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Volume 13
Issue 4
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Machines, Volume 13, Issue 4 (April 2025) – 84 articles

Cover Story (view full-size image): The focus of Industry 4.0 was on technological advancement, often overlooking human interaction. Industry 5.0 shifts the emphasis toward the role of humans and their interaction with emerging technologies. According to this new paradigm, in this paper, we investigate the automation of a traditionally manual task within the logistics sector, employing a human-centered approach aimed at minimizing high-risk ergonomic activities and improving operator well-being. To support this, a motion capture system and digital human simulation software were used to develop a digital twin of a real-world industrial case study. This virtual environment enabled the testing and evaluation of different automation strategies, ultimately identifying the most effective solution based on performance indicators. View this paper
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19 pages, 4399 KiB  
Article
Spike Stall Precursor Detection in a Single-Stage Axial Compressor: A Data-Driven Dynamic Modeling Approach
by Anish Thapa, Jichao Li and Marco P. Schoen
Machines 2025, 13(4), 338; https://doi.org/10.3390/machines13040338 - 21 Apr 2025
Abstract
Operational safety and fuel efficiency are critical, yet often conflicting, objectives in modern civil get engine designs. Optimal efficiency operating conditions are typically close to unsafe regions, such as compressor stalls, which can cause severe engine damage. Consequently, engines are generally operated below [...] Read more.
Operational safety and fuel efficiency are critical, yet often conflicting, objectives in modern civil get engine designs. Optimal efficiency operating conditions are typically close to unsafe regions, such as compressor stalls, which can cause severe engine damage. Consequently, engines are generally operated below peak efficiency to maintain a sufficient stall margin. Reducing this margin through active control requires stall precursor detection and mitigation mechanisms. While several algorithms have shown promising results in predicting modal stalls, predicting spike stalls remains a challenge due to their rapid onset, leaving little time for corrective actions. This study addresses this gap by proposing a method to identify spike stall precursors based on the changing dynamics within a compressor blade passage. An autoregressive time series model is utilized to capture these dynamics and its changes are related to the flow condition within the blade passage. The autoregressive model is adaptively extracted from measured pressure data from a one-stage axial compressor test stand. The corresponding eigenvalues of the model are monitored by utilizing an outlier detection mechanism that uses pressure reading statistics. Outliers are proposed to be associated with spike stall precursors. The model order, which defines the number of relevant eigenvalues, is determined using three information criteria: the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Conditional Model Estimator (CME). For prediction, an outlier detection algorithm based on the Generalized Extreme Studentized Deviate (GESD) Test is introduced. The proposed method is experimentally validated on a single-stage low-speed axial compressor. Results demonstrate consistent stall precursor detection, with future application for timely control interventions to prevent spike stall inception. Full article
(This article belongs to the Section Turbomachinery)
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35 pages, 20715 KiB  
Article
Enhancing Precision in Arc Welding Simulations: A Comprehensive Study of the Ellipsoidal Heat Source Model
by Senol Sert and Ergun Nart
Machines 2025, 13(4), 337; https://doi.org/10.3390/machines13040337 - 20 Apr 2025
Viewed by 6
Abstract
Arc welding is a complex multiphysics mitigation process, and the related finite element simulation requires significant computational resources for multiphysics modeling to determine the temperature distributions in engineering problems accurately. Engineers and researchers aim to achieve reliable results from finite element analysis while [...] Read more.
Arc welding is a complex multiphysics mitigation process, and the related finite element simulation requires significant computational resources for multiphysics modeling to determine the temperature distributions in engineering problems accurately. Engineers and researchers aim to achieve reliable results from finite element analysis while minimizing computational costs. This research extensively studies the application of conventional ellipsoidal heat source formulation to obtain improved temperature distribution during arc welding for practical applications. The ellipsoidal heat source model, which artificially modifies the coefficient of thermal conductivity in the welding pool area to simulate stirring effects, is proven to be scientifically valid by comparing its results with those of COMSOL’s multiphysics arc welding analyses. The findings from finite element analyses demonstrate that the temperature fields generated using the modified ellipsoidal approach exhibit strong agreement with those obtained from multiphysics simulations, especially within the core regions of the weld pool. The method can easily be implemented in all different welding methods in which a stirring effect is formed by either electromagnetic or buoyancy-driven flows in the weld pool area. Furthermore, the method offers computational efficiency without sacrificing accuracy, making it suitable for industrial applications where multiphysics modeling is not feasible but reliable thermal and structural predictions are essential. Full article
(This article belongs to the Section Advanced Manufacturing)
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19 pages, 2072 KiB  
Article
Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition
by Haodong Chi and Huiyuan Chen
Machines 2025, 13(4), 336; https://doi.org/10.3390/machines13040336 - 18 Apr 2025
Viewed by 44
Abstract
To address the issues of non-stationarity, noise interference, and insufficient discriminative power of traditional fault feature extraction methods in rolling bearing vibration signals, this paper proposes a fault diagnosis method based on multi-scale permutation entropy (MPE) and a multi-strategy improved sparrow search algorithm [...] Read more.
To address the issues of non-stationarity, noise interference, and insufficient discriminative power of traditional fault feature extraction methods in rolling bearing vibration signals, this paper proposes a fault diagnosis method based on multi-scale permutation entropy (MPE) and a multi-strategy improved sparrow search algorithm (MSSA) under local mean decomposition (LMD). First, LMD is employed to adaptively decompose the original signal. Effective product functions (PFs) are then selected using the Pearson correlation coefficient, enabling signal reconstruction that suppresses noise interference while preserving fault impact components. Second, to overcome the limited capability of traditional time-frequency features in representing complex fault patterns, MPE is introduced to construct a multi-scale complexity feature vector, effectively capturing the scale-dependent differences in the dynamic behavior of signals. Furthermore, considering the instability of classification caused by the empirical setting of hidden layer nodes in the extreme learning machine (ELM), a multi-strategy improved sparrow search algorithm is proposed to optimize ELM parameters. This algorithm integrates an adaptive Levy flight mechanism and dynamic reverse learning. The long-tail jump characteristics of Levy flight enhance the global search capability, while dynamic reverse learning increases population diversity, preventing premature convergence. The experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of over 96% across multiple datasets, verifying its robustness in handling non-stationary signals and fault classification. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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23 pages, 7777 KiB  
Article
Research on GIS Circuit Breaker Fault Diagnosis Based on Closing Transient Vibration Signals
by Yue Yu and Hongyan Zhao
Machines 2025, 13(4), 335; https://doi.org/10.3390/machines13040335 - 18 Apr 2025
Viewed by 103
Abstract
GIS circuit breakers play a critical role in maintaining the reliability of modern power systems. However, mechanical failures, such as spring fatigue, transmission rod jamming, and loosening of structural components, can significantly impact their performance. Traditional diagnostic methods struggle to identify these issues [...] Read more.
