Machines

19 pages, 2299 KiB

Open AccessArticle

A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application

by Valentina De Simone, Valentina Di Pasquale, Joanna Calabrese, Salvatore Miranda and Raffaele Iannone

Machines 2025, 13(7), 602; https://doi.org/10.3390/machines13070602 (registering DOI) - 12 Jul 2025

Abstract

Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to [...] Read more.

Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs. Full article

(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)

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18 pages, 806 KiB

Open AccessArticle

Decoupled Reinforcement Hybrid PPO–Sliding Control for Underactuated Systems: Application to Cart–Pole and Acrobot

by Yi-Jen Mon

Machines 2025, 13(7), 601; https://doi.org/10.3390/machines13070601 - 11 Jul 2025

Abstract

Underactuated systems, such as the Cart–Pole and Acrobot, pose significant control challenges due to their inherent nonlinearity and limited actuation. Traditional control methods often struggle to achieve stable and optimal performance in these complex scenarios. This paper presents a novel stable reinforcement learning [...] Read more.

Underactuated systems, such as the Cart–Pole and Acrobot, pose significant control challenges due to their inherent nonlinearity and limited actuation. Traditional control methods often struggle to achieve stable and optimal performance in these complex scenarios. This paper presents a novel stable reinforcement learning (RL) approach for underactuated systems, integrating advanced exploration–exploitation mechanisms and a refined policy optimization framework to address instability issues in RL-based control. The proposed method is validated through extensive experiments on two benchmarkunderactuated systems: the Cart–Pole and Acrobot. In the Cart–Pole task, the method achieves long-term balance with high stability, outperforming traditional RL algorithms such as the Proximal Policy Optimization (PPO) in average episode length and robustness to environmental disturbances. For the Acrobot, the approach enables reliable swing-up and near-vertical stabilization but cannot achieve sustained balance control beyond short time intervals due to residual dynamics and control limitations. A key contribution is the development of a hybrid PPO–sliding mode control strategy that enhances learning efficiency and stabilities for underactuated systems. Full article

(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)

26 pages, 28244 KiB

Open AccessArticle

Addressing Local Minima in Path Planning for Drones with Reinforcement Learning-Based Vortex Artificial Potential Fields

by Boyi Xiao, Lujun Wan, Xueyan Han, Zhilong Xi, Chenbo Ding and Qiang Li

Machines 2025, 13(7), 600; https://doi.org/10.3390/machines13070600 - 11 Jul 2025

Abstract

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper [...] Read more.

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article

(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)

22 pages, 5935 KiB

Open AccessArticle

Aeroelastic Study of Downwind and Upwind Configurations Under Different Power Levels of Wind Turbines

by Zixuan Sun, Zhenye Sun, Yusheng Xia, Wenzhong Shen, Weijun Zhu and Esteban Ferrer

Machines 2025, 13(7), 599; https://doi.org/10.3390/machines13070599 - 11 Jul 2025

Abstract

Downwind wind turbines offer potential for reduced blade loads and lighter designs, yet systematic aeroelastic comparisons against upwind configurations remain limited, especially for multi-megawatt scales. This study conducts comprehensive OpenFAST simulations of the IEA 15 MW reference turbine in both configurations, contextualized against [...] Read more.

Downwind wind turbines offer potential for reduced blade loads and lighter designs, yet systematic aeroelastic comparisons against upwind configurations remain limited, especially for multi-megawatt scales. This study conducts comprehensive OpenFAST simulations of the IEA 15 MW reference turbine in both configurations, contextualized against smaller turbines (2.1, 5, and 10 MW). Scaling trends reveal that, with the increase in turbine size, the disadvantage of the downwind turbine (higher flapwise and edgewise fatigue load) is gradually disappearing and even becomes an advantage. However, downwind configurations amplify tower base loads significantly. These results highlight scalable benefits for blade loads but underscore critical trade-offs requiring tower reinforcement. Optimizing rotor-nacelle mass distribution emerges as a key pathway to mitigate tower penalties while leveraging blade-load alleviation for larger downwind turbines. Full article

