Journal Description
Machines
Machines
is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI. The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Mechanical) / CiteScore - Q2 (Control and Optimization)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.5 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.1 (2023);
5-Year Impact Factor:
2.2 (2023)
Latest Articles
A Composite Vision-Based Method for Post-Assembly Dimensional Inspection of Engine Oil Seals
Machines 2025, 13(4), 261; https://doi.org/10.3390/machines13040261 (registering DOI) - 22 Mar 2025
Abstract
Addressing the challenge of manual dependency and the difficulty in automating the online detection of height discrepancies following engine oil seal assembly, this paper proposes a composite vision-based method for the post-assembly size inspection of engine oil seals. The proposed method enables non-contact,
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Addressing the challenge of manual dependency and the difficulty in automating the online detection of height discrepancies following engine oil seal assembly, this paper proposes a composite vision-based method for the post-assembly size inspection of engine oil seals. The proposed method enables non-contact, online three-dimensional measurement of oil seals already installed on the engine. To achieve accurate positioning of the inner and outer ring regions of the oil seals, the process begins with obtaining the center point and the major and minor axes through ellipse fitting, which is performed using progressive template matching and the least squares method. After scaling the ellipse along its axes, the preprocessed image is segmented using the peak–valley thresholding method to generate an annular ROI (region of interest) mask, thereby reducing the complexity of the image. By integrating three-frequency four-step phase-shifting profilometry with an improved RANSAC (random sample consensus)-based plane fitting algorithm, the height difference between the inner and outer rings as well as the press-in depth are accurately calculated, effectively eliminating interference from non-target regions. Experimental results demonstrate that the proposed method significantly outperforms traditional manual measurement in terms of speed, with the relative deviations of the height difference and press-in depth confined within 0.33% and 1.45%, respectively, and a detection success rate of 96.35% over 1415 samples. Compared with existing methods, the proposed approach not only enhances detection accuracy and efficiency but also provides a practical and reliable solution for real-time monitoring of engine oil seal assembly dimensions, highlighting its substantial industrial application potential.
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(This article belongs to the Special Issue Visual Measurement and Intelligent Robotic Manufacturing)
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Open AccessArticle
Lean Tools Implementation Model in Shipbuilding Processes Under Conditions of Predominantly Custom Production
by
Zoran Kunkera, Biserka Runje, Nataša Tošanović and Neven Hadžić
Machines 2025, 13(4), 260; https://doi.org/10.3390/machines13040260 (registering DOI) - 22 Mar 2025
Abstract
The European shipbuilding industry is primarily active in the niches of building vessels with high added value characterized by individual demand or eventual orders in smaller series—the authors approach this research motivated by the desire to contribute to maintaining its competitiveness on the
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The European shipbuilding industry is primarily active in the niches of building vessels with high added value characterized by individual demand or eventual orders in smaller series—the authors approach this research motivated by the desire to contribute to maintaining its competitiveness on the world market. To enhance business processes, shipyards have at their disposal, among others, digital technologies and Lean tools. However, the production of highly complex products in a business environment with complex inter-process relations among a large number of stakeholders also implies a highly demanding project of Lean methodology implementation. And according to the literature gap and available archival data, the outcome is very uncertain. Therefore, the authors conduct this research for the purpose of overcoming the risk of failure in completing the Lean implementation process with the aim of contributing to the transformation of the shipbuilding system into a smart and sustainable, or climate-neutral, one. As experts in the field of research and based on interviews with representatives of one of the European shipyards, the authors develop a Lean process management implementation model adapted not only to custom production in shipbuilding but also to other industries with similar characteristics. The model theoretically results not only in the successful closure of the Lean transformation process in an optimal time and at low costs but also in the simultaneous continuous improvement of shipbuilding processes during the implementation period. Moreover, the neutral influence of the business system’s organizational structure on the presented model adds originality to this study.