GIS circuit breakers play a critical role in maintaining the reliability of modern power systems. However, mechanical failures, such as spring fatigue, transmission rod jamming, and loosening of structural components, can significantly impact their performance. Traditional diagnostic methods struggle to identify these issues effectively due to the enclosed nature of GIS equipment. This study explores the use of vibration signal analysis, specifically during the closing transient phase of the GIS circuit breaker. The proposed method combines wavelet packet decomposition, rough set theory for feature extraction and dimensionality reduction, and the S_Kohonen neural network for fault type identification. Experimental results demonstrate the robustness and accuracy of the method, achieving a diagnostic accuracy of 96.7% in identifying mechanical faults. Compared with traditional methods, this approach offers improved efficiency and accuracy in diagnosing GIS circuit breaker faults. The proposed method is highly applicable for predictive maintenance and fault diagnosis in power grid systems, contributing to enhanced operational safety and reliability. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 7874 KiB  
Article
MomentumNet-CD: Real-Time Collision Detection for Industrial Robots Based on Momentum Observer with Optimized BP Neural Network
by Jinhua Ye, Yechen Fan, Quanjie Kang, Xiaohan Liu, Haibin Wu and Gengfeng Zheng
Machines 2025, 13(4), 334; https://doi.org/10.3390/machines13040334 - 18 Apr 2025
Viewed by 153
Abstract
The accurate detection and identification of collision states in industrial robot environments is a critically important and challenging task. Deep learning-based methods have been widely applied to collision detection; however, these methods primarily rely on dynamic models and dynamic threshold settings, which are [...] Read more.
The accurate detection and identification of collision states in industrial robot environments is a critically important and challenging task. Deep learning-based methods have been widely applied to collision detection; however, these methods primarily rely on dynamic models and dynamic threshold settings, which are subject to modeling errors and threshold adjustment latency. To address this issue, we propose MomentumNet-CD, a novel collision detection method for industrial robots that leverages backpropagation (BP) neural networks. MomentumNet-CD extracts collision state features through a momentum observer and constructs an observation model using Mahalanobis distance. These features are then processed by an optimized three-layer BP neural network for accurate collision identification. The network is trained using a modified Levenberg–Marquardt algorithm by introducing regularization terms and continuous probability outputs. Furthermore, we developed a comprehensive acquisition system based on the Q8-USB data acquisition card and the QUARC 2.7 real-time control environment. The system integrates key hardware components including a MR-J2S-70A servo driver, ATI six-dimensional force/torque (F/T) sensor, and ISO-U2-P1-F8 isolation transmitter, and the corresponding software module is developed through MATLAB/Simulink R2022b, which achieves the high-frequency real-time acquisition of critical robot joint states. The experimental results show that the MomentumNet-CD method achieves an overall accuracy of 93.65% under five different speed conditions, and the detection delay is only 12.16 ms. Compared with the existing methods, the method shows obvious advantages in terms of the accuracy and response speed of collision detection. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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29 pages, 4987 KiB  
Review
A Review of Recent Advancements in Heat Pump Systems and Developments in Microchannel Heat Exchangers
by Roopesh Chowdary Sureddi, Liang Li, Hongwei Wu, Niccolo Giannetti, Kiyoshi Saito and David Rees
Machines 2025, 13(4), 333; https://doi.org/10.3390/machines13040333 - 18 Apr 2025
Viewed by 58
Abstract
Heating and cooling are the main concerns across a wide range of sectors, including residential buildings, industrial facilities, transportation and commercial enterprises. This being the case, a continuous rise in the cost of energy demands more effective ways to conserve energy. Heat pump [...] Read more.
Heating and cooling are the main concerns across a wide range of sectors, including residential buildings, industrial facilities, transportation and commercial enterprises. This being the case, a continuous rise in the cost of energy demands more effective ways to conserve energy. Heat pump (HP) systems provide the one of the best possible solutions to this problem as they offer an economical and energy-efficient system. In this review, HP systems are overviewed as energy-efficient and cost-effective solutions, focusing on their characteristic properties but also on enhancements, novel techniques and the use of heat exchangers (HXs), and microchannel heat exchangers (MCHEs) in these systems, as well as their development in recent years and their limitations. The main factors contributing to variations in the performance of HP systems are temperature and humidity in the ambient atmosphere. The present study is expected to support numerical and experimental performance analysis, and design miniaturisation via MCHEs. Unique designs or manufacturing techniques in MCHEs; various configurations in HP systems, depending on their load and environmental conditions; various nanofluids; and a comparison of nanofluids with different base metals are presented and discussed. Comparisons between various MCHEs and their respective limitations provide evidence-based guidelines for technology selection and designs for optimised operation at given environmental and load conditions. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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23 pages, 12252 KiB  
Article
Mechanical Fault Diagnosis Method of a Disconnector Based on Improved Dung Beetle Optimizer–Multivariate Variational Mode Decomposition and Convolutional Neural Network–Bidirectional Long Short-Term Memory
by Chi Zhang, Hongzhong Ma and Wei Sun
Machines 2025, 13(4), 332; https://doi.org/10.3390/machines13040332 - 18 Apr 2025
Viewed by 126
Abstract
As one of the main faults of a disconnector, a mechanical fault is difficult to diagnose in time because of its weak self-evidence, its wide range of fault categories, and the difficulty in obtaining fault sample data. To address this issue, this study [...] Read more.
