Machines

16 pages, 2577 KiB

Open AccessArticle

Vibration Fatigue Characteristics of a High-Speed Train Bogie and Traction Motor Based on Field Measurement and Spectrum Synthesis

by Lirong Guo, Guoshun Li, Can Chen, Yichao Zhang, Hongwei Zhang and Dao Gong

Machines 2025, 13(7), 613; https://doi.org/10.3390/machines13070613 - 16 Jul 2025

Abstract

In this study, the fatigue behavior in high-speed train bogie frames and mounted traction motors was investigated through dynamic stress measurements and vibration analysis. A spectrum synthesis method was developed to integrate multipoint random vibrations from the bogie frame into a unified excitation [...] Read more.

In this study, the fatigue behavior in high-speed train bogie frames and mounted traction motors was investigated through dynamic stress measurements and vibration analysis. A spectrum synthesis method was developed to integrate multipoint random vibrations from the bogie frame into a unified excitation spectrum for motor fatigue assessment. The results demonstrate that fatigue damage in the bogie frame progresses linearly with increasing speed, with critical stress concentrations being identified at the motor base weld seams (41.4 MPa equivalent stress at 400 km/h). Traction motor vibration spectra were found to deviate substantially from IEC 61373 standards, leading to higher fatigue damage that follows an exponential growth pattern relative to speed increases. The proposed methodology provides direct experimental validation of component-specific fatigue mechanisms under operational loading conditions. Full article

(This article belongs to the Special Issue Research and Application of Rail Vehicle Technology)

24 pages, 6089 KiB

Open AccessArticle

An Optimized 1-D CNN-LSTM Approach for Fault Diagnosis of Rolling Bearings Considering Epistemic Uncertainty

by Onur Can Kalay

Machines 2025, 13(7), 612; https://doi.org/10.3390/machines13070612 - 16 Jul 2025

Abstract

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and [...] Read more.

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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23 pages, 951 KiB

Open AccessArticle

Multi-Objective Evolution and Swarm-Integrated Optimization of Manufacturing Processes in Simulation-Based Environments

by Panagiotis D. Paraschos, Georgios Papadopoulos and Dimitrios E. Koulouriotis

Machines 2025, 13(7), 611; https://doi.org/10.3390/machines13070611 - 16 Jul 2025

Abstract

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data [...] Read more.

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article

(This article belongs to the Section Advanced Manufacturing)

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12 pages, 2871 KiB

Open AccessArticle

Multi-Objective Optimization Design of Low-Frequency Band Gap for Local Resonance Acoustic Metamaterials Based on Genetic Algorithm

by Jianjiao Deng, Yunuo Qin, Xi Chen, Yanyong He, Yu Song, Xinpeng Zhang, Wenting Ma, Shoukui Li and Yudong Wu

Machines 2025, 13(7), 610; https://doi.org/10.3390/machines13070610 - 16 Jul 2025

Abstract

Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for [...] Read more.

Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for local resonance acoustic metamaterials (LRAMs) based on a multi-objective genetic algorithm. Within a COMSOL Multiphysics 6.2 with MATLAB R2024b co-simulation framework, a parameterized unit cell model of the metamaterial is constructed. The optimization process targets two objectives: minimizing the band gap’s deviation from the target and reducing the structural mass. A multi-objective fitness function is formulated by incorporating the band gap deviation and structural mass constraints, and non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform a global search over the geometric parameters of the resonant unit. The resulting Pareto-optimal solution set achieves a unit cell mass as low as 26.49 g under the constraint that the band gap deviation does not exceed 2 Hz. The results of experimental validation show that the optimized metamaterial configuration reduces the peak of the low-frequency frequency response function (FRF) at 63 Hz by up to 75% in a car door structure. Furthermore, the simulation predictions exhibit good agreement with the experimental measurements, confirming the effectiveness and reliability of the proposed method in engineering applications. The proposed multi-objective optimization framework is highly general and extensible and capable of effectively balancing between the acoustic performance and structural mass, thus providing an efficient engineering solution for low-frequency noise control problems. Full article

(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)

