Machines

24 pages, 1162 KB

Open AccessArticle

Predicting Operational Reliability of the Directional Control Valves of the Hydraulic Press System Using Taguchi Method and Regression Analysis

by Borivoj Novaković, Mica Djurdjev, Luka Djordjević, Vesna Drakulović, Ljiljana Radovanović and Velibor Premčevski

Machines 2025, 13(12), 1124; https://doi.org/10.3390/machines13121124 (registering DOI) - 7 Dec 2025

Abstract

This paper presents a study that investigates the operational reliability of directional control valves used in hydraulic press systems by applying the Taguchi method and regression analysis. The research focuses on key hydraulic parameters—kinematic viscosity, internal leakage, pressure, and temperature—to identify their influence [...] Read more.

This paper presents a study that investigates the operational reliability of directional control valves used in hydraulic press systems by applying the Taguchi method and regression analysis. The research focuses on key hydraulic parameters—kinematic viscosity, internal leakage, pressure, and temperature—to identify their influence on valve reliability. Three valves (DCV1–DCV3) were tested under identical conditions using an L8 orthogonal array to optimize the experimental design while maintaining statistical validity. The Taguchi analysis revealed that internal leakage is the dominant factor affecting valve reliability, consistently confirmed across all statistical evaluations, including signal-to-noise (S/N) ratios and ANOVA results. Regression models were developed for each valve to quantify the effect of each factor and showed excellent predictive accuracy (R² > 98%). The study concludes that minimizing internal leakage, maintaining lower temperatures, and applying higher operating pressures significantly enhance valve reliability, while viscosity had negligible effect within the tested range. Valve DCV2 demonstrated the highest predicted reliability. These findings offer valuable insights for the optimization of hydraulic valve design and maintenance strategies, contributing to the improved performance and longevity of industrial hydraulic systems. Full article

(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)

24 pages, 2506 KB

Open AccessArticle

A Predictive Maintenance Approach for Composting Plants Based on ERP and Digital Twin Integration

by Hamed Nozari and Agnieszka Szmelter-Jarosz

Machines 2025, 13(12), 1123; https://doi.org/10.3390/machines13121123 (registering DOI) - 6 Dec 2025

Abstract

This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical [...] Read more.

This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical assets with their virtual counterparts and management systems. The digital twin acts as a real-time virtual model of critical equipment—such as aeration motors, mixers, and reactors—enabling continuous monitoring, dynamic simulation, and predictive fault detection. Meanwhile, the ERP system provides an integrated environment for maintenance scheduling, data management, and resource allocation, ensuring that maintenance decisions are data-driven and synchronized with operational workflows. Machine learning algorithms, implemented using hybrid physical–data models, predict equipment degradation trends and optimize maintenance interventions. The proposed framework was validated in an industrial-scale composting facility, where results demonstrated a 40% increase in mean time to failure (MTTF), a 35% reduction in repair time, and a 30% decrease in maintenance costs, resulting in a return on investment of 42.5% within the first year. The system’s modular architecture and high adaptability to different machinery types confirm its potential applicability to broader machine design and automation contexts, supporting the transition toward intelligent, self-maintaining industrial systems. Full article

(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)

► Show Figures

Figure 1

26 pages, 24094 KB

Open AccessArticle

Determining Relevant 3D Roughness Parameters for Sandblasted Surfaces: A Methodological Approach

by Maxence Bigerelle, Eddy Chevallier, Julie Lemesle, Raphael Deltombe, Frederic Robache, Romain Vayron, Nadiia Zubchuk, Ingrid Proriol-Serre, Stephane Benayoun and Karine Anselme

Machines 2025, 13(12), 1122; https://doi.org/10.3390/machines13121122 - 5 Dec 2025

Abstract

This study presents a robust methodology for analyzing 3D roughness parameters to characterize sandblasted surfaces, identifying the most relevant descriptors for process optimization. Sandblasting with irregularly shaped corundum particles is performed using five grit sizes (25, 50, 90, 125, and 250 µm) and [...] Read more.