As one of the main faults of a disconnector, a mechanical fault is difficult to diagnose in time because of its weak self-evidence, its wide range of fault categories, and the difficulty in obtaining fault sample data. To address this issue, this study proposes a new fault diagnosis algorithm based on multivariate variational mode decomposition optimized by the improved dung beetle optimizer, and at the same time, an experimental platform for vibration signal acquisition was built to simulate three typical mechanical faults. First, the parameters of multivariate variational mode decomposition were optimized using an improved dung beetle optimizer, and the intrinsic mode function with a Pearson correlation coefficient higher than 0.1 was retained after the signal was decomposed. Then, the energy, entropy, and time–frequency domain eigenvalues of the selected intrinsic mode function were calculated to construct the feature matrix, and its dimensions were reduced to two dimensions. Finally, this matrix was input to convolutional neural network–bidirectional long short-term memory for fault classification. The verification of the experimental data shows that the proposed algorithm can successfully diagnose different mechanical faults of the disconnector, and the accuracy rate was 96.67%. The research content provides a new idea for the fault diagnosis of disconnectors. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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19 pages, 4482 KiB  
Article
Decentralized Adaptive Control of Closed-Kinematic Chain Mechanism Manipulators
by Tri T. Nguyen, Charles C. Nguyen, Tuan M. Nguyen, Tu T. C. Duong, Ha Tang T. Ngo and Lu Sun
Machines 2025, 13(4), 331; https://doi.org/10.3390/machines13040331 - 18 Apr 2025
Viewed by 159
Abstract
This paper presents a new decentralized adaptive control scheme for motion control of robot manipulators built based on a closed-kinematic chain mechanism (CKCM). By employing the synchronization technique and model reference adaptive control (MRAC) based on the Lyapunov direct method, the Decentralized Adaptive [...] Read more.
This paper presents a new decentralized adaptive control scheme for motion control of robot manipulators built based on a closed-kinematic chain mechanism (CKCM). By employing the synchronization technique and model reference adaptive control (MRAC) based on the Lyapunov direct method, the Decentralized Adaptive Synchronized Control scheme (DASCS) is developed. The DASCS can ensure global asymptotic convergence of tracking errors while forcing all active joints to move in a predefined synchronous manner in the presence of uncertainties and sudden changes in payload. Furthermore, the control scheme has a simple structure, independent of the manipulator’s dynamic model, ensuring computational efficiency. Results of computer simulations conducted to evaluate the performance of the control scheme applied to controlling the motion of a CKCM manipulator with six degrees of freedom are reported and discussed. Full article
(This article belongs to the Section Automation and Control Systems)
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30 pages, 8754 KiB  
Article
Multi-Objective Optimization of Gear Design of E-Axles to Improve Noise Emission and Load Distribution
by Luciano Cianciotta, Marco Cirelli and Pier Paolo Valentini
Machines 2025, 13(4), 330; https://doi.org/10.3390/machines13040330 - 17 Apr 2025
Viewed by 92
Abstract
This paper presents a comprehensive methodology to enable the optimization of an automotive electric axle to reduce noise emissions and improve load distribution. The proposed method consists of the application of two sequential optimization procedures. The first one focuses on the gears’ macro-geometry, [...] Read more.
This paper presents a comprehensive methodology to enable the optimization of an automotive electric axle to reduce noise emissions and improve load distribution. The proposed method consists of the application of two sequential optimization procedures. The first one focuses on the gears’ macro-geometry, based on an objective function that combines the contact ratio, power loss, and center distance. The second one optimizes the micro-geometry of the teeth to reduce the sound pressure generated by tooth impacts. Mechanical stress limits are considered as a constraint in the optimization process. Shafts, joints, and the electric motor are analyzed, taking into account their deformation that influences the dynamics of the entire system. The results of the proposed procedure are verified through experimental measurements and the comparison can be considered successful. Full article
(This article belongs to the Special Issue Advances in Vehicle Dynamics)
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14 pages, 4344 KiB  
Article
Investigation of Transfer Learning Method for Motor Fault Detection
by Prashant Kumar, Saurabh Singh and Doug Young Song
Machines 2025, 13(4), 329; https://doi.org/10.3390/machines13040329 - 17 Apr 2025
Viewed by 78
Abstract
Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to [...] Read more.
Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to ensure maximum productivity. Recently, DL has been implemented as a data-driven approach for detecting faults in motors. However, due to the limited availability of labeled fault data, the performance of the DL model is constrained. This issue is addressed by leveraging transfer learning (TL), which uses knowledge from a larger source domain to improve performance in a smaller target domain. In this paper, a multiple fault detection (FD) model for EMs is proposed by combining the ideas of deep convolutional neural networks (CNNs) and TL. A one-dimensional signal-to-image conversion technique is suggested for converting the vibration signal to images, and an Inception-ResNet-v2-inspired FD model is proposed for detecting bearing faults in the motor. The proposed method achieved a mean accuracy of more than 99%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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15 pages, 3821 KiB  
Article
MATLAB Application for User-Friendly Design of Fully Convolutional Data Description Models for Defect Detection of Industrial Products and Its Concurrent Visualization
by Fusaomi Nagata, Shingo Sakata, Keigo Watanabe, Maki K. Habib and Ahmad Shahrizan Abdul Ghani
Machines 2025, 13(4), 328; https://doi.org/10.3390/machines13040328 - 17 Apr 2025
Viewed by 114
Abstract
In this paper, a fully convolutional data description (FCDD) model is applied to defect detection and its concurrent visualization for industrial products and materials. The authors’ propose a MATLAB application that enables users to efficiently and in a user-friendly way design, train, and [...] Read more.
In this paper, a fully convolutional data description (FCDD) model is applied to defect detection and its concurrent visualization for industrial products and materials. The authors’ propose a MATLAB application that enables users to efficiently and in a user-friendly way design, train, and test various kinds of neural network (NN) models for defect detection. Models supported by the application include the following original designs: convolutional neural network (CNN), transfer learning-based CNN, NN-based support vector machine (SVM), convolutional autoencoder (CAE), variational autoencoder (VAE), fully convolution network (FCN) (such as U-Net), and YOLO. However, FCDD is not yet supported. This paper includes the software development of the MATLAB R2024b application, which is extended to be able to build FCDD models. In particular, a systematic threshold determination method is proposed to obtain the best performance for defect detection from FCDD models. Also, through three different kinds of defect detection experiments, the usefulness and effectiveness of FCDD models in terms of defect detection and its concurrent visualization are quantitatively and qualitatively evaluated by comparing conventional transfer learning-based CNN models. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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2 pages, 147 KiB  
Correction
Correction: Saadah et al. Developing Robust Safety Protocols for Radiosurgery within Patient Positioning System Framework. Machines 2024, 12, 106
by Alaa Saadah, Laszlo Fadgyas, Donald Medlin, Jad Saud, Jason Henderson, Tibor Koroknai, Máté Koroknai, Levente Menyhárt, David Takacs, Peter Panko, Xiaoran Zheng, Endre Takacs and Géza Husi
Machines 2025, 13(4), 327; https://doi.org/10.3390/machines13040327 - 17 Apr 2025
Viewed by 38
Abstract
After the publication of the paper, Endre Takacs from Clemson University contacted the corresponding author Alaa Saadah to ask for multiple individuals (Laszlo Fadgyas, Jason Henderson, Tibor Koroknai, Máté Koroknai, David Takacs, Peter Panko, and Endre Takacs) to be added as additional co-authors [...] Read more.