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28 pages, 9135 KiB

Open AccessArticle

Performance Analysis of a Reciprocating Refrigeration Compressor Under Variable Operating Speeds

by Willian T. F. D. da Silva, Vitor M. Braga and Cesar J. Deschamps

Machines 2025, 13(7), 609; https://doi.org/10.3390/machines13070609 - 15 Jul 2025

Abstract

Variable-speed reciprocating compressors (VSRCs) have been increasingly used in domestic refrigeration due to their ability to modulate cooling capacity and reduce energy consumption. A detailed understanding of performance-limiting factors such as volumetric and exergetic inefficiencies is essential for optimizing their operation. An experimentally [...] Read more.

Variable-speed reciprocating compressors (VSRCs) have been increasingly used in domestic refrigeration due to their ability to modulate cooling capacity and reduce energy consumption. A detailed understanding of performance-limiting factors such as volumetric and exergetic inefficiencies is essential for optimizing their operation. An experimentally validated simulation model was developed using GT-SUITE to analyze a VSRC operating with R-600a across speeds from 1800 to 6300 rpm. Volumetric inefficiencies were quantified using a stratification methodology, while an exergy-based approach was adopted to assess the main sources of thermodynamic inefficiency in the compressor. Unlike traditional energy analysis, exergy analysis reveals where and why irreversibilities occur, linking them directly to power consumption and providing a framework for optimizing design. Results reveal that neither volumetric nor exergy efficiency varies monotonically with compressor speed. At low speeds, exergetic losses are dominated by the electrical motor (up to 19% of input power) and heat transfer (up to 13.5%). Conversely, at high speeds, irreversibilities from fluid dynamics become critical, with losses from discharge valve throttling reaching 5.8% and bearing friction increasing to 6.5%. Additionally, key volumetric inefficiencies arise from piston–cylinder leakage, which causes up to a 4.5% loss at low speeds, and discharge valve backflow, causing over a 5% loss at certain resonant speeds. The results reveal complex speed-dependent interactions between dynamic and thermodynamic loss mechanisms in VSRCs. The integrated modeling approach offers a robust framework for diagnosing inefficiencies and supports the development of more energy-efficient compressor designs. Full article

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

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18 pages, 8131 KiB

Open AccessArticle

Rapid Dismantling of Aluminum Stranded Conductors: An Automated Approach

by Zhinan Cao, Jie Feng, Shijun Xie, Qian Peng, Jiahui Chen, Cheng Wen and Jigang Huang

Machines 2025, 13(7), 608; https://doi.org/10.3390/machines13070608 - 15 Jul 2025

Abstract

Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To [...] Read more.

Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To demonstrate the proposed method, a modular prototype machine that includes four main functional modules (transmission, untwisting, separation, and shearing) was developed. Experimental results from the prototype dismantling machine demonstrated that when processing JL/G3A-500/65 conductors (Sichuan Star Cable Co., Ltd., Leshan, China) under the following operational parameters—0.5 rad/s rotational speed, 10 cm extension length, 2400 N clamping force, and 40 N·m torque application—the system achieved a single-layer dismantling efficiency exceeding 98%. This represents a significant improvement in operational speed compared to traditional manual methods. The developed machine achieved collaborative control of axial feed, reverse untwisting, and automatic shearing, elevating the untwisting qualification rate to 95%. This solution provides an efficient and safe approach to conductor inspection, demonstrating substantial engineering application value. Full article

(This article belongs to the Special Issue Design and Experimental Activity of Testing Machines and Mechanical Test Rigs)

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18 pages, 4099 KiB

Open AccessArticle

Numerical Study of the Effect of Unsteady Aerodynamic Forces on the Fatigue Load of Yawed Wind Turbines

by Dereje Haile Hirgeto, Guo-Wei Qian, Xuan-Yi Zhou and Wei Wang

Machines 2025, 13(7), 607; https://doi.org/10.3390/machines13070607 - 15 Jul 2025

Abstract

The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw [...] Read more.