This study presents a robust methodology for analyzing 3D roughness parameters to characterize sandblasted surfaces, identifying the most relevant descriptors for process optimization. Sandblasting with irregularly shaped corundum particles is performed using five grit sizes (25, 50, 90, 125, and 250 µm) and three pressure levels (2, 3, and 4 bar). The resulting surfaces are characterized through eight 3D roughness parameters: Sa, Spc, Sal, Sfd, Sdq, Sdr, Spd, and Str. A linear model of the form Q = a + b.D + d.D.P, where Q represents the roughness parameter, D is the average grit size, and P is the sandblasting pressure, is employed. For Spd, a nonlinear model, Spd = (a + b.D + d.D.P)², yields a significantly improved determination coefficient, demonstrating the model’s enhanced ability to capture the complexity of the Spd parameter. The double-bootstrap analysis validates the statistical significance of all models, providing confidence intervals for each parameter. This approach emphasizes the importance of advanced 3D roughness descriptors for accurately analyzing surface textures in sandblasting processes, offering a reliable framework for surface characterization and industrial optimization. Full article

(This article belongs to the Special Issue Dimensional Measurement, Analysis, and Control for Precision Manufacturing)

► Show Figures

Figure 1

32 pages, 6322 KB

Open AccessArticle

Development of a Robotic Manipulator for Piano Performance via Numbered Musical Notation Recognition

by Pu-Sheng Tsai, Ter-Feng Wu and Chen-Ting Liao

Machines 2025, 13(12), 1121; https://doi.org/10.3390/machines13121121 - 5 Dec 2025

Abstract

This paper presents a piano-playing robotic system that integrates numbered musical notation recognition with automated manipulator control. The system captures the notation using a camera, applies four-point detection for perspective correction, and performs measure segmentation through an orthogonal projection method. A pixel-scanning technique [...] Read more.

This paper presents a piano-playing robotic system that integrates numbered musical notation recognition with automated manipulator control. The system captures the notation using a camera, applies four-point detection for perspective correction, and performs measure segmentation through an orthogonal projection method. A pixel-scanning technique is then used to locate the positions of numerical notes, pitch dots, and rhythmic markers. Digit recognition is achieved using a CNN model trained on both the MNIST handwritten digit dataset and a custom computer-font digit dataset (CFDD), enabling robust identification of numerical symbols under varying font styles. The hardware platform consists of a 3D-printed robotic hand mounted on a linear rail and driven by an ESP32-based embedded controller with custom driver circuits. According to the recognized musical notes, the manipulator executes lateral positioning and vertical key-press motions to reproduce piano melodies. Experimental results demonstrate reliable notation recognition and accurate performance execution, confirming the feasibility of combining computer vision and robotic manipulation for low-cost, automated musical performance. Full article

(This article belongs to the Special Issue Advances and Challenges in Robotic Manipulation)

► Show Figures

Figure 1

26 pages, 2026 KB

Open AccessArticle

Advancing Intelligent Fault Diagnosis Through Enhanced Mechanisms in Transfer Learning

by Hadi Abbas and Ratna B. Chinnam

Machines 2025, 13(12), 1120; https://doi.org/10.3390/machines13121120 - 5 Dec 2025

Abstract

Intelligent Fault Diagnosis (IFD) systems are integral to predictive maintenance and real-time monitoring but often encounter challenges such as data scarcity, non-linearity, and changing operational conditions. To address these challenges, we propose an enhanced transfer learning framework that integrates the Universal Adaptation Network [...] Read more.

