PolyJet™ 3D printing is an additive manufacturing (AM) technology from StratasysTM. It has been used for applications such as tissue mimicking, printing anatomical models, and surgical planning. The materials available from StratasysTM have the inherent capabilities of producing a number of PolyJet™ materials with a range of physical properties that can be utilized for representing realistic tissue behavior mechanically. The preset materials available in the PolyJet™ printing software version 1.92.17.44384 GrabCADTM Print allow the user to manufacture materials similar to biological tissue, but the combinations of possibilities are limited and might not represent the broad spectrum of all tissue types. The purpose of this study was to determine the combination of PolyJet™ materials that most accurately mimicked a particular biological tissue mechanically. A detailed Design of Experiment (DOE) methodology was used to determine the combination of material mixtures and printing parameters and to analyze their mechanical properties that best matched the biological tissue properties available in the literature of approximately 50 different tissue types. Uniaxial tensile testing was performed according to the ASTM standard D638-14 of samples printed from Stratasys J850 digital anatomy printer to their determined stress–strain properties. The obtained values were subsequently validated by comparing them with the corresponding mechanical properties of biological tissues available in the literature. The resulting model, developed using the DOE approach, successfully produced artificial tissue analogs that span a wide range of mechanical characteristics, from tough, load-bearing tissues to soft, compliant tissues. The validation confirmed the effectiveness of the model in replicating the diverse mechanical behavior of various human tissues. Overall, this paper provides a detailed methodology of how materials and settings were chosen in GrabCADTM Print software and Digital Anatomy CreatorTM (DAC) to achieve an accurate artificial tissue material. Full article
(This article belongs to the Special Issue Recent Advances in 3D Printing in Industry 4.0)
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25 pages, 14946 KiB  
Article
The Application of Recurrence Plots to Identify Nonlinear Responses Using Magnetometer Data for Wind Turbine Design
by Juan Carlos Jauregui-Correa and Luis Morales-Velazquez
Machines 2025, 13(3), 233; https://doi.org/10.3390/machines13030233 - 13 Mar 2025
Viewed by 1311
Abstract
This work uses recurrence plots (RPs) to identify nonlinearities and non-stationary conditions in wind turbines. Traditionally, recurrence plots have been applied to vibration or acoustic data; this paper applies them to magnetometer and accelerometer data to compare the sensitivity. The recurrence plots are [...] Read more.
This work uses recurrence plots (RPs) to identify nonlinearities and non-stationary conditions in wind turbines. Traditionally, recurrence plots have been applied to vibration or acoustic data; this paper applies them to magnetometer and accelerometer data to compare the sensitivity. The recurrence plots are generated by plotting points in the phase space and identifying those points where the dynamic system returns to a similar configuration, meaning that the state variables are similar to previous conditions. The state variables for the acceleration data are the position and velocity, whereas, for the magnetometer data, they are the magnitude of the magnetic field and its integral. The time series are integrated by combining the shifting principle of harmonic functions and the empirical mode decomposition. The EMD method separates the original signal into several modes, shifts them, and combines them back. The time series were obtained from an accelerometer and a magnetometer mounted in a wind turbine. The results showed that the RP presents different patterns depending on the signal; magnetometer signals identify low-frequency components, such as magnetic field anomalies, and accelerometer signals identify high-frequency components, such as bearings and gears. Full article
(This article belongs to the Special Issue Nonlinear Mechanical Vibration in Machine Design)
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35 pages, 7142 KiB  
Review
A Bibliometric Review of 3D-Printed Functionally Graded Materials, Focusing on Mechanical Properties
by Cristina Veres and Maria Tănase
Machines 2025, 13(3), 232; https://doi.org/10.3390/machines13030232 - 12 Mar 2025
Viewed by 547
Abstract
Functionally graded materials (FGMs) are a class of advanced materials characterized by spatially varying properties, offering significant advantages in aerospace, automotive, and biomedical industries. The integration of additive manufacturing (AM) has revolutionized the fabrication of FGMs, enabling precise control over material gradients and [...] Read more.