After the publication of the paper, Endre Takacs from Clemson University contacted the corresponding author Alaa Saadah to ask for multiple individuals (Laszlo Fadgyas, Jason Henderson, Tibor Koroknai, Máté Koroknai, David Takacs, Peter Panko, and Endre Takacs) to be added as additional co-authors of the original publication [...] Full article
(This article belongs to the Section Industrial Systems)
20 pages, 5660 KiB  
Article
Fault Diagnosis for Imbalanced Datasets Based on Deep Convolution Fuzzy System
by Junwei Zhu and Linfang Zhu
Machines 2025, 13(4), 326; https://doi.org/10.3390/machines13040326 - 17 Apr 2025
Viewed by 127
Abstract
To address the data imbalance issue in the process of collecting bearing fault data in industrial environments and to enhance the robustness and generalization ability of fault diagnosis, this paper proposes a bearing fault diagnosis method based on a Bidirectional Autoregressive Variational Autoencoder [...] Read more.
To address the data imbalance issue in the process of collecting bearing fault data in industrial environments and to enhance the robustness and generalization ability of fault diagnosis, this paper proposes a bearing fault diagnosis method based on a Bidirectional Autoregressive Variational Autoencoder (BAVAE) and a Deep Convolutional Interval Type-2 Fuzzy System (DCIT2FS). First, the method extracts features from the imbalanced dataset using dual-tree complex wavelet transform (DTCWT), and then feeds the feature dataset into the proposed BAVAE for data augmentation. The BAVAE improves data generation capabilities by introducing autoregressive distributions to learn latent variables, iteratively obtaining complex high-order latent variables, and amplifying inter-class differences through the introduction of feature discrimination loss during training. Given that relying solely on data augmentation under imbalanced data conditions may lead to overfitting or underfitting, this paper combines the generalization approximation ability of Interval Type-2 (IT2) fuzzy systems with the feature extraction capability of deep convolutional networks, achieving a better balance between model complexity and feature transformation, thereby enhancing the stability and accuracy of the final diagnosis. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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26 pages, 15094 KiB  
Article
Design and Analysis of a Novel Hydraulic Energy Storage Component
by Jinlin Yao, Xiangyu He, Yuanhao Yang, Yanshuo Zhu, Guangxin Xiao and Yizhe Huang
Machines 2025, 13(4), 325; https://doi.org/10.3390/machines13040325 - 17 Apr 2025
Viewed by 120
Abstract
The hydraulic energy storage component (HESC) is the core component of hydraulic energy regeneration (HER) technologies in construction equipment, directly influencing the overall energy efficiency of the system. However, under complex practical operating conditions, the performance of traditional HESCs has become a critical [...] Read more.
The hydraulic energy storage component (HESC) is the core component of hydraulic energy regeneration (HER) technologies in construction equipment, directly influencing the overall energy efficiency of the system. However, under complex practical operating conditions, the performance of traditional HESCs has become a critical factor limiting the broader application of HER technologies. This paper proposes a novel hydraulic energy storage component (NHESC) that integrates hybrid energy storage through the use of compressed air and electric energy. The system configuration of the NHESC is first designed, followed by the modeling of key components and analysis of working states. Second, based on the working state of energy absorption and release in the NHESC, a corresponding determination strategy is formulated. Third, a simulation model of the boom potential energy regeneration (PER) system based on the NHESC is developed, with partial experimental validation to verify its reliability. Finally, the recovery, reuse, and regeneration efficiencies in state pair B-E of the NHESC mode and the accumulator mode are compared, followed by an analysis of energy losses in the hydraulic components. The analysis results, based on simulation, indicate that the regeneration efficiency of the NHESC is 55.1%, which is better than the 41.1% of the traditional hydraulic accumulator. The NHESC combines the advantages of compressed gas energy storage and electric energy storage, effectively resolving issues of passive operation and uncontrollability while demonstrating superior energy regeneration capabilities. Full article
(This article belongs to the Section Machine Design and Theory)
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27 pages, 16538 KiB  
Article
Attempts at Pseudo-Inverse Vibro-Acoustics by Means of SLDV-Based Full-Field Mobilities
by Alessandro Zanarini
Machines 2025, 13(4), 324; https://doi.org/10.3390/machines13040324 - 16 Apr 2025
Viewed by 179
Abstract
Lightweight components can have structural integrity and reliability concerns, coming from dynamic airborne pressure fields. Hardly tuned numerical structural models may enter into vibro-acoustic simulations of the pressure fields radiated by vibrating plates, potentially masking the forecast of severe outputs. Instead, this paper [...] Read more.
Lightweight components can have structural integrity and reliability concerns, coming from dynamic airborne pressure fields. Hardly tuned numerical structural models may enter into vibro-acoustic simulations of the pressure fields radiated by vibrating plates, potentially masking the forecast of severe outputs. Instead, this paper proposes—for the direct and inverse vibro-acoustic approaches—to characterise the broad frequency band structural dynamics of radiating surfaces by means of experiment-based full-field contactless techniques, with increased spatial resolution, but without the inertia-related distortions of traditional measurement transducers. The SLDV-based mobilities bring the real-life behaviour of the component into the vibro-acoustic simulations, with the actual realisation-related complete structural dynamics and broad frequency band excitation. The paper aims at assessing the procedure for the estimation, in the whole spectrum, of the airborne force, which can be transmitted by an airborne pressure field to known structural locations. The simulation tools revisit the simple Rayleigh integral approximation of sound radiation from a vibrating surface, a real thin flat plate, describable by SLDV-based complex-valued full-field mobilities. Airborne pressure fields and excitation forces concern the early attempts of direct and pseudo-inverse vibro-acoustics. Details, examples and considerations about the whole procedures are thoroughly provided: on the simulation of the vibro-acoustic transfer matrix and of the radiated sound pressures with given excitation forces; on the retrieval of the airborne forces in restraining locations, together with the assessment of the numerical precision of the retrieving procedure. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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23 pages, 7117 KiB  
Article
Effect of Wheel Polygonalization on the Dynamic Characteristics of Gear-Transmission Systems of Urban Railway Vehicles
by Danping Xu, Jinhai Wang, Jianwei Yang, Yi Wu and Xiaorui Wen
Machines 2025, 13(4), 323; https://doi.org/10.3390/machines13040323 - 16 Apr 2025
Viewed by 84
Abstract
The gear-transmission system plays a crucial role in power transmission for urban railway vehicles. However, it can experience abnormal meshing conditions due to wheel polygonization, which presents a potential safety hazard for vehicle operations. To address this issue, the present study develops a [...] Read more.