Intelligent Fault Diagnosis (IFD) systems are integral to predictive maintenance and real-time monitoring but often encounter challenges such as data scarcity, non-linearity, and changing operational conditions. To address these challenges, we propose an enhanced transfer learning framework that integrates the Universal Adaptation Network (UAN) with Spectral-normalized Neural Gaussian Process (SNGP), WideResNet, and attention mechanisms, including self-attention and an outlier attention layer. UAN’s flexibility bridges diverse fault conditions, while SNGP’s robustness enables uncertainty quantification for more reliable diagnostics. WideResNet’s architectural depth captures complex fault patterns, and the attention mechanisms focus the diagnostic process. Additionally, we employ Optuna for hyperparameter optimization, using a structured study to fine-tune model parameters and ensure optimal performance. The proposed approach is evaluated on benchmark datasets, demonstrating superior fault identification accuracy, adaptability to varying operational conditions, and resilience against data anomalies compared to existing models. Our findings highlight the potential of advanced machine learning techniques in IFD, setting a new standard for applying these methods in complex diagnostic environments. Full article

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

► Show Figures

Graphical abstract

13 pages, 1819 KB

Open AccessArticle

Development and Experimental Verification of a Thermal Elongation Prediction Model for Electric Spindles

by Xinyu Liu, Lefu Jiang and Han Ye

Machines 2025, 13(12), 1119; https://doi.org/10.3390/machines13121119 - 5 Dec 2025

Abstract

Thermal elongation in high-speed motorized spindles constitutes a major source of machining error in five-axis machine tools, critically impacting machining precision. This study aims to develop and validate a cumulative thermal error compensation model for predicting spindle thermal elongation, subsequently enabling effective compensation [...] Read more.

Thermal elongation in high-speed motorized spindles constitutes a major source of machining error in five-axis machine tools, critically impacting machining precision. This study aims to develop and validate a cumulative thermal error compensation model for predicting spindle thermal elongation, subsequently enabling effective compensation via a dedicated control algorithm. Key thermal error factors, primarily spindle speed and cumulative thermal error, were identified through analysis. An innovative numerical prediction model incorporating these factors was established. Its performance was evaluated through experiments utilizing eddy-current displacement sensors for high-speed, high-precision thermal elongation measurement. The validation results demonstrated the model’s strong predictive capability: During spindle startup, prediction errors exhibited minor transients, stabilizing near zero once the operating speed was reached. Under dynamic speed changes, the maximum prediction error was only 1.28 μm, with the overall maximum residual error recorded at 2.04 μm. These findings confirm the model’s high accuracy. Furthermore, the model exhibits excellent generalization capability, delivering significant compensation effectiveness across diverse variable-speed operating conditions. This work successfully developed a highly accurate numerical model and a practical compensation strategy, significantly enhancing the positioning accuracy of high-speed spindles against thermal disturbances. The proposed approach offers substantial engineering utility for thermal error compensation in precision machining applications. Full article

(This article belongs to the Special Issue Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools)

► Show Figures

Figure 1

20 pages, 5165 KB

Open AccessArticle

Development of a Test Rig for Detecting Fatigue Cracks in a Plastic Component of a Medical Device via Acoustic Signal Acquisitions

by Luigi Leopardi, Valerio Mangeruga, Matteo Giacopini, Marco Di Settimi and Roberto Rosi

Machines 2025, 13(12), 1118; https://doi.org/10.3390/machines13121118 - 4 Dec 2025

Abstract

This work presents the design and implementation of a mechanical test bench developed for the comparative evaluation of three configurations of a mechanical biomedical device: the reference version and two optimized alternatives aimed at improving long-term reliability and functional performance. The test bench [...] Read more.

This work presents the design and implementation of a mechanical test bench developed for the comparative evaluation of three configurations of a mechanical biomedical device: the reference version and two optimized alternatives aimed at improving long-term reliability and functional performance. The test bench performs mechanical fatigue testing under controlled and repeatable conditions, simulating the cyclic loads typical of real-world operation. A key innovation of this system is the integration of a non-invasive acoustic acquisition module, which continuously monitors the dynamic behavior of the device during testing. The analysis of acoustic signals allows for the early detection of wear, looseness, deformation, and the onset of structural defects, providing valuable insight into the device’s mechanical health without altering its configuration. This study also details the engineering design of the control system, emphasizing both hardware integration and software architecture supporting real-time signal processing. Experimental results demonstrate that acoustic analysis represents an effective non-destructive approach for evaluating the endurance and reliability of compact plastic biomedical devices. The proposed methodology contributes to more accurate service life estimation, supports product validation, and promotes continuous improvements in the safety and quality of mechanical systems used in biomedical applications. Full article