Functionally graded materials (FGMs) are a class of advanced materials characterized by spatially varying properties, offering significant advantages in aerospace, automotive, and biomedical industries. The integration of additive manufacturing (AM) has revolutionized the fabrication of FGMs, enabling precise control over material gradients and complex geometries. This review presents a comprehensive bibliometric and content analysis of 3D-printed FGMs, focusing on materials, processing techniques, mechanical properties, and application trends. The findings highlight the growing research interest in FGMs since 2016, with a peak in 2021, and the dominant contributions from the USA and China. Key research trends include advancements in selective laser melting and direct energy deposition techniques, which have enhanced mechanical performance by improving wear resistance, tensile strength, and elasticity. Despite these advancements, challenges such as residual stresses, interfacial bonding weaknesses, and material anisotropy persist. Future research should focus on optimizing AM processes to enhance material homogeneity, developing eco-friendly materials to align with sustainability goals, and establishing standardized testing methods for FGMs to ensure their reliability in industrial applications. Full article
(This article belongs to the Special Issue Advances in 3D Printing Technologies: Materials, Processes, and Applications)
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20 pages, 6052 KiB  
Article
Representation Learning for Vision-Based Autonomous Driving via Probabilistic World Modeling
by Haoqiang Chen, Yadong Liu and Dewen Hu
Machines 2025, 13(3), 231; https://doi.org/10.3390/machines13030231 - 12 Mar 2025
Viewed by 442
Abstract
Representation learning plays a vital role in autonomous driving by extracting meaningful features from raw sensory inputs. World models emerge as an effective approach to representation learning by capturing predictive features that can anticipate multiple possible futures, which is particularly suited for driving [...] Read more.
Representation learning plays a vital role in autonomous driving by extracting meaningful features from raw sensory inputs. World models emerge as an effective approach to representation learning by capturing predictive features that can anticipate multiple possible futures, which is particularly suited for driving scenarios. However, existing world model approaches face two critical limitations: First, conventional methods rely heavily on computationally expensive variational inference that requires decoding back to high-dimensional observation space. Second, current end-to-end autonomous driving systems demand extensive labeled data for training, resulting in prohibitive annotation costs. To address these challenges, we present BYOL-Drive, a novel method that firstly introduces the self-supervised representation-learning paradigm BYOL (Bootstrap Your Own Latent) to implement world modeling. Our method eliminates the computational burden of observation space decoding while requiring substantially fewer labeled data compared to mainstream approaches. Additionally, our model only relies on monocular camera images as input, making it easy to deploy and generalize. Based on this learned representation, experiments on the standard closed-loop CARLA benchmark demonstrate that our BYOL-Drive achieves competitive performance with improved computational efficiency and significantly reduced annotation requirements compared to the state-of-the-art methods. Our work contributes to the development of end-to-end autonomous driving. Full article
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22 pages, 7429 KiB  
Article
Nonlinear Dynamic Modeling of a Gear-Bearing Transmission System Based on Dynamic Meshing Parameters
by Jinzhou Song, Lei Hou, Rui Ma, Zhonggang Li, Rongzhou Lin, Yi Chen, Yushu Chen and Nasser A. Saeed
Machines 2025, 13(3), 230; https://doi.org/10.3390/machines13030230 - 12 Mar 2025
Viewed by 347
Abstract
The nonlinear contact force between gears and bearings exhibits intricate dynamics. This paper focuses on the coupling relationship between the time-varying meshing parameters of the gears, dynamic backlash, and dynamic bearing clearance in gear-bearing transmission systems. A dynamic model of a gear-bearing transmission [...] Read more.
The nonlinear contact force between gears and bearings exhibits intricate dynamics. This paper focuses on the coupling relationship between the time-varying meshing parameters of the gears, dynamic backlash, and dynamic bearing clearance in gear-bearing transmission systems. A dynamic model of a gear-bearing transmission system considering dynamic meshing parameters is established. The coupling mechanism between meshing stiffness, gear backlash, bearing clearance, and gear vibration response in gear transmission systems is analyzed. The results demonstrate a negative correlation between the gears’ geometric center distance and meshing stiffness amplitude. Gear vibration can affect the relative position of the gears. Changes in the relative position of the gears lead to an increase in the number of frequency components in the frequency domain of gear meshing stiffness. During gear rotation, the meshing parameters of the gears and tooth side clearance fluctuate with gear vibration. With increasing speed, the model’s dynamic meshing parameters also increase accordingly. The model achieves a feedback calculation of the system parameters and vibration responses in gear-bearing system dynamics. Full article
(This article belongs to the Special Issue Nonlinear Dynamics, Vibration Monitoring and Fault Diagnostics in Rotating Systems)
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17 pages, 5812 KiB  
Article
Trajectory Tracking of a Wall-Climbing Cutting Robot Based on Kinematic and PID Joint Optimization
by Xiaoguang Liu, Zhenmin Wang, Jing Wu, Hongmin Wu and Hao Zhang
Machines 2025, 13(3), 229; https://doi.org/10.3390/machines13030229 - 12 Mar 2025
Viewed by 337
Abstract
Cutting is a crucial step in the industrial production process, particularly in the manufacture of large structures. In certain spatial positions, using a mobile robot, especially a wall-climbing robot (WCR) with adsorption function, is essential for carrying cutting torches to cut large steel [...] Read more.