The gear-transmission system plays a crucial role in power transmission for urban railway vehicles. However, it can experience abnormal meshing conditions due to wheel polygonization, which presents a potential safety hazard for vehicle operations. To address this issue, the present study develops a dynamic model of an urban railway vehicle that integrates the gear-transmission system, simulating the effects of wheel polygonization on its dynamic behavior. The simulation results reveal that as the amplitude of wheel polygonization and vehicle speed increase, the vertical wheel–rail force, gear-meshing force, and dynamic transmission error (DTE) escalate. Furthermore, an increase in the order of wheel polygonization leads to a rise in the vertical wheel–rail force. In contrast, the gear-meshing force and DTE exhibit distinct trends at different speeds. At a speed of 20 km/h, these parameters increase by 51.34% and 0.29%, respectively. As the speed increases, the peaks of gear-meshing force and DTE occur at the 7th-order and 3rd-order polygon, respectively, suggesting that the dynamic response of the gear-transmission system becomes more sensitive to lower-order polygon effects at higher speeds, which necessitates greater attention during operation. Additionally, the phase difference of wheel polygonization exerts a significant influence on gear-meshing force under various conditions, such as in-phase, out-of-phase, 60° phase difference, and 120° phase difference. Therefore, in engineering applications, it is essential to consider the phase difference of wheel polygonization to alleviate excessive gear-meshing forces and ensure stable transmission performance. The findings of this paper offer insights into the dynamic evaluation and wheelset re-profiling of gear-transmission systems in urban railway vehicles. Full article
(This article belongs to the Special Issue Research and Application of Rail Vehicle Technology)
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22 pages, 7512 KiB  
Article
The Structural Design and Optimization of a Railway Fastener Nut Disassembly and Assembly Machine
by Xiangang Cao, Guoyin Chen, Mengzhen Zuo, Jiasong Zang, Peng Wang and Xudong Wu
Machines 2025, 13(4), 322; https://doi.org/10.3390/machines13040322 - 15 Apr 2025
Viewed by 187
Abstract
During the maintenance of railway fasteners, there are issues with the current nut disassembly and assembly operation, including low efficiency, heavy reliance on manual labor, and high physical strain. A mechanical device has been designed to move along the railway track while identifying [...] Read more.
During the maintenance of railway fasteners, there are issues with the current nut disassembly and assembly operation, including low efficiency, heavy reliance on manual labor, and high physical strain. A mechanical device has been designed to move along the railway track while identifying and locating the center of the nut to perform disassembly and assembly operations. First, based on the nut disassembly and assembly process and the operating environment, the structure of the equipment was designed. This machine can simultaneously disassemble and assemble all the nuts on a single rail tie and accommodate position errors and deviations of spiral spikes. Secondly, to verify the structural reliability of the designed machine, a static simulation analysis was conducted on the key load-bearing structures under extreme operating conditions. Based on the simulation results, a lightweight design was applied to the machine’s carrier platform. The performance of the nut assembly and disassembly mechanism was optimized based on the Kriging model and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimized machine reduced its mass by 21.7% and increased its strength by more than 30%. A transient analysis was also conducted on the optimized machine structure, further validating its strength. Finally, based on the design and optimization results, a physical prototype of the nut disassembly machine was constructed and tested. The results show that the device can efficiently perform nut disassembly and assembly tasks on the railway track. Both the mechanical structure’s reliability and functionality meet the design objectives and requirements, demonstrating significant application value for promoting the intelligent maintenance of railways. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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19 pages, 4650 KiB  
Article
Simulation Analysis of an Electric Locomotive with a Hydraulic Wheelset Guidance System for Improved Performance in Curved Tracks
by Jan Kalivoda
Machines 2025, 13(4), 321; https://doi.org/10.3390/machines13040321 - 14 Apr 2025
Viewed by 103
Abstract
A reduction of forces acting between the railway track and the vehicle is one of the key issues in the design of modern rolling stock. Because the capabilities of reducing wheel–rail contact forces in track curves by conventional methods are encountered at their [...] Read more.
A reduction of forces acting between the railway track and the vehicle is one of the key issues in the design of modern rolling stock. Because the capabilities of reducing wheel–rail contact forces in track curves by conventional methods are encountered at their limits, innovative approaches in the design of vehicle suspension and wheelset guidance occur. Among them, an active wheelset steering appears to be very promising. However, an active wheelset steering system is rather complicated and expensive and raises many safety issues. Therefore, a passive hydraulic system that links longitudinal motions of axle boxes is proposed. The system is relatively simple and, compared to the active wheelset steering, does not need any energy supply or sensor system for the detection of a track shape. Two arrangements of the hydraulic system had been proposed and implemented in a simulation model. The simulation model is based on a cosimulation of two separate models, a multibody model of an electric locomotive, and a model of the hydraulic system. The goal of this study is to evaluate the contribution of the hydraulic system to the natural radial alignment of wheelsets in curves and thus to reduce the wear of wheels and to determine the parameters of the hydraulic system to maximize the wear reduction benefits while minimizing a decrease in critical speed. Simulations of a vehicle running in various scenarios, including a run in a real track section of a length of 20 km, have been performed. As a criterion for the wear of wheels and rails, a T-gamma wear number was used, from which a sum of frictional work in wheel–rail contacts was calculated. The results of the simulations and the comparison of hydraulic axle box connection systems and a standard locomotive are presented and discussed in the paper. The results obtained confirmed a significant potential benefit of the proposed hydraulic system in reducing wheel wear on curved tracks. Full article
(This article belongs to the Section Vehicle Engineering)
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16 pages, 7860 KiB  
Article
Optimized Variational Mode Decomposition and Convolutional Block Attention Module-Enhanced Hybrid Network for Bearing Fault Diagnosis
by Bin Yuan, Lei Lei and Suifan Chen
Machines 2025, 13(4), 320; https://doi.org/10.3390/machines13040320 - 14 Apr 2025
Viewed by 156
Abstract
Accurate fault diagnosis remains a critical but unresolved issue in predictive maintenance, as industrial environments typically involve large amounts of electromagnetic interference and mechanical noise that can severely degrade the signal quality. In this study, we propose an innovative diagnostic framework to address [...] Read more.