(This article belongs to the Special Issue Advancements in Fluid-Structure Interaction Vibration Control Technology)

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14 pages, 3914 KiB

Open AccessArticle

Thermal Error Analysis of Hydrostatic Turntable System

by Jianlei Wang, Changhui Ke, Kaiyu Hu and Jun Zha

Machines 2025, 13(7), 598; https://doi.org/10.3390/machines13070598 - 10 Jul 2025

Abstract

The thermal error caused by the temperature rise in the service condition of the hydrostatic turntable system has a significant impact on the accuracy of the machine tool. The temperature rise is mainly caused by the friction heat of the bearing and the [...] Read more.

The thermal error caused by the temperature rise in the service condition of the hydrostatic turntable system has a significant impact on the accuracy of the machine tool. The temperature rise is mainly caused by the friction heat of the bearing and the heat of the oil pump. The amount of heat mainly depends on the working parameters, such as the oil supply pressure and the oil film gap. The unreasonable parameter setting will cause the reduction in the internal flow of the hydrostatic bearing and the increase in the oil pump power, which makes the heat of the lubricating oil increase and the heat dissipation capacity decrease during the movement. Based on the established hydrostatic turntable system, in order to explore the main influencing factors of its thermal error, the temperature field model of the component is established by calculating the thermal balance of the key components of the system. The thermal coupling analysis of the component is carried out by using the model, and the temperature rise, deformation and strain curves of the hydrostatic turntable system under different service conditions are obtained. The results show that with the increase in the temperature, the deformation and strain of the bearing increase monotonously. For every 1 °C increase, the total deformation of the bearing increases by about 0.285

μ m

. The higher the oil supply pressure, the higher the temperature rise in the system. The larger the oil film gap, the lower the temperature rise in the system. The oil supply pressure has a greater influence on the temperature rise and thermal deformation than the oil film gap. This study provides a valuable reference for reducing the thermal error generated by the hydraulic turntable of the ultra-precision lathe. Full article

(This article belongs to the Special Issue Advanced Design, Manufacturing, and Applications of Precision Machine Tools)

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24 pages, 5982 KiB

Open AccessArticle

Study on Friction and Wear Performance of Bionic Function Surface in High-Speed Ball Milling

by Youzheng Cui, Xinmiao Li, Minli Zheng, Haijing Mu, Chengxin Liu, Dongyang Wang, Bingyang Yan, Qingwei Li, Fengjuan Wang and Qingming Hu

Machines 2025, 13(7), 597; https://doi.org/10.3390/machines13070597 - 10 Jul 2025

Abstract

During the service life of automotive panel stamping dies, the surface is often subjected to high loads and repeated friction, resulting in excessive wear. This leads to die failure, reduced machining accuracy, and decreased production efficiency. To enhance the anti-friction and wear-resistant performance [...] Read more.

During the service life of automotive panel stamping dies, the surface is often subjected to high loads and repeated friction, resulting in excessive wear. This leads to die failure, reduced machining accuracy, and decreased production efficiency. To enhance the anti-friction and wear-resistant performance of die steel surfaces, this study introduces the concept of biomimetic engineering in surface science. By mimicking microstructural configurations found in nature with outstanding wear resistance, biomimetic functional surfaces were designed and fabricated. Specifically, quadrilateral dimples inspired by the back of dung beetles, pentagonal scales from armadillo skin, and hexagonal scales from the belly of desert vipers were selected as biological prototypes. These surface textures were fabricated on Cr12MoV die steel using high-speed ball-end milling. Finite element simulations and dry sliding wear tests were conducted to systematically investigate the tribological behavior of surfaces with different dimple geometries. The results showed that the quadrilateral dimple surface derived from the dung beetle exhibited the best performance in reducing friction and wear. Furthermore, the milling parameters for this surface were optimized using response surface methodology. After optimization, the friction coefficient was reduced by 21.3%, and the wear volume decreased by 38.6% compared to a smooth surface. This study confirms the feasibility of fabricating biomimetic functional surfaces via high-speed ball-end milling and establishes an integrated surface engineering approach combining biomimetic design, efficient manufacturing, and parameter optimization. The results provide both theoretical and methodological support for improving the service life and surface performance of large automotive panel dies. Full article