Cutting is a crucial step in the industrial production process, particularly in the manufacture of large structures. In certain spatial positions, using a mobile robot, especially a wall-climbing robot (WCR) with adsorption function, is essential for carrying cutting torches to cut large steel components. The cutting quality directly impacts the overall manufacturing quality. Therefore, effectively tracking the cutting trajectory of wall-climbing cutting robots is very important. This study proposes a controller based on a kinematic model and PID optimization. The controller is designed to manage the robot’s kinematic trajectory, including the torch slider, through the kinematic modeling of the wall-climbing cutting robot (WCCR). The stability of the control law is proven using the Lyapunov function, which controls the linear and angular velocities of the WCCR and the motion speed of the cross slider. Simulations verify that the control law performs well in tracking both straight-line and circular trajectories. The impact of different control law parameters on straight-line trajectory tracking is also compared. By introducing PID optimization control, the controller’s anti-interference capabilities are enhanced, addressing the issue of motion velocity fluctuation when the WCCR tracks curved trajectories. The simulation and experiment results demonstrate the effectiveness of the proposed controller. Full article
(This article belongs to the Special Issue Climbing Robots: Scaling Walls with Precision and Efficiency)
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21 pages, 5290 KiB  
Article
Design, Testing, and Optimization of a Filling-Type Silage Crushing, Shredding, and Baling Integrated Machine
by Tong Dai, Wei Sun, Danzhu Zhang and Petru A. Simionescu
Machines 2025, 13(3), 228; https://doi.org/10.3390/machines13030228 - 12 Mar 2025
Viewed by 367
Abstract
To address the limitations of large silage machines in hilly and small-scale farming regions and the inefficiencies of existing small-scale crushing and baling machines, in this study, we developed an integrated silage crushing, shredding, and baling machine. Using discrete element software (EDEM 2022.3), [...] Read more.
To address the limitations of large silage machines in hilly and small-scale farming regions and the inefficiencies of existing small-scale crushing and baling machines, in this study, we developed an integrated silage crushing, shredding, and baling machine. Using discrete element software (EDEM 2022.3), the baling process of shredded straw was simulated, achieving a baled grass density of 140.067 kg/m3, meeting practical requirements. A three-factor, three-level experiment was conducted to evaluate the effects of the hammer blade quantity, blade length, and hammer angle on machine productivity and straw shredding rate. Performance data were analyzed using Design-Expert 10.0.7 software to develop regression models and assess the significance of each factor. The results indicated that productivity was most influenced by hammer blade quantity, followed by blade length and hammer angle, while the shredding rate was primarily affected by blade length, then hammer blade quantity, and hammer angle. The optimal configuration was identified as 32 hammer blades, a blade length of 99 mm, and a hammer angle of 14°. Validation experiments demonstrated a productivity of 2815.29 kg/h, a straw shredding rate of 94.28%, and a baled grass density of 124.52 kg/m3, closely aligning with the predicted values and confirming the reliability of the optimization. Full article
(This article belongs to the Section Machine Design and Theory)
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25 pages, 30860 KiB  
Article
Comparison of Induction Machine Drive Control Schemes on the Distribution of Power Losses in a Three-Level NPC Converter
by Carlos A. Reusser, Matías Parra, Gerardo Mino-Aguilar and Victor R. Gonzalez-Diaz
Machines 2025, 13(3), 227; https://doi.org/10.3390/machines13030227 - 12 Mar 2025
Viewed by 311
Abstract
Medium- and high-power drive applications have grown since the past decade as the most common solution for high demanding industrial processes. Multilevel converters, in particular the three-level neutral point clamped (3L-NPC) topology driving medium-voltage induction machines, has become the most commonly adopted solution. [...] Read more.