The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw offsets using blade element momentum theory, dynamic blade element momentum, and the converging Lagrange filaments vortex method, all implemented in OpenFAST. Simulations employed yaw angles from −40° to 40°, with turbulent inflow generated by TurbSim, an OpenFAST tool for realistic wind conditions. Fatigue loads were calculated according to IEC 61400-1 design load case 1.2 standards, using thirty simulations per yaw angle across five wind speed bins. Damage equivalent load was evaluated via rainflow counting, Miner’s rule, and Goodman correction. Results showed that the free vortex method, by modeling unsteady aerodynamic forces, yielded distinct differences in damage equivalent load compared to the blade element method in yawed conditions. The free vortex method predicted lower damage equivalent load for the low-speed shaft bending moment at negative yaw offsets, attributed to its improved handling of unsteady effects that reduce load variations. Conversely, for yaw offsets above 20°, the free vortex method indicated higher damage equivalent for low-speed shaft torque, reflecting its accurate capture of dynamic inflow and unsteady loading. These findings highlight the critical role of unsteady aerodynamics in fatigue load predictions and demonstrate the free vortex method’s value within OpenFAST for realistic damage equivalent load estimates in yawed turbines. The results emphasize the need to incorporate unsteady aerodynamic models like the free vortex method to accurately assess yaw offset impacts on wind turbine component fatigue. Full article

(This article belongs to the Special Issue Aerodynamic Analysis of Wind Turbine Blades)

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23 pages, 9638 KiB

Open AccessArticle

A Study on the Influence Mechanism of the Oil Injection Distance on the Oil Film Distribution Characteristics of the Gear Meshing Zone

by Wentao Zhao, Lin Li and Gaoan Zheng

Machines 2025, 13(7), 606; https://doi.org/10.3390/machines13070606 - 14 Jul 2025

Viewed by 49

Abstract

Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational [...] Read more.

Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational fluid dynamics (CFD) and dynamic mesh technology. By implementing the volume of fluid (VOF) multiphase flow model and the standard k-ω turbulence model, the study simulates the dynamic distribution of lubricant in gear meshing zones and analyzes critical parameters such as the oil volume fraction, eddy viscosity, and turbulent kinetic energy. The results show that reducing the oil injection distance significantly enhances lubricant coverage and continuity: as the injection distance increases from 4.8 mm to 24 mm, the lubricant shifts from discrete droplets to a dense wedge-shaped film, mitigating lubrication failure risks from secondary atomization and energy loss. The optimized injection distance also improves the spatial stability of eddy viscosity and suppresses excessive dissipation of turbulent kinetic energy, enhancing both the shear-load capacity and thermal management. Dynamic data from monitoring point P show that reducing the injection distance stabilizes lubricant velocity and promotes more consistent oil film formation and heat transfer. Through multiphysics simulations and parametric analysis, this study elucidates the interaction between geometric parameters and hydrodynamic behaviors in jet lubrication systems. The findings provide quantitative evaluation methods for structural optimization and energy control in gear lubrication systems, offering theoretical insights for thermal management and reliability enhancement in high-speed transmission. These results contribute to the lightweight design and sustainable development of industrial equipment. Full article

(This article belongs to the Section Friction and Tribology)

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24 pages, 1076 KiB

Open AccessArticle

Visual–Tactile Fusion and SAC-Based Learning for Robot Peg-in-Hole Assembly in Uncertain Environments

by Jiaxian Tang, Xiaogang Yuan and Shaodong Li

Machines 2025, 13(7), 605; https://doi.org/10.3390/machines13070605 - 14 Jul 2025

Viewed by 58

Abstract

Robotic assembly, particularly peg-in-hole tasks, presents significant challenges in uncertain environments where pose deviations, varying peg shapes, and environmental noise can undermine performance. To address these issues, this paper proposes a novel approach combining visual–tactile fusion with reinforcement learning. By integrating multimodal data [...] Read more.

Robotic assembly, particularly peg-in-hole tasks, presents significant challenges in uncertain environments where pose deviations, varying peg shapes, and environmental noise can undermine performance. To address these issues, this paper proposes a novel approach combining visual–tactile fusion with reinforcement learning. By integrating multimodal data (RGB image, depth map, tactile force information, and robot body pose data) via a fusion network based on the autoencoder, we provide the robot with a more comprehensive perception of its environment. Furthermore, we enhance the robot’s assembly skill ability by using the Soft Actor–Critic (SAC) reinforcement learning algorithm, which allows the robot to adapt its actions to dynamic environments. We evaluate our method through experiments, which showed clear improvements in three key aspects: higher assembly success rates, reduced task completion times, and better generalization across diverse peg shapes and environmental conditions. The results suggest that the combination of visual and tactile feedback with SAC-based learning provides a viable and robust solution for robotic assembly in uncertain environments, paving the way for scalable and adaptable industrial robots. Full article

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

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58 pages, 38117 KiB

Open AccessArticle

Multi-Disciplinary Investigations on the Best Flying Wing Configuration for Hybrid Unmanned Aerial Vehicles: A New Approach to Design

by Janani Priyadharshini Veeraperumal Senthil Nathan, Martin Navamani Chellapandian, Vijayanandh Raja, Parvathy Rajendran, It Ee Lee, Naveen Kumar Kulandaiyappan, Beena Stanislaus Arputharaj, Subhav Singh and Deekshant Varshney

Machines 2025, 13(7), 604; https://doi.org/10.3390/machines13070604 - 14 Jul 2025

Viewed by 161

Abstract

Flying wing Unmanned Aerial Vehicles (UAVs) are an interesting flight configuration, considering its benefits over aerodynamic, structural and added stealth aspects. The existing configurations are thoroughly studied from the literature survey and useful observations with respect to design and analysis are obtained. The [...] Read more.

Flying wing Unmanned Aerial Vehicles (UAVs) are an interesting flight configuration, considering its benefits over aerodynamic, structural and added stealth aspects. The existing configurations are thoroughly studied from the literature survey and useful observations with respect to design and analysis are obtained. The proposed design method includes distinct calculations of the UAV and modelling using 3D experience. The created innovative models are simulated with the help of computational fluid dynamics techniques in ANSYS Fluent to obtain the aerodynamic parameters such as forces, pressure and velocity. The optimization process continues to add more desired modifications to the model, to finalize the best design of flying wing frame for the chosen application and mission profile. In total, nine models are developed starting with the base model, then leading to the conventional, advanced and nature inspired configurations such as the falcon and dragonfly models, as it has an added advantage of producing high maneuverability and lift. Following this, fluid structure interaction analysis has been performed for the best performing configurations, resulting in the determination of variations in the structural behavior with the imposition of advanced composite materials, namely, boron, Kevlar, glass and carbon fiber-reinforced polymers. In addition to this, a hybrid material is designed by combining two composites that resulted in superior material performance when imposed. Control dynamic study is performed for the maneuvers planned as per mission profile, to ensure stability during flight. All the resulting parameters obtained are compared with one another to choose the best frame of the flying wing body, along with the optimum material to be utilized for future analysis and development. Full article

(This article belongs to the Special Issue Design and Application of Bionic Robots)

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26 pages, 3701 KiB

Open AccessArticle

Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP

by Yongjuan Zhao, Chaozhe Guo, Jiangyong Mi, Lijin Wang, Haidi Wang and Hailong Zhang

Machines 2025, 13(7), 603; https://doi.org/10.3390/machines13070603 - 12 Jul 2025

Viewed by 163

Abstract

Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective [...] Read more.

Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively. Full article

(This article belongs to the Section Vehicle Engineering)

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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 - 12 Jul 2025

Viewed by 172

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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17 pages, 1163 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

Viewed by 174

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 benchmark underactuated 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)

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27 pages, 28182 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

Viewed by 81

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)

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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

Viewed by 100

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

Viewed by 121

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

Viewed by 370

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

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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

Viewed by 280

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

Viewed by 177

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

Viewed by 145

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

Viewed by 124

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

Viewed by 503

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

Viewed by 211

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

Viewed by 159

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

Viewed by 206

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

Viewed by 185

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

Viewed by 187

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

Viewed by 154

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

Viewed by 189

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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