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

► Show Figures

Figure 1

28 pages, 1704 KB

Open AccessArticle

Vibration Spectrum Analysis of Rolling Bearings Based on Nonlinear Stiffness Model

by Dawei Guo, Hong He, Zhuyao Li, Chong Zhang and Jiyou Fei

Machines 2025, 13(12), 1117; https://doi.org/10.3390/machines13121117 - 4 Dec 2025

Abstract

This paper addresses the issue of fault diagnosis in high-speed train bogie bearings under complex working conditions and proposes a method for calculating the characteristic frequency of rolling bearings that takes into account the influence of radial clearance. By establishing a five-degree-of-freedom nonlinear [...] Read more.

This paper addresses the issue of fault diagnosis in high-speed train bogie bearings under complex working conditions and proposes a method for calculating the characteristic frequency of rolling bearings that takes into account the influence of radial clearance. By establishing a five-degree-of-freedom nonlinear dynamic model, this study systematically analyzes the modulation mechanism of radial clearance on the fault characteristic frequency of bearings and verifies the findings through an experimental platform. The results indicate that an increase in clearance not only leads to significant attenuation of the fault characteristic frequency amplitude, but also induces sideband modulation effects, thereby interfering with fault diagnosis accuracy. The experimental data show good agreement with the theoretical calculations, verifying the effectiveness of the proposed method. Specifically, the nonlinear stiffness-based characteristic frequency calculation reduces the prediction error from 6.9–5.7% under traditional theory to 2.3–3.4% across a wide range of rotational speeds. Meanwhile, the clearance-induced amplitude attenuation predicted by the model is also experimentally confirmed, with measured amplitude reductions of 35–42% as clearance increases from 0.2 μm to 0.5 μm. These results not only demonstrate the accuracy and engineering applicability of the method but also provide new theoretical foundations and practical references for health monitoring and early fault diagnosis of high-speed train bearings. Full article

(This article belongs to the Section Machine Design and Theory)

21 pages, 2192 KB

Open AccessArticle

Development, Implementation and Experimental Assessment of Path-Following Controllers on a 1:5 Scale Vehicle Testbed

by Luca Biondo, Angelo Domenico Vella and Alessandro Vigliani

Machines 2025, 13(12), 1116; https://doi.org/10.3390/machines13121116 - 3 Dec 2025

Abstract

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling [...] Read more.

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing. Full article

(This article belongs to the Special Issue Emerging Research in Autonomous Vehicle Technology: Innovations in On-Road and Off-Road Driving Challenges)

► Show Figures

Figure 1

33 pages, 3790 KB

Open AccessArticle

Block–Neighborhood-Based Multi-Objective Evolutionary Algorithm for Distributed Resource-Constrained Hybrid Flow Shop with Machine Breakdown

by Ying Xu, Shulan Lin and Junqing Li

Machines 2025, 13(12), 1115; https://doi.org/10.3390/machines13121115 - 3 Dec 2025

Abstract

Production scheduling that involves distributed factories, machine maintenance, and resource constraints plays a crucial role in manufacturing. However, these realistic constraints have rarely been considered simultaneously in the hybrid flow shop (HFS). To address this issue, a distributed resource-constrained hybrid flow shop scheduling [...] Read more.

Production scheduling that involves distributed factories, machine maintenance, and resource constraints plays a crucial role in manufacturing. However, these realistic constraints have rarely been considered simultaneously in the hybrid flow shop (HFS). To address this issue, a distributed resource-constrained hybrid flow shop scheduling problem with machine breakdowns (DRCHFSP-MB) is studied. There are two optimization objectives, i.e., makespan and total energy consumption (TEC). To solve the strongly NP-hard problem, a mathematical model is established and a block–neighborhood-based multi-objective evolutionary algorithm (BNMOEA) is developed. In the proposed algorithm, an efficient hybrid initialization method is adopted to obtain high-quality individuals to participate in the evolutionary process of the population. Next, to enhance the search capability of the BNMOEA, three well-designed crossover operators are used in the global search. Then, the convergence of the proposed algorithm is improved by utilizing eight critical factory-based local search operators combined with block–neighborhood. Finally, the BNMOEA is compared with several of the most advanced multi-objective algorithms; the results indicate that the BNMOEA has an outstanding performance in solving DRCHFSP-MB. Full article