Accurate fault diagnosis remains a critical but unresolved issue in predictive maintenance, as industrial environments typically involve large amounts of electromagnetic interference and mechanical noise that can severely degrade the signal quality. In this study, we propose an innovative diagnostic framework to address the challenging problem of bearing fault diagnosis in vibration signals under complex noise conditions. We develop the VMD-CNN-BiLSTM-CBAM model by systematically integrating the variational mode decomposition (VMD), convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and convolutional block attention module (CBAM). The framework starts with VMD-based signal decomposition, which effectively separates the noise component from the bearing vibration features. Based on this denoising, a CNN architecture is employed to extract multi-scale spatio-temporal features through its hierarchical learning mechanism. The subsequent BiLSTM layer captures bidirectional temporal dependencies to model fault-evolution patterns, while the CBAM module strategically highlights key diagnostic features through adaptive channel spatial attention. Experimental validation using the Case Western Reserve University and Jiangnan University bearing datasets demonstrates the excellent performance of the model, with average accuracies of 99.76% and 99.40%, respectively. Finally, additional validation through our customized testbed confirms the usefulness of the model with an average accuracy of 99.70%. These results demonstrate that the proposed approach greatly improves fault diagnosis in noisy industrial environments through its synergistic architectural design and enhanced noise immunity. Full article
(This article belongs to the Special Issue Advanced Signal Processing Methods and Deep Neural Networks for Machine Fault Diagnosis)
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19 pages, 2177 KiB  
Article
Current- and Vibration-Based Detection of Misalignment Faults in Synchronous Reluctance Motors
by Angela Navarro-Navarro, Vicente Biot-Monterde, Jose E. Ruiz-Sarrio and Jose A. Antonino-Daviu
Machines 2025, 13(4), 319; https://doi.org/10.3390/machines13040319 - 14 Apr 2025
Viewed by 175
Abstract
Misalignment faults in drive systems occur when the motor and load are not properly aligned, leading to deviations in the centerlines of the coupled shafts. These faults can cause significant damage to bearings, shafts, and couplings, making early detection essential. Traditional diagnostic techniques [...] Read more.
Misalignment faults in drive systems occur when the motor and load are not properly aligned, leading to deviations in the centerlines of the coupled shafts. These faults can cause significant damage to bearings, shafts, and couplings, making early detection essential. Traditional diagnostic techniques rely on vibration monitoring, which provides insights into both mechanical and electromagnetic fault signatures. However, its main drawback is the need for external sensors, which may not be feasible in certain applications. Alternatively, motor current signature analysis (MCSA) has proven effective in detecting faults without requiring additional sensors. This study investigates misalignment faults in synchronous reluctance motors (SynRMs) by analyzing both vibration and current signals under different load conditions and operating speeds. Fast Fourier transform (FFT) is applied to extract characteristic frequency components linked to misalignment. Experimental results reveal that the amplitudes of rotational frequency harmonics (1xfr, 2xfr, and 3xfr) increase in the presence of misalignment, with 1xfr exhibiting the most stable progression. Additionally, acceleration-based vibration analysis proves to be a more reliable diagnostic tool compared to velocity measurements. These findings highlight the potential of combining current and vibration analysis to enhance misalignment detection in SynRMs, improving predictive maintenance strategies in industrial applications. Full article
(This article belongs to the Special Issue New Advances in Synchronous Reluctance Motors)
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18 pages, 4367 KiB  
Article
Efficient Real-Time Tool Chatter Detection Through Bandpass Filtering
by Javier Arenas, Jorge Martínez de Alegría, Patxi X. Aristimuño and Vicente Gómez
Machines 2025, 13(4), 318; https://doi.org/10.3390/machines13040318 - 14 Apr 2025
Viewed by 155
Abstract
Tool Chatter or Self-Excited Vibration is a common issue in machining processes. This phenomenon arises due to various factors, such as tool rigidity, depth of cut, spindle speed, etc., leading to poor surface finish, excessive tool wear, and premature deterioration of machine components. [...] Read more.
Tool Chatter or Self-Excited Vibration is a common issue in machining processes. This phenomenon arises due to various factors, such as tool rigidity, depth of cut, spindle speed, etc., leading to poor surface finish, excessive tool wear, and premature deterioration of machine components. To prevent tool chatter, a real-time chatter detection algorithm was developed using a low-cost accelerometer in combination with internal machine variables. The algorithm operates without requiring a prior model of the specific tool characteristics, making it capable of detecting chatter by simply knowing the number of teeth of the active tool. Furthermore, the implementation of the detection algorithm meets the strict requirements of real-time embedded systems, ensuring high determinism, low latency, and minimal computational cost. This enables efficient and optimal integration into the machine. The developed chatter detection system was validated through machine-based experimental testing. Full article
(This article belongs to the Special Issue Sensors and Signal Processing in Manufacturing Processes)
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18 pages, 2132 KiB  
Article
Intelligent Casting Quality Inspection Method Integrating Anomaly Detection and Semantic Segmentation
by Min-Chieh Chen, Shih-Yu Yen, Yue-Feng Lin, Ming-Yi Tsai and Ting-Hsueh Chuang
Machines 2025, 13(4), 317; https://doi.org/10.3390/machines13040317 - 13 Apr 2025
Viewed by 178
Abstract
Wind power generation plays an important role in renewable energy, and the core casting components have extremely high requirements for precision and quality. In actual practice, we found that an insufficient workforce limits traditional manual inspection methods and often creates difficulty in unifying [...] Read more.