(This article belongs to the Section Friction and Tribology)

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16 pages, 3015 KiB

Open AccessArticle

Energy Efficiency Analysis of Hydraulic Excavators’ Swing Drive Transmission

by Vesna Jovanović, Dragoslav Janošević, Dragan Marinković, Nikola Petrović and Boban Nikolić

Machines 2025, 13(7), 596; https://doi.org/10.3390/machines13070596 - 10 Jul 2025

Abstract

The paper provides an analysis of the energy efficiency of the swing drive system of hydraulic excavators, which integrally includes a hydraulic motor and a planetary reducer. The indicator of the drive’s energy efficiency is determined based on the efficiency of the hydraulic [...] Read more.

The paper provides an analysis of the energy efficiency of the swing drive system of hydraulic excavators, which integrally includes a hydraulic motor and a planetary reducer. The indicator of the drive’s energy efficiency is determined based on the efficiency of the hydraulic motor and the planetary reducer. The efficiency of the hydraulic motor is defined as a function of the specific flow, pressure, and the number of revolutions of the hydraulic motor. The efficiency of the reducer is determined using structural analysis of planetary gearboxes and the moment method. As an example, the results of a comparative analysis of the energy efficiency of the swing drive of a tracked hydraulic excavator, weighing 16,000 kg and having a bucket volume of 0.6 m³, are presented. From the set of possible generated variant solutions of the drive, obtained through the synthesis process based on the required torque and platform rotation speed, two extreme drive variants were selected for the analysis. In the first configuration, a hydraulic motor characterized by a low specific flow is combined with a three-stage reduction gear featuring a higher overall transmission ratio, whereas the second configuration integrates a high-specific-flow hydraulic motor with a two-stage reduction gear of a lower transmission ratio. The obtained results of the comparative analysis of the drive’s energy efficiency are presented depending on the change in the required torque and the rotational speed of the platform. Full article

(This article belongs to the Special Issue Components of Hydrostatic Drive Systems)

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34 pages, 3597 KiB

Open AccessArticle

Human Factors and Ergonomics in Sustainable Manufacturing Systems: A Pathway to Enhanced Performance and Wellbeing

by Violeta Firescu and Daniel Filip

Machines 2025, 13(7), 595; https://doi.org/10.3390/machines13070595 - 9 Jul 2025

Abstract

Human Factors and Ergonomics (HF/E) play an essential role in the development of sustainable manufacturing systems. By prioritizing worker wellbeing through the mitigation of occupational hazards and the enhancement of workplace health, HF/E contributes significantly to improved system performance. In accordance with the [...] Read more.

Human Factors and Ergonomics (HF/E) play an essential role in the development of sustainable manufacturing systems. By prioritizing worker wellbeing through the mitigation of occupational hazards and the enhancement of workplace health, HF/E contributes significantly to improved system performance. In accordance with the principles of Industry 5.0 and Society 5.0, which emphasize human-centered design and wellbeing, organizations that effectively integrate HF/E principles can achieve a competitive advantage on the market. Based on a globally recognized ranking system utilized by investors in making informed decisions, the study focuses on manufacturing companies ranked by their occupational health and safety (OHS) scores, a key criterion for assessing the social dimension of company performance. This research aims to identify and analyze top-ranked companies that explicitly highlight HF/E-related benefits within their public documents and sustainability reports. The paper investigates aspects related to the integration of AI and digital technologies to enhance safety and health in manufacturing systems, with a specific focus on human presence detection in hazardous zones, improvements in machines and equipment design, occupational risk assessments, and initiatives for enhancing worker wellbeing. The findings are expected to provide compelling evidence for companies to prioritize HF/E consideration during the design and redesign phases of sustainable manufacturing systems. The paper provides significant value to non-indexed companies by offering a dual approach for improving OHS performance, based on an empirical evaluation assessment method and practical strategies for effective OHS implementation in different manufacturing industries and countries. Full article