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

► Show Figures

Figure 1

19 pages, 3496 KB

Open AccessArticle

Evaluating Low Temperature’s Impact on Lithium-Ion Batteries: Examination of Performance Metrics with Respect to Size and Chemistry

by Ahmed Abdelrahman, Yuxin Hu and Jie Liu

Machines 2025, 13(12), 1114; https://doi.org/10.3390/machines13121114 - 2 Dec 2025

Abstract

This study explores the effects of low temperatures on the performance of various lithium-ion batteries (LIBs), comparing different sizes and chemical compositions. Experiments were conducted in a sub-zero temperature environment, examining discharge behavior, internal resistance, and capacity retention. The findings reveal that smaller-sized [...] Read more.

This study explores the effects of low temperatures on the performance of various lithium-ion batteries (LIBs), comparing different sizes and chemical compositions. Experiments were conducted in a sub-zero temperature environment, examining discharge behavior, internal resistance, and capacity retention. The findings reveal that smaller-sized batteries (18650, 21700) have a marked resilience to cold, outperforming larger 26650 cells, with smaller average capacity declines noted in both LiCoO₂ and LiMn₂O₂ chemistries. The study also introduces a new adaptive filtering technique for better battery behaviour analysis at low temperatures, which avoids distortion of important electrochemical signals. Full article

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

► Show Figures

Figure 1

24 pages, 1571 KB

Open AccessArticle

Improved FMEA Risk Assessment Based on Load Sharing and Its Application to a Magnetic Lifting System

by Bo Sun, Lei Wang, Jian Zhang and Ning Ding

Machines 2025, 13(12), 1113; https://doi.org/10.3390/machines13121113 - 2 Dec 2025

Abstract

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the [...] Read more.

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the weights of risk factors (RFs) when processing data related to the Risk Priority Number (RPN). This leads to significant subjectivity in the final risk ranking of failure modes. To overcome these drawbacks, this study proposes an improved FMEA risk assessment method based on load sharing, aiming to develop an improved FMEA method that addresses the critical limitations of traditional approaches by integrating load sharing principles and systematic weight determination, thereby enhancing risk assessment objectivity and accuracy in complex multi-component systems. First, probabilistic linguistic terms are adopted to quantify experts’ risk assessment information, and the geometric mean method is then used to aggregate assessments from multiple experts. Second, the Fuzzy Best–Worst Method (FBWM) is employed to determine the relative weights of the three RPN factors (Occurrence, Severity, and Detection). Additionally, partial system structural data are obtained through load sharing, and these data—combined with the calculated factor weights—are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate the final risk ranking of failure modes. Finally, a case study of a magnetic crane is conducted to verify the feasibility and effectiveness of the proposed method, supplemented by comparative experiments to demonstrate its superiority. Full article

(This article belongs to the Section Advanced Manufacturing)

► Show Figures

Figure 1

21 pages, 34821 KB

Open AccessArticle

The Study and Application of Quadrilateral Space-Time Absolute Nodal Coordinate Formulation Cable Element

by Dekun Chen, Jia Feng, Naidan Hou and Zhou Huang

Machines 2025, 13(12), 1112; https://doi.org/10.3390/machines13121112 - 2 Dec 2025

Abstract

The construction of a high-order shape function is a key and difficulty for unstructured grid mesh and sliding boundary problems. In this paper, a construction method of space-time absolute nodal coordinate formulation quadrilateral cable (SACQ) is proposed, and the accuracy of the SACQ [...] Read more.