Wind power generation plays an important role in renewable energy, and the core casting components have extremely high requirements for precision and quality. In actual practice, we found that an insufficient workforce limits traditional manual inspection methods and often creates difficulty in unifying quality judgment standards. Customized optical path design is often required, especially when conducting internal and external defect inspections, which increases overall operational complexity and reduces inspection efficiency. We developed an automated optical inspection (AOI) system to address these challenges. The system integrates a semantic segmentation neural network to handle external surface detection and an anomaly detection model to detect internal defects. In terms of internal defect detection, the GC-AD-Local model we tested achieved 100% accuracy on experimental images, and the results were relatively stable. In the external detection part, we compared five different semantic segmentation models and found that MobileNetV2 performed the best in terms of average accuracy (65.8%). It was incredibly stable when dealing with surface defects with significant shape variations, and the prediction results were more consistent, making it more suitable for introduction into actual production line applications. Overall, this AOI system boosts inspection efficiency and quality consistency, reduces reliance on manual experience, and is of great assistance in quality control and process intelligence for wind power castings. We look forward to further expanding the amount of data and improving the generalization capabilities of the model in the future, making the system more complete and suitable for practical applications. 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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26 pages, 12604 KiB  
Article
Investigation of Lattice Geometries Formed by Metal Powder Additive Manufacturing for Energy Absorption: A Comparative Study on Ti6Al4V, Inconel 718, and AISI 316L
by Ömer Faruk Çakır and Mehmet Erdem
Machines 2025, 13(4), 316; https://doi.org/10.3390/machines13040316 - 13 Apr 2025
Viewed by 308
Abstract
Impact absorbers are needed in many different areas in terms of energy absorption and crashworthiness. While the design of these structures is expected to increase mechanical performance, they are expected to be lightweight, and when evaluated in this context, lattice structures come to [...] Read more.
Impact absorbers are needed in many different areas in terms of energy absorption and crashworthiness. While the design of these structures is expected to increase mechanical performance, they are expected to be lightweight, and when evaluated in this context, lattice structures come to the fore. In this study, impact absorbers, also known as crash boxes, consisting of lattice structures designed to increase energy absorption performance were fabricated by a new manufacturing method, metal powder additive manufacturing, and their mechanical performance was experimentally investigated under quasi-static axial loading, and energy absorption data were obtained. The specimens were designed from Ti6Al4V, INC 718, and AISI 316L materials by forming 18 matrix structures with square and hexagonal geometries. According to this study, the lattice structures absorbed up to 4.5 times more energy than the shell structures of a similar material group. According to the normalized values among all samples, the hexagonal sample made of Ti6Al4V material showed 4.3 times higher energy absorption efficiency. The AISI 316L material showed the best crushing performance due to its ductile structure. Full article
(This article belongs to the Section Vehicle Engineering)
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15 pages, 5408 KiB  
Article
Research on the Configuration of Wheeled Mobile Welding Robots Under Multiple Working Conditions
by Shuyan Yao, Long Xue, Jiqiang Huang, Yong Zou and Ruiying Zhang
Machines 2025, 13(4), 315; https://doi.org/10.3390/machines13040315 - 12 Apr 2025
Viewed by 209
Abstract
Mobile welding robots have attracted considerable attention due to their flexible movement and robust adaptability, offering substantial market potential. However, the complexity of their operational conditions poses specific demands on robot configurations, with no established design methodology available at the time. To address [...] Read more.
Mobile welding robots have attracted considerable attention due to their flexible movement and robust adaptability, offering substantial market potential. However, the complexity of their operational conditions poses specific demands on robot configurations, with no established design methodology available at the time. To address this challenge, we constructed Lagrange’s equations for wheeled mobile welding robots. Through simplification and deduction analysis, we concluded that larger wheeled mobile welding robots are suited for paths with larger curvature radii, whereas smaller ones are more appropriate for paths with smaller curvature radii. Based on the above analysis and considering the load-bearing capacity of the robot, we proposed a configuration design method for wheeled mobile welding robots, evolving from large to small and micro wheels, including track constraints and wheel number adjustments. Subsequently, prototype robots, including magnetic wheel, tracked, and flexible contour tracing mobile welding robots, have been developed to accommodate various operational conditions. Welding experiments demonstrate that these three configurations, distinguished by their travel path curvature radii, can effectively meet the mobile welding requirements of their respective environments. The effectiveness of the configuration design of wheeled mobile welding robots under different working conditions has been verified. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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34 pages, 3804 KiB  
Article
EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI
by Md. Ehsanul Haque, Mahe Zabin and Jia Uddin
Machines 2025, 13(4), 314; https://doi.org/10.3390/machines13040314 - 12 Apr 2025
Viewed by 553
Abstract
As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in [...] Read more.
As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. In addition, existing models are also heavy and inefficient. A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. We further optimize the models and their combination to a hybrid Soft Voting Classifier. These techniques were applied to a dataset of 40,040 data entries that had been simulated by a Variable Frequency Drive (VFD) model. We evaluated eight machine learning models, and our proposed Soft Voting Classifier has the highest test accuracy of 94.52% and a Kappa score of 0.9210 on diagnostic performance. Also, the model has minimal memory usage and low inference latency. In addition, Local Interpretable Model-Agnostic Explanations (LIME) were used to improve transparency and gain an understanding of decisions made through the Soft Voting Classifier. Also, the framework was validated by an additional real-world dataset, thereby further confirming its robustness and consistency in performance for different conditions, which indicates the generalizability of the framework in real-world applications. RFE-CV is found to be very effective in feature selection and helps to construct a lightweight and cost-effective ensemble voting model for enhancing fault diagnosis for EV Drive Motors, overcoming its unsatisfactory transparency, accuracy, and computational efficiency. Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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19 pages, 1358 KiB  
Article
Friction Monitoring in Kaplan Turbines
by Lars-Johan Sandström, Kim Berglund, Pär Marklund and Gregory F. Simmons
Machines 2025, 13(4), 313; https://doi.org/10.3390/machines13040313 - 11 Apr 2025
Viewed by 99
Abstract
Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropower. This study suggests a model [...] Read more.
Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropower. This study suggests a model approach for friction monitoring of the bearings inside the Kaplan turbine’s hub. The approach is developed for when normal and anomalous data exist. The study compares isolation forest (iForest), local outlier factor (LOF), one-class support vector machine (OC-SVM), and Mahalanobis distance (MD) for anomaly detection, where iForest and OC-SVM appear to be good choices due to their robust performance. A moving decision filter (MDF) is fed with the output from the anomaly detection models to classify the data as normal or anomalous. The parameters in the MDF are optimized with Bayesian optimization to increase the performance of the models. The approach is tested using data from two actual hydropower turbines. The study shows that the model approach works for both turbines. However, the parameter optimization must be performed separately for each turbine. Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
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18 pages, 6221 KiB  
Article
A Study on the Wear Characteristics of a Point Contact Pair of Angular Contact Ball Bearings Under Mixed Lubrication
by Yongjian Yu, Zifan Dong, Yujun Xue, Haichao Cai and Jun Ye
Machines 2025, 13(4), 312; https://doi.org/10.3390/machines13040312 - 11 Apr 2025
Viewed by 172
Abstract
Under mixed lubrication, the macro size is affected by the wear of the surface roughness peaks, which results in degradation of the bearing accuracy. To study the wear characteristics of rolling bearings under mixed lubrication, based on the elastohydrodynamic lubrication theory and Archard [...] Read more.