(This article belongs to the Special Issue Digital Technologies to Support Human Factors Engineering in Manufacturing System Design: Theory and Applications)

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26 pages, 15469 KiB

Open AccessArticle

A Research Method to Investigate the Effect of Vibration Suppression on Thin-Walled Parts of Aluminum Alloy 6061 Based on Cutting Fluid Spraying (CFS)

by Yonglin Min, Xiao Liu, Gaofeng Hu, Gang Jin, Yuanhao Ma, Yipu Bian, Yihan Xie, Mengpan Hu and Desheng Li

Machines 2025, 13(7), 594; https://doi.org/10.3390/machines13070594 - 9 Jul 2025

Abstract

This study aims to address the issues of high tool wear rate, severe deterioration of machining accuracy, and surface integrity in thin-walled part cutting processes, which are caused by vibration. To do so, this paper proposes a thin-walled part processing vibration control method [...] Read more.

This study aims to address the issues of high tool wear rate, severe deterioration of machining accuracy, and surface integrity in thin-walled part cutting processes, which are caused by vibration. To do so, this paper proposes a thin-walled part processing vibration control method based on CFS. With aluminum alloy 6061 planar thin-walled parts as the object of study, in this paper a CFS experimental platform was established, the influence of CFS on the dynamic characteristics of the thin-walled parts was analyzed, the effects of milling force and processing vibration during thin-walled part milling were investigated. The results show that compared with UCFS, CFS can significantly reduce the acceleration response amplitude of thin-walled parts and shorten their vibration decay time. When the spraying point coincides with the hammering point, the optimal vibration suppression effect is achieved at a spraying velocity V of 13 m/s, a spraying area S of 31 mm², and a spraying angle θ of 30°; the acceleration response amplitude decreases by 76.2%, and the vibration attenuation time decreases by 74.7%. This method can provide a certain support force and damping effect for thin-walled parts by CFS, thus reducing the milling force and machining vibration. Full article

(This article belongs to the Special Issue Advanced Manufacturing Processes and Technologies: Trends and Innovations)

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17 pages, 877 KiB

Open AccessArticle

Challenges in CFD Model Validation: A Case Study Approach Using ANSYS CFX and TurboGrid

by Jordan Dickenson, James M. Buick, Jovana Radulovic and James Bull

Machines 2025, 13(7), 593; https://doi.org/10.3390/machines13070593 - 8 Jul 2025

Abstract

Model validation is an essential part of CFD-based projects. Despite being successfully employed for decades, the level and extent of CFD model validation details vary significantly in the published literature, which, in turn, adversely affects the repeatability and usefulness of published models and [...] Read more.

Model validation is an essential part of CFD-based projects. Despite being successfully employed for decades, the level and extent of CFD model validation details vary significantly in the published literature, which, in turn, adversely affects the repeatability and usefulness of published models and data. This study explores the various challenges associated with validating CFD models of thermodynamic components, namely, the compressors and their performance evaluation. The methodology involves blade generation through TurboGrid and BladeGen, mesh generation to ensure computational efficiency, and pre-processing with CFX to define boundary conditions and turbulence models, all within ANSYS 2024 R1. Three case studies are discussed, each assessing different compressor configurations and common challenges encountered during the model validation stage. Based on the case studies, a number of recommendations are presented relating to best practices in terms of both the use of published materials to validate new models and the level of detail required for experimental or simulation publication to ensure they can be replicated or used to validate a new model. Full article