The construction of a high-order shape function is a key and difficulty for unstructured grid mesh and sliding boundary problems. In this paper, a construction method of space-time absolute nodal coordinate formulation quadrilateral cable (SACQ) is proposed, and the accuracy of the SACQ element is studied and verified with three different applications. First, the shape function of SACQ is constructed with spatiotemporal reduction coordinates, and the action integral of SACQ is composed with the Lagrangian function and discrete with perspective transformation. Second, the numerical convergence region is discussed and determined with the Courant number. Furthermore, a space-time nodal dislocation and its relation with the Courant number are studied. The simulation and verification are focusing on some realistic problems. Finally, a one-sided impact, a free-flexible pendulum, a taut string with a sliding boundary and a deployable guyed mast under an impact transverse wave are simulated. In these problems, an unstructured grid meshed with SACQ has similar energy convergence and accuracy to a structured grid but shows better efficiency. Full article

(This article belongs to the Section Advanced Manufacturing)

► Show Figures

Figure 1

18 pages, 3216 KB

Open AccessArticle

Experimental Validation and Dynamic Analysis of Additive Manufacturing Burner for Gas Turbine Applications

by Alessio Cascino, Enrico Meli, Andrea Rindi, Egidio Pucci and Emanuele Matoni

Machines 2025, 13(12), 1111; https://doi.org/10.3390/machines13121111 - 1 Dec 2025

Abstract

In the context of a rapidly evolving energy sector, the development of hydrogen-ready gas turbines, a key milestone in the energy transition toward decarbonization, requires advanced gas injectors capable of operating with both natural gas and hydrogen. Additive manufacturing (AM) significantly accelerates the [...] Read more.

In the context of a rapidly evolving energy sector, the development of hydrogen-ready gas turbines, a key milestone in the energy transition toward decarbonization, requires advanced gas injectors capable of operating with both natural gas and hydrogen. Additive manufacturing (AM) significantly accelerates the iterative design process, enabling the production of single-piece, fully three-dimensional components and supporting rapid prototyping with optimized process parameters. Early assessment of component durability, particularly structural damping, is crucial to predicting dynamic response and high-cycle fatigue life, reducing costly design modifications in later stages. In this work, a methodology is proposed for structural damping characterization at the early prototypal stage, combining ping test measurements with a numerical approach based on the Half-Power Bandwidth Method (HBM) enhanced by an iterative procedure. This approach enables the accurate estimation of representative damping values for low-mass, high-stiffness components, overcoming the limited accuracy of standard Rayleigh damping models at this stage. The methodology was applied to a gas turbine burner manufactured via additive manufacturing, demonstrating that even with partial experimental data, it is possible to obtain reliable damping estimates, support rapid design iteration, and ensure convergence toward optimal mechanical performance. Full article

(This article belongs to the Section Advanced Manufacturing)

► Show Figures

Figure 1

20 pages, 9548 KB

Open AccessArticle

The Role of Graphite-like Carbon Films in Mitigating Fretting Wear of Slewing Bearings

by Xiaoxu Pang, Xu Zuo, Minghao Yang, Dingkang Zhu, Qiaoshuo Li, Chongfeng Jiang and Jingxi Mao

Machines 2025, 13(12), 1110; https://doi.org/10.3390/machines13121110 - 1 Dec 2025

Abstract

We aimed to address the issue of fretting wear on the rollers and raceways of pitch bearings in wind turbines during shutdown and under intermittent high loads. This study focuses on triple-row cylindrical roller bearings. A finite element wear simulation of the contact [...] Read more.

We aimed to address the issue of fretting wear on the rollers and raceways of pitch bearings in wind turbines during shutdown and under intermittent high loads. This study focuses on triple-row cylindrical roller bearings. A finite element wear simulation of the contact area between a single roller and the raceway was established based on Hertzian contact theory and the modified Archard model. The wear coefficient values of the model before and after coating were verified through experiments, with results of k₁ = 3.125 × 10⁻⁸ and k₂ = 4.5 × 10⁻¹⁰, respectively. The effects of normal load, displacement amplitude, and cycle number on the fretting wear behavior of rollers under both uncoated and GLC-coated conditions were investigated. The results show that the GLC (Glassy Carbon-like Carbon) film significantly reduces the friction coefficient and wear. Compared to uncoated rollers, it reduces the maximum wear depth by approximately 90.53% across various normal loads, displacement amplitudes, and numbers of cycles. Additionally, the wear rate of the coated rollers remains consistently low with small fluctuations. The conclusion holds that the GLC film reduces the interface shear force and effective slip amplitude, enhances surface hardness and stability, and improves the fretting wear resistance of pitch bearings by an order of magnitude under complex load and oil-starved conditions. The primary objective of this work is to investigate the mechanisms for enhancing the anti-fretting wear performance of pitch bearings, with the goal of significantly extending their service life and reliability in harsh operating environments. Full article