Under mixed lubrication, the macro size is affected by the wear of the surface roughness peaks, which results in degradation of the bearing accuracy. To study the wear characteristics of rolling bearings under mixed lubrication, based on the elastohydrodynamic lubrication theory and Archard wear model, and considering the coupling of the oil film and roughness, a wear prediction model of angular contact ball bearings under mixed lubrication was established, and the influence of the working parameters and hardness on bearing wear was analyzed. The results show that the wear depth of the outer grove increases with an increase in the load, or a decrease in the rotational speed or the initial viscosity of lubricating oil. The load has the most significant effect on the wear depth of the outer grove. There is a critical value for the load, rotational speed, and initial viscosity of the lubricating oil, which varies with the parameters of other working conditions and the hardness of the materials. When the increase in load exceeds the critical value or the rotational speed and initial viscosity of lubricating oil are less than the critical value, the outer groove fails because the wear depth exceeds the critical value of wear depth. The ratio of the load on the rolling element to the hardness of the outer grove at different entrainment speeds and initial viscosities of lubricating oil can be used to predict the wear degree of the outer grove. When the ratio is greater than a certain threshold, the outer grove is faulted owing to wear, and the threshold decreases with an increase in the initial viscosity of lubricating oil or the decrease in rotational speed. Full article
(This article belongs to the Section Friction and Tribology)
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28 pages, 8808 KiB  
Article
Design and Dimension Optimization of Rigid–Soft Hand Function Rehabilitation Robots
by Rui Zhang, Meng Ning, Yuqian Wang and Jun Yang
Machines 2025, 13(4), 311; https://doi.org/10.3390/machines13040311 - 11 Apr 2025
Viewed by 144
Abstract
The growing population of hand dysfunction patients necessitates advanced rehabilitation technologies. Current robotic solutions face limitations in motion compatibility and systematic design frameworks. This study develops a rigid–soft coupling rehabilitation robot integrating linkage mechanisms with soft components. A machine vision system captures natural [...] Read more.
The growing population of hand dysfunction patients necessitates advanced rehabilitation technologies. Current robotic solutions face limitations in motion compatibility and systematic design frameworks. This study develops a rigid–soft coupling rehabilitation robot integrating linkage mechanisms with soft components. A machine vision system captures natural grasping trajectories, analyzed through polynomial regression. Hierarchical constraint modeling and an improved artificial bee colony algorithm optimize linkage dimensions and control strategies, achieving enhanced human–robot kinematic matching. Finite element simulations using a Yeoh hyperelastic model refine soft component geometry for balance compliance and coordination. Prototype validation demonstrates high-precision trajectory tracking, grasping across 20–70 mm objects, and steady fingertip forces during training. Experimental results confirm the system’s ability to replicate physiological motion patterns and adapt to multiple rehabilitation scenarios. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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39 pages, 9094 KiB  
Article
Analysis of the Interaction of Robots as Part of a Robotic System for Biomaterial Aliquotation
by Sergey Khalapyan, Larisa Rybak, Dmitry Malyshev, Vladislav Cherkasov and Vladislav Vorobyev
Machines 2025, 13(4), 310; https://doi.org/10.3390/machines13040310 - 11 Apr 2025
Viewed by 244
Abstract
The paper considers the problem of interaction between robots with parallel and serial structures that are part of a robotic system for aliquoting biomaterials. An approach to selecting the relative position and limiting the ranges of movement of manipulators working nearby to avoid [...] Read more.
The paper considers the problem of interaction between robots with parallel and serial structures that are part of a robotic system for aliquoting biomaterials. An approach to selecting the relative position and limiting the ranges of movement of manipulators working nearby to avoid collisions is presented. The elimination of collisions is ensured by the absence of intersections between work safety zones (a 3D space within which all manipulator links can be located for a given range of robot positions). Universal algorithms for determining work safety zones were developed, including for an individual manipulator and taking into account the work safety zone of the manipulator installed nearby and other obstacles. An analysis of the workspace and safety zones was performed, taking into account both individual limitations and limitations associated with collaboration within the system. The issue of adapting control algorithms of the robotic system to external disturbances in order to minimize the time spent on executing a given trajectory was addressed. In particular, the meeting point (interaction) of robots solving the problem of biomaterial aliquotation was optimized depending on the workload level of each robot. Experiments were carried out to verify the developed approaches. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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18 pages, 11654 KiB  
Article
Analysis of the Fracture and the Repair of the Screw Spindle of a Friction Screw Press
by Rade Vasiljević and Dragan Pantelić
Machines 2025, 13(4), 309; https://doi.org/10.3390/machines13040309 - 10 Apr 2025
Viewed by 232
Abstract
The drive mechanism of a friction screw press consists of a screw transmission, a friction transmission and a belt transmission. Improper maintenance and axial misalignment of the screw spindle and the press are the main possible causes of screw spindle failure. The causes [...] Read more.
The drive mechanism of a friction screw press consists of a screw transmission, a friction transmission and a belt transmission. Improper maintenance and axial misalignment of the screw spindle and the press are the main possible causes of screw spindle failure. The causes of the screw spindle fracture are investigated in the first part of this paper. A visual examination of the screw spindle is carried out in the first step. In the second step, the chemical composition and mechanical properties of the material from which the screw spindle of the drive mechanism is made are experimentally examined, and a metallographic examination of the fracture surfaces on the screw spindle is carried out using an electronic microscope. In the second part of this paper, the effects of screw spindle disturbances on the fracture are analyzed by applying the finite element method. The third part of this paper shows how the problem of repairing the damaged screw spindle of the drive mechanism of the friction screw press is solved. Firstly, the repair solution is described. Then, a safety check of the welded joint is presented. The final part refers to the techno-economic justification of the performed repair of the screw spindle. The obtained research results are important because the same problems or similar problems could appear in machine elements of various types of machine tools. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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