(This article belongs to the Special Issue Theoretical and Experimental Study on Compressor Performance)

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30 pages, 9360 KiB

Open AccessArticle

Dynamic Positioning and Optimization of Magnetic Target Based on Binocular Vision

by Jing Li, Yang Wang, Ligang Qu, Guangming Lv and Zhenyu Cao

Machines 2025, 13(7), 592; https://doi.org/10.3390/machines13070592 - 8 Jul 2025

Abstract

Aiming at the problems of visual occlusion, reduced positioning accuracy and pose loss in the dynamic scanning process of aviation large components, this paper proposes a binocular vision dynamic positioning method based on magnetic target. This method detects the spatial coordinates of the [...] Read more.

Aiming at the problems of visual occlusion, reduced positioning accuracy and pose loss in the dynamic scanning process of aviation large components, this paper proposes a binocular vision dynamic positioning method based on magnetic target. This method detects the spatial coordinates of the magnetic target in real time through the binocular camera, extracts the target center to construct a unified reference system of the measurement platform, and uses MATLAB simulation to analyze the influence of different target layouts on the scanning stability and positioning accuracy. On this basis, a dual-objective optimization model with the objectives of ‘minimizing the number of targets’ and ‘spatial distribution uniformity’ is established, and Monte Carlo simulation is used to evaluate the robustness under Gaussian noise and random frame loss interference. The experimental results on the C-Track optical tracking platform show that the optimized magnetic target layout reduces the rotation error of the dynamic scanning from 0.055° to 0.035°, the translation error from 0.31 mm to 0.162 mm, and the scanning efficiency is increased by 33%, which significantly improves the positioning accuracy and tracking stability of the system under complex working conditions. This method provides an effective solution for high-precision dynamic measurement of aviation large components. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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19 pages, 3176 KiB

Open AccessArticle

Deploying an Educational Mobile Robot

by Dorina Plókai, Borsa Détár, Tamás Haidegger and Enikő Nagy

Machines 2025, 13(7), 591; https://doi.org/10.3390/machines13070591 - 8 Jul 2025

Abstract

This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped [...] Read more.

This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped with odometry and inertial measurement units (IMUs), to gather comprehensive motion data. To enhance the reliability and interpretability of the data, advanced data processing techniques—such as moving averages, correlation analysis, and exponential smoothing—were employed. Python-based tools, including Matplotlib and Visual Studio Code, were used for data visualization and analysis. The analysis provided key insights into the robot’s motion dynamics; specifically, its stability during linear movements and variability during turns. By applying moving average filtering and exponential smoothing, noise in the sensor data was significantly reduced, enabling clearer identification of motion patterns. Correlation analysis revealed meaningful relationships between velocity and acceleration during various motion states. These findings underscore the value of advanced data processing techniques in improving the performance and reliability of educational mobile robots. The insights gained in this pilot project contribute to the optimization of navigation algorithms and motion control systems, enhancing the robot’s future potential in STEM education applications. Full article

(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)

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20 pages, 4411 KiB

Open AccessArticle

A Dual-Level Intelligent Architecture-Based Method for Coupling Fault Diagnosis of Temperature Sensors in Traction Converters

by Yunxiao Fu, Qiuyang Zhou and Haichuan Tang

Machines 2025, 13(7), 590; https://doi.org/10.3390/machines13070590 - 8 Jul 2025

Abstract

To address the coupled fault diagnosis challenge between temperature sensors and equipment in traction converter cooling systems, this paper proposes a dual-level intelligent diagnostic architecture. This method achieves online sensor fault isolation and early equipment anomaly warning by leveraging spatiotemporal correlation modeling of [...] Read more.

To address the coupled fault diagnosis challenge between temperature sensors and equipment in traction converter cooling systems, this paper proposes a dual-level intelligent diagnostic architecture. This method achieves online sensor fault isolation and early equipment anomaly warning by leveraging spatiotemporal correlation modeling of multimodal sensor data and ensemble learning-based prediction. At the first level, it integrates multi-source parameters such as outlet temperature and pressure to establish dynamic prediction models, which are combined with adaptive threshold mechanisms for detecting various sensor faults including offset, open-circuit, and noise interference. At the second level, it monitors the status of temperature sensors through time-series analysis of inlet temperature data. Verified on an edge computing platform, the proposed method effectively resolves the coupling misdiagnosis between sensor distortion and equipment faults while maintaining physical interpretability, thereby significantly enhancing diagnostic robustness under complex operating conditions. Full article

(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)

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50 pages, 23293 KiB

Open AccessArticle

Optimal Dimensional Synthesis of Ackermann and Watt-I Six-Bar Steering Mechanisms for Two-Axle Four-Wheeled Vehicles

by Yaw-Hong Kang, Da-Chen Pang and Dong-Han Zheng

Machines 2025, 13(7), 589; https://doi.org/10.3390/machines13070589 - 7 Jul 2025

Abstract

This study investigates the dimensional synthesis of steering mechanisms for front-wheel-drive, two-axle, four-wheeled vehicles using two metaheuristic optimization algorithms: Differential Evolution with golden ratio (DE-gr) and Improved Particle Swarm Optimization (IPSO). The vehicle under consideration has a track-to-wheelbase ratio of 0.5 and an [...] Read more.

This study investigates the dimensional synthesis of steering mechanisms for front-wheel-drive, two-axle, four-wheeled vehicles using two metaheuristic optimization algorithms: Differential Evolution with golden ratio (DE-gr) and Improved Particle Swarm Optimization (IPSO). The vehicle under consideration has a track-to-wheelbase ratio of 0.5 and an inner wheel steering angle of 70 degrees. The mechanisms synthesized include the Ackermann steering mechanism and two variants (Type I and Type II) of the Watt-I six-bar steering mechanisms, also known as central-lever steering mechanisms. To ensure accurate steering and minimize tire wear during cornering, adherence to the Ackermann steering condition is enforced. The objective function combines the mean squared structural error at selected steering positions with a penalty term for violations of the Grashoff inequality constraint. Each optimization run involved 100 or 200 iterations, with numerical experiments repeated 100 times to ensure robustness. Kinematic simulations were conducted in ADAMS v2015 to visualize and validate the synthesized mechanisms. Performance was evaluated based on maximum structural error (steering accuracy) and mechanical advantage (transmission efficiency). The results indicate that the optimized Watt-I six-bar steering mechanisms outperform the Ackermann mechanism in terms of steering accuracy. Among the Watt-I variants, the Type II designs demonstrated superior performance and convergence precision compared to the Type I designs, as well as improved results compared to prior studies. Additionally, the optimal Type I-2 and Type II-2 mechanisms consist of two symmetric Grashof mechanisms, can be classified as non-Ackermann-like steering mechanisms. Both optimization methods proved easy to implement and showed reliable, efficient convergence. The DE-gr algorithm exhibited slightly superior overall performance, achieving optimal solutions in seven cases compared to four for the IPSO method. Full article

(This article belongs to the Special Issue The Kinematics and Dynamics of Mechanisms and Robots)

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29 pages, 4266 KiB

Open AccessArticle

Analysis of Friction Torque Characteristics of a Novel Ball–Roller Composite Turntable Bearing

by Heng Tian, Weiwang Li, Xiuhua Shao, Zhanli Zhang and Wenhu Zhang

Machines 2025, 13(7), 588; https://doi.org/10.3390/machines13070588 - 7 Jul 2025

Abstract

Traditional three-row roller YRT turntable bearings exhibit high friction torque during operation, which limits their performance in high-precision and high-response applications. To address this issue, a novel ball–roller composite turntable bearing is proposed that effectively reduces friction torque while maintaining a high load [...] Read more.