(This article belongs to the Section Turbomachinery)

► Show Figures

Figure 1

20 pages, 1536 KB

Open AccessArticle

Contrastive Learning-Based One-Class Classification for Intelligent Manufacturing System

by Seunghwan Song

Machines 2025, 13(12), 1109; https://doi.org/10.3390/machines13121109 - 1 Dec 2025

Abstract

Time-series anomaly detection is imperative for ensuring reliability and safety in intelligent manufacturing systems. However, real-world environments typically provide only normal operating data and exhibit significant periodicity, noise, imbalance, and domain variability. The present study proposes CL-OCC, a contrastive learning-based one-class framework that [...] Read more.

Time-series anomaly detection is imperative for ensuring reliability and safety in intelligent manufacturing systems. However, real-world environments typically provide only normal operating data and exhibit significant periodicity, noise, imbalance, and domain variability. The present study proposes CL-OCC, a contrastive learning-based one-class framework that integrates seasonal-trend decomposition using loess (STL) for structure-preserving temporal augmentation, a cosine-regularized soft boundary for compact normal-region formation, and variance-preserving regularization to prevent latent collapse. A convolutional recurrent encoder is first pretrained via an autoencoder objective and subsequently optimized through a unified loss that balances contrastive invariance, soft-boundary constraint, and variance dispersion. Experiments on semiconductor equipment data and three public benchmarks demonstrate that CL-OCC provides competitive or superior performance relative to reconstruction-, prediction-, and contrastive-based baselines. CL-OCC exhibits smoother anomaly trajectories, earlier detection of gradual drifts, and strong robustness to noise, window-length variation, and extreme class imbalance. A study of the effects of ablation and interaction on the stability of representations indicates that STL-based augmentation, boundary shaping, and variance regularization contribute complementary benefits to this stability. While the qualitative results indicate limited sensitivity to extremely short impulsive disturbances, the proposed framework delivers a generalizable and stable solution for unsupervised industrial monitoring, with promising potential for extension to multi-resolution analysis and online prognostics and health management (PHM) applications. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation, 2nd Edition)

► Show Figures

Figure 1

0 pages, 14864 KB

Open AccessArticle

A PHIL Controller Design Automation Method for Grid-Forming Inverters with Much Reduced Computational Delay

by Jian Yu, Hao Wu, Yulong Hao, Xuanxuan Liang and Zixiang Zhang

Machines 2025, 13(12), 1108; https://doi.org/10.3390/machines13121108 - 29 Nov 2025

Abstract

Within a power hardware-in-the-loop (PHIL) controller design automation (CDA) framework for voltage feedback grid-forming inverters, a scaled-down inverter system is developed for time-domain response solving. This hardware-based approach effectively addresses the conflicting demands of accuracy, computational efficiency, and modeling cost that are commonly [...] Read more.

Within a power hardware-in-the-loop (PHIL) controller design automation (CDA) framework for voltage feedback grid-forming inverters, a scaled-down inverter system is developed for time-domain response solving. This hardware-based approach effectively addresses the conflicting demands of accuracy, computational efficiency, and modeling cost that are commonly encountered in simulation-based methods. Conventional synchronous sampling in digitally controlled pulse-width modulation (PWM) inverters introduces severe low-frequency distortion and significant ripple components in the step response, leading to non-decaying oscillations that compromise the extraction of settling time and steady-state error. By analyzing the sideband aliasing mechanism in capacitor-voltage sampling and associated harmonic-cancellation conditions, aliasing-free sampling is achieved using 90° phase-shifted anti-aliasing filters combined with synchronous sampling. Although Fast Fourier Transform (FFT) filtering offers the highest fidelity, it suffers from window-boundary distortions and is unsuitable for online use; therefore, four practical filtering schemes are evaluated against the FFT benchmark, among which oversampling with moving-average filtering (MAF) retains dynamics closest to the FFT result while avoiding its distortions. An objective function incorporating step-response metrics is constructed to optimize single-variable active damping and multiple resonant controllers, mitigating severe overshoot encountered in conventional integral-based approaches. Experimental results verify the aliasing mechanism and the effectiveness of the proposed CDA method. Full article