Medium- and high-power drive applications have grown since the past decade as the most common solution for high demanding industrial processes. Multilevel converters, in particular the three-level neutral point clamped (3L-NPC) topology driving medium-voltage induction machines, has become the most commonly adopted solution. In this context, several AC drive control schemes are suitable, such as scalar control (SC), field-oriented control (FOC), model predictive control (MPC), and direct torque control (DTC). Each of these control strategies exhibit a particular operational profile which affects the switching pattern of the converter semiconductors, thus conditioning the switching and conducting losses of these power devices. This work presents a comparison of the conduction and switching losses between different drives control schemes, such as scalar control, field-oriented control, direct torque control, and model predictive control, analyzing their impact on thermal efficiency in a 3L-NPC multilevel converter, under different loading operational conditions. This analysis allows for choosing the most suitable control strategy and switching frequency for a given operational profile. Full article
(This article belongs to the Special Issue New Trends of Permanent Magnet Machines)
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18 pages, 13899 KiB  
Article
Enhanced Force Sensing Method Using BP Neural Network and Sensitive Point Analysis for Forging Manipulator Clamping Mechanism in Operational Conditions
by Yangtao Xing, Fugang Zhai, Zhiqiang He, Xiaonan Wang and Runyuan Zhao
Machines 2025, 13(3), 226; https://doi.org/10.3390/machines13030226 - 11 Mar 2025
Viewed by 357
Abstract
Given the challenging operational environment of forging machines, characterized by harsh conditions, dynamic load fluctuations, and multi-axis force coupling, traditional force measurement and sensing methods often fail to meet the high demands for precision and reliable load sensing. To address this challenge, this [...] Read more.
Given the challenging operational environment of forging machines, characterized by harsh conditions, dynamic load fluctuations, and multi-axis force coupling, traditional force measurement and sensing methods often fail to meet the high demands for precision and reliable load sensing. To address this challenge, this study introduces a novel force sensing methodology based on a Backpropagation (BP) neural network for the clamping mechanism of forging machines. First, a force sensing model is developed to describe the behavior of the clamping mechanism under forging conditions, elucidating the force transmission laws and identifying key hinge points for force sensing. Next, a BP neural network model is constructed to analyze and decouple the forging state, exploring the impact of hidden layer neuron nodes on decoupling effectiveness and determining the optimal configuration of these nodes. The effectiveness of the proposed methodology is demonstrated through the network’s ability to decouple both the direction and magnitude of internal forging forces. The results confirm that this approach enables accurate and reliable force sensing for the clamping mechanism, providing a robust foundation for force control in forging machines. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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20 pages, 15155 KiB  
Article
Nonlinear Vibration of Oblique-Stiffened Multilayer Functionally Graded Cylindrical Shells Under External Excitation with Internal and Superharmonic Resonances
by Kamran Foroutan and Farshid Torabi
Machines 2025, 13(3), 225; https://doi.org/10.3390/machines13030225 - 11 Mar 2025
Viewed by 331
Abstract
This research examines a semi-analytical approach for analyzing the nonlinear vibration (NV) characteristics of oblique-stiffened multilayer functionally graded (OSMFG) cylindrical shells (CSs) under external excitation. The material’s properties are continuously graded along the thickness direction. The CSs are made up of three layers: [...] Read more.
This research examines a semi-analytical approach for analyzing the nonlinear vibration (NV) characteristics of oblique-stiffened multilayer functionally graded (OSMFG) cylindrical shells (CSs) under external excitation. The material’s properties are continuously graded along the thickness direction. The CSs are made up of three layers: an inner metal-rich layer, an exterior ceramic-rich layer, and a functionally graded (FG) layer in between. The stiffeners’ constitutive material is graded constantly throughout their thicknesses. von Kármán equations, the smeared stiffener technique, and the Galerkin approach are used to address the NV problem. The vibration behavior is investigated via the method of multiple scales (MMSs). The analysis considers an internal resonance of 1:1/3:1/9 as well as a superharmonic resonance of order 3/1. The impacts of various material and geometric characteristics on the NV of OSMF-CSs are thoroughly investigated. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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