Traditional three-row roller YRT turntable bearings exhibit high friction torque during operation, which limits their performance in high-precision and high-response applications. To address this issue, a novel ball–roller composite turntable bearing is proposed that effectively reduces friction torque while maintaining a high load capacity. A mechanical model based on statics is established, and the Newton–Raphson method is employed to calculate the contact load. The formation mechanism of friction torque is analyzed, and a corresponding computational model is developed and validated using experimental data. The effects of axial load, eccentricity, overturning moment, rotational speed, and axial clearance on friction torque are systematically studied. Results indicate that friction torque increases with these parameters. Axial clearance has a significant influence, and an optimal clearance value between the balls and rollers is determined. Additionally, a reasonable range for the raceway curvature radius coefficient is proposed. When the numerical ratio of balls to rollers is 1, the bearing exhibits optimal friction performance. Among various roller crowning strategies, logarithmic crowning yields the best results. This study provides a theoretical basis and technical support for the optimized design of ball–roller composite turntable bearings. Full article

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15 pages, 4334 KiB

Open AccessArticle

Research on Wheel Polygonal Wear Based on the Vehicle–Track Coupling Vibration of Metro

by Yixuan Shi, Qingzhou Mao, Qunsheng Wang, Huanyun Dai, Xinyu Peng and Cuijun Dong

Machines 2025, 13(7), 587; https://doi.org/10.3390/machines13070587 - 7 Jul 2025

Abstract

Wheel polygonal wear of metro deteriorates the vibration environment of the vehicle system, potentially leading to resonance-induced fatigue failure of components. This poses serious risks to operational safety and increases maintenance costs. To address the adverse effects of wheel polygonal wear, dynamic tracking [...] Read more.

Wheel polygonal wear of metro deteriorates the vibration environment of the vehicle system, potentially leading to resonance-induced fatigue failure of components. This poses serious risks to operational safety and increases maintenance costs. To address the adverse effects of wheel polygonal wear, dynamic tracking tests and numerical simulations were conducted. The modal analysis focused on the vehicle–track coupling system, incorporating various track structures to explore the formation mechanisms and key influencing factors of polygonization. Test results revealed dominant polygonal wear patterns of the seventh to ninth order, inducing forced vibrations in the 50–70 Hz frequency range. These frequencies closely match the P2 resonance frequency generated by wheel–rail interaction. When vehicle–track coupling is considered, the track’s frequency response shows multiple peaks within this range, indicating susceptibility to resonance excitation. Additionally, rail joint irregularities act as geometric excitation sources that trigger polygonal development, while the P2 force resonance mode plays a critical role in its amplification. Full article

(This article belongs to the Section Vehicle Engineering)

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17 pages, 3487 KiB

Open AccessArticle

Feature Extraction and Diagnosis of Power-Shift System Faults in Unmanned Hydro-Mechanical Transmission Tractors

by Ya Li, Kuan Liu, Xiaohan Chen, Kejia Zhai, Yangting Liu, Yehui Zhao and Guangming Wang

Machines 2025, 13(7), 586; https://doi.org/10.3390/machines13070586 - 7 Jul 2025

Abstract

To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature extraction method for data dimensionality reduction and [...] Read more.

To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature extraction method for data dimensionality reduction and a deep learning network called W_SCBAM were introduced for fault diagnosis. Both W_SCBAM and conventional algorithms were trained 20 times, and their performance was compared. Further testing of W_SCBAM was conducted in various application scenarios. The results indicate that the feature extraction method reduces the sample length from 46 to 3. The fault diagnosis accuracy of W_SCBAM for the radial-inlet clutch system has an expectation of 98.5% and a variance of 1.6%, respectively, outperforming other algorithms. W_SCBAM also excels in diagnosing faults in the axial-inlet clutch system, achieving 97.6% accuracy even with environmental noise. Unlike traditional methods, this study integrates the update of a dimensionality reduction matrix into network parameter training, achieving high-precision classification with minimal input data and lightweight network structure, ensuring reliable data transmission and real-time fault diagnosis of unmanned tractors. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment)

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23 pages, 3072 KiB

Open AccessArticle

Zone-Wise Uncertainty Propagation and Dimensional Stability Assessment in CNC-Turned Components Using Manual and Automated Metrology Systems

by Mohammad S. Alsoufi, Saleh A. Bawazeer, Mohammed W. Alhazmi, Hani Alhazmi and Hasan H. Hijji

Machines 2025, 13(7), 585; https://doi.org/10.3390/machines13070585 - 6 Jul 2025

Abstract

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic [...] Read more.

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic experimental comparison was conducted between a manual Digital Vernier Caliper (DVC) and an automated Coordinate Measuring Machine (CMM) using five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Dimensional measurements were taken from five consecutive machining zones to capture localized metrological behaviors. The results indicated that the CMM consistently achieved lower expanded uncertainty (as low as 0.00166 mm) and minimal propagated uncertainties (≤0.0038 mm), regardless of material hardness or cutting position. In contrast, the DVC demonstrated significantly higher uncertainty (up to 0.03333 mm) and propagated errors exceeding 0.035 mm, particularly in harder materials and unsupported zones affected by surface degradation and fixture variability. Root-sum-square (RSS) modeling confirmed that manual measurements are more prone to operator-induced error amplification. While the DVC sometimes recorded lower absolute errors, its substantial uncertainty margins hampered measurement reliability. To statistically validate these findings, a two-way ANOVA was performed, confirming that both the measurement system and machining zone significantly impacted uncertainty, as well as their interaction. These results emphasize the importance of material-informed and zone-sensitive metrology, highlighting the advantages of automated systems in sustaining measurement repeatability and dimensional stability in high-precision applications. Full article

(This article belongs to the Section Automation and Control Systems)

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20 pages, 18025 KiB

Open AccessArticle

Numerical Research on Pressure Fluctuation Characteristics of Small-Scale and High-Speed Automotive Pump

by Lulu Zheng, Xiaoping Chen, Jinglei Qu and Xiaojie Ma

Machines 2025, 13(7), 584; https://doi.org/10.3390/machines13070584 - 5 Jul 2025

Abstract

Rotor–stator interaction and the coupling between the clearance flow and main flow amplify the flow complexity in small-scale, high-speed automotive pumps. This degrades the pressure fluctuations, compromising the operational stability of these pumps. To better understand the pressure fluctuation distribution characteristics within such [...] Read more.

Rotor–stator interaction and the coupling between the clearance flow and main flow amplify the flow complexity in small-scale, high-speed automotive pumps. This degrades the pressure fluctuations, compromising the operational stability of these pumps. To better understand the pressure fluctuation distribution characteristics within such a pump, the Reynolds-averaged Navier–Stokes equations and the shear stress transport k-ω turbulence model were applied to numerically compute the pump. The simulation results were compared with experimental data, and good agreement was achieved. The results show that pressure fluctuations in the main flow region are mainly dominated by the blade passing frequency, and the intensity of pressure fluctuations in the near-field area of the tongue reaches its peak value, showing significant fluctuation characteristics. Significant peak signals are captured in the low-frequency band of pressure fluctuations in the clearance region. The pressure fluctuation characteristics are also affected by the rotor–stator interaction between the impeller front shroud and the volute casing, while the dominant frequency is still the blade passing frequency. In addition, the dominant frequencies of pressure fluctuations in the main and clearance flows show a similar distribution to the flow rate, but the minimum amplitude corresponds to different flow rates. Full article

(This article belongs to the Section Turbomachinery)

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