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(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods, 2nd Edition)
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Open AccessArticle
Analysis of Electromagnetic Vibration in Permanent Magnet Motors Based on Random PWM Technology
by
Chi Ma, Yongxiang Wang, Huang Chen, Jianfeng Hong and Yi Wang
Machines 2025, 13(4), 259; https://doi.org/10.3390/machines13040259 (registering DOI) - 22 Mar 2025
Abstract
High vibration noise limits the application of permanent magnet motors in electric locomotive traction. This paper focuses on the high-frequency electromagnetic vibration in traction permanent magnet motors introduced by inverters. It explores the impact of periodic and random switching frequency pulse-width modulation (PWM)
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High vibration noise limits the application of permanent magnet motors in electric locomotive traction. This paper focuses on the high-frequency electromagnetic vibration in traction permanent magnet motors introduced by inverters. It explores the impact of periodic and random switching frequency pulse-width modulation (PWM) schemes on the high-frequency electromagnetic vibration performance of permanent magnet motors. The studied works are as follows: (1) The sources of higher-order harmonic components in the stator current are analyzed, and the characteristics of electromagnetic forces generated by these higher-order harmonic currents are studied. (2) The principles for suppressing high-frequency electromagnetic vibrations through random PWM are introduced. (3) The impact of the random switching frequency on higher-order harmonic currents in permanent magnet motors is analyzed through simulations. (4) The comprehensive experimental validation and evaluation of the random PWM technique are conducted on a permanent magnet motor. The results show that the vibration near the carrier frequency can be effectively weakened, but the overall vibration level has not been effectively reduced.
Full article
(This article belongs to the Special Issue Vibration Detection of Induction and PM Motors)
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Toward Inclusive Smart Cities: Sound-Based Vehicle Diagnostics, Emergency Signal Recognition, and Beyond
by
Amr Rashed, Yousry Abdulazeem, Tamer Ahmed Farrag, Amna Bamaqa, Malik Almaliki, Mahmoud Badawy and Mostafa A. Elhosseini
Machines 2025, 13(4), 258; https://doi.org/10.3390/machines13040258 (registering DOI) - 21 Mar 2025
Abstract
Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses
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Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes the significance of sound-based diagnostics for real-time detection of faults through analyzing sounds directly generated by vehicles, such as engine or brake noises, and the classification of external emergency sounds, like sirens, relevant to vehicle safety. Secondly, this paper introduces a novel dataset encompassing vehicle fault sounds, emergency sirens, and environmental noises specifically curated to address the absence of such specialized datasets. A comprehensive framework is proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, and Chromatograms), and classification using 11 models. Evaluations using both compact (52 features) and expanded (126 features) representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar classes like Universal Joint Failure, Knocking, and Pre-ignition Problem remain challenging. Logistic Regression yielded the highest accuracy of 86.5% for the vehicle fault dataset (DB1) using compact features, while neural networks performed best for datasets DB2 and DB3, achieving 88.4% and 85.5%, respectively. In the second scenario, a Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach is proposed, significantly enhancing accuracy to 91.04% for DB1, 88.85% for DB2, and 86.85% for DB3. These results highlight the effectiveness of the proposed methods in addressing key ITS limitations and enhancing accessibility for individuals with disabilities through auditory-based vehicle diagnostics and emergency recognition systems.
Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)
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Open AccessArticle
Estimation of Vibration-Induced Fatigue Damage in a Tracked Vehicle Suspension Arm at Critical Locations Under Real-Time Random Excitations
by
Ayaz Mahmood Khan, Muhammad Shahid Khalil and Muhammad Muzammil Azad
Machines 2025, 13(4), 257; https://doi.org/10.3390/machines13040257 - 21 Mar 2025
Abstract
Probabilistic random vibration can speed up wear and tear on several components of the tracked vehicle, including the track system, drivetrain, and suspension. Extended exposure to high levels of vibration can cause structural damage to the vehicle frame and other critical components. Assessing
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Probabilistic random vibration can speed up wear and tear on several components of the tracked vehicle, including the track system, drivetrain, and suspension. Extended exposure to high levels of vibration can cause structural damage to the vehicle frame and other critical components. Assessing random vibration in track vehicles requires a comprehensive approach that considers both the root causes and potential consequences of the vibrations. This random vibration significantly influences the structural performance of suspension arm which is key component of tracked vehicle. Damage due to fatigue is conventionally computed using time domain loaded signals with stress or strain data. This approach generally holds good when loading is periodic in nature but not be a good choice when dynamic resonance is in process. In this case an alternative frequency domain fatigue life analysis is used where the random loads and responses are characterized using a concept called Power spectral density (PSD). The current research article investigates the fatigue damage characteristics of a tracked vehicle suspension arm considering the dynamic loads induced by traversing on smooth and rough terrain. The analysis focusses on assessing the damage and stress response Power spectral density (PSD) ground-based excitation which is termed PSD-G acceleration. Quasi Static Finite Element Method based approach is used to simulate the operational conditions experienced by the suspension arm. Through comprehensive numerical simulations, the fatigue damage accumulation patterns are examined, providing insights into the structure integrity and performance durability of the suspension arm under varying operational scenarios. The obtained stress response PSD data and fatigue damage showed that the rough terrain response exhibits higher stresses in suspension arm. The accumulated stresses in case of rough terrain may prompt to brittle failure at specific critical locations. This research contributes to the advancement to the design and optimization strategies for tracked vehicle components enhancing their reliability and longevity in demanding operational environments.
Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
Open AccessArticle
Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network
by
Zishuo Wang, Hongwei Cui, Shuning Liang, Tao Ding and Xingquan Gao
Machines 2025, 13(4), 256; https://doi.org/10.3390/machines13040256 - 21 Mar 2025
Abstract
In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining.
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In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%.
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(This article belongs to the Section Industrial Systems)
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Boids-Based Integration Algorithm for Formation Control and Obstacle Avoidance in Unmanned Aerial Vehicles
by
Jing Lu, Jiayi Zhao and Junda Niu
Machines 2025, 13(4), 255; https://doi.org/10.3390/machines13040255 - 21 Mar 2025
Abstract
Unmanned Aerial Vehicles (UAVs), as widely used tools, can achieve better efficiency when integrated into a multi-UAV system than individual, dispersed units. Obstacle avoidance and formation control are fundamental requirements for such systems. The Boids algorithm, a biomimetic model suitable for swarming, serves
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Unmanned Aerial Vehicles (UAVs), as widely used tools, can achieve better efficiency when integrated into a multi-UAV system than individual, dispersed units. Obstacle avoidance and formation control are fundamental requirements for such systems. The Boids algorithm, a biomimetic model suitable for swarming, serves as the foundation for this study. This paper proposes a novel integrated algorithm based on Boids that can be applied to multi-UAV systems for obstacle avoidance and formation control. The algorithm enables the multi-UAV system to automatically form formations, autonomously avoid obstacles, and recover formations rapidly. In this algorithm, each UAV functions as an agent within the system that is capable of independently collecting and sharing information. Each agent can make independent decisions to enter either the formation mode or the obstacle avoidance mode based on external environmental factors. The formation mode utilizes the virtual structure method to guide UAVs to their virtual formation positions. In the obstacle avoidance mode, the artificial potential field method is employed to ensure that each UAV maintains a safe distance from other UAVs that pose collision risks and various complex obstacles, regardless of their number. Simulation experiments were conducted on the Unity platform, varying the number of UAVs and the formation shapes. The results verified that the algorithm operates correctly, stably, and in a timely manner, demonstrating good performance.
Full article
(This article belongs to the Special Issue Advanced Aircraft Aerodynamics, Flight Stability, Stabilization and Control of Flying Vehicles)
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Open AccessArticle
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
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
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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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Open AccessArticle
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
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
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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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Open AccessArticle
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
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
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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.
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(This article belongs to the Section Turbomachinery)
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Open AccessArticle
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
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
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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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Open AccessArticle
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
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
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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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Open AccessReview
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
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
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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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Open AccessArticle
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
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
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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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Open AccessArticle
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
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
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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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Open AccessReview
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
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
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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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Open AccessArticle
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
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
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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.
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(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Open AccessArticle
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
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
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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.
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(This article belongs to the Section Vehicle Engineering)
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Open AccessArticle
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
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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,
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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%.
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Open AccessArticle
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
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
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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.
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(This article belongs to the Section Machines Testing and Maintenance)
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