(This article belongs to the Section Electrical Machines and Drives)

► Show Figures

Figure 1

20 pages, 815 KB

Open AccessArticle

Automotive Scratch Detection: A Lightweight Convolutional Network Approach Augmented by Generative Adversarial Learning

by Guojie Qu, Jiaying Liao, Kai Liu, Bin Xu and Yuwen Qian

Machines 2025, 13(12), 1107; https://doi.org/10.3390/machines13121107 - 29 Nov 2025

Abstract

The growing demand for high-precision machining and inspection in modern manufacturing has positioned machine vision as a key technology for surface defect detection. However, identifying subtle surface scratches on automotive components remains a challenging task due to the stringent requirements on sensitivity, precision, [...] Read more.

The growing demand for high-precision machining and inspection in modern manufacturing has positioned machine vision as a key technology for surface defect detection. However, identifying subtle surface scratches on automotive components remains a challenging task due to the stringent requirements on sensitivity, precision, and robustness against complex background interference. In this paper, we propose an automated detection system with a Convolutional Neural Network (CNN) architecture. To address data scarcity, we construct a large-scale, high-quality dataset using both data augmentation and Generative Adversarial Network (GAN)-based synthesis. Furthermore, the proposed lightweight CNN replaces traditional fully connected layers with one-dimensional convolutional layers to reduce parameter complexity and model size, while a Dropout mechanism is incorporated to mitigate overfitting and enhance generalization. Experimental results demonstrate that the proposed model achieves superior detection accuracy and robustness across diverse imaging conditions. Moreover, the developed system effectively addresses the limitations of data insufficiency and model complexity, offering an efficient and automated solution for surface quality inspection in industrial manufacturing. Full article

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

► Show Figures

Figure 1

18 pages, 2965 KB

Open AccessArticle

Optimizing the Transformer Iron Core Cutting Stock Problem Using a Discrete Artificial Bee Colony Algorithm

by Qiang Luo, Zuogan Tang and Chunrong Pan

Machines 2025, 13(12), 1106; https://doi.org/10.3390/machines13121106 - 28 Nov 2025

Abstract

In the manufacturing of iron core for high-power transformers, a cutting stock problem arises where large-width silicon steel coils must be cut into narrower coils, known as strips. Typically, the required length of each strip far exceeds that of a single coil. Therefore, [...] Read more.

In the manufacturing of iron core for high-power transformers, a cutting stock problem arises where large-width silicon steel coils must be cut into narrower coils, known as strips. Typically, the required length of each strip far exceeds that of a single coil. Therefore, the problem necessitates additional consideration of how to split the strips and arrange them on the large coils, with the goal of minimizing the total number of strips. In this paper, we propose a discrete artificial bee colony algorithm to address this problem. The algorithm replaces the stochastic roulette wheel with biased selection in the onlooker bee phase and introduces partially mapped crossover in both the onlooker and scout bee phases. These enhancements facilitate more effective utilization of information from high-quality solutions, thereby improving the algorithm’s stability and its capacity to obtain higher-quality results. Experimental results show that compared to existing methods reported in the literature, the proposed approach reduces the total number of strips by an average of over 3.9% and 7.6% for Set 2 and Set 3, respectively, while also exhibiting a faster convergence rate than other competitive algorithms. Full article

(This article belongs to the Section Advanced Manufacturing)

► Show Figures

Figure 1

Journal Description

Machines

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI