Machines

36 pages, 5862 KB

Open AccessArticle

Reliability Analysis of Aerospace Blade Manufacturing Equipment: A Multi-Source Uncertainty FMECA Method for Five-Axis CNC Machine Tool Spindle Systems

by Muhao Han, Yufei Li, Hailong Tian, Yuzhi Sun, Zixuan Ni, Yunshenghao Qiu and Haoyuan Li

Machines 2026, 14(4), 360; https://doi.org/10.3390/machines14040360 - 25 Mar 2026

Abstract

Five-axis Computerized Numerical Control (CNC) machine tools play a pivotal role in the precision manufacturing of aeroengine turbine blades, where ultra-high reliability and accuracy are essential. Failure Mode, Effects and Criticality Analysis (FMECA) has been widely applied in the reliability assessment of such [...] Read more.

Five-axis Computerized Numerical Control (CNC) machine tools play a pivotal role in the precision manufacturing of aeroengine turbine blades, where ultra-high reliability and accuracy are essential. Failure Mode, Effects and Criticality Analysis (FMECA) has been widely applied in the reliability assessment of such advanced machining systems due to its systematic evaluation of potential failure modes. However, traditional FMECA approaches often overlook the ambiguity of human cognition and the interdependence among expert evaluations, limiting their effectiveness in complex aerospace manufacturing environments. To address these issues, this paper proposes a novel FMECA framework based on generalized intuitionistic linguistic theory. A new Generalized Intuitionistic Linguistic Weighted Geometric Average (GILWGA) operator is introduced to couple multi-source expert information and quantify the fuzziness inherent in subjective assessments. Additionally, an intuitionistic linguistic entropy-based weighting scheme is developed to dynamically evaluate key risk factors, including severity, occurrence, detectability, and controllability. The proposed framework is applied to a case study involving the spindle system of a five-axis CNC machine tool used in aeroengine blade production. The results demonstrate that the proposed method offers more robust and consistent failure mode prioritization, providing effective decision support for reliability-centered maintenance in aerospace equipment manufacturing. Full article

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

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23 pages, 3020 KB

Open AccessArticle

Evaluation of Regression Models for Predicting Cutting Forces Based on Spindle Speed, Feed Speed and Milling Strategy During MDF Board Milling

by Tomáš Čuchor, Peter Koleda, Ján Šustek, Lukáš Štefančin, Richard Kminiak, Pavol Koleda and Zuzana Vyhnáliková

Machines 2026, 14(4), 359; https://doi.org/10.3390/machines14040359 (registering DOI) - 25 Mar 2026

Abstract

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. Unlike previous studies that focus primarily on force measurement, this work integrates experimental analysis with machine learning-based predictive modelling to [...] Read more.

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. Unlike previous studies that focus primarily on force measurement, this work integrates experimental analysis with machine learning-based predictive modelling to improve process understanding and prediction accuracy. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) and electrical power consumption under varying spindle speeds (14,000, 16,000 and 18,000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected F_x and F_y components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. The Fine Tree algorithm demonstrated the best performance, achieving validation metrics of R² = 0.9 and RMSE = 0.60 (MSE = 0.36, MAE = 0.48), and improved testing performance with R² = 0.95 and RMSE = 0.44 (MSE = 0.20, MAE = 0.36). After model comparison using RMSE, R², training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained data on cutting force and electrical energy consumption are suitable for reliable predictive modelling in CNC milling of MDF boards. However, it is necessary to work with those components that have the greatest dependence on speed, feed, or type of milling, and these are the force components measured on the x and y axes. Full article

(This article belongs to the Special Issue Monitoring and Control of Machining Processes)

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17 pages, 3951 KB

Open AccessArticle

Thermo-Mechanical Analysis and Fatigue Life Estimation of Shrink-Fit Tool Holders

by Kubilay Aslantas, Ekrem Oezkaya and Adem Çiçek

Machines 2026, 14(4), 358; https://doi.org/10.3390/machines14040358 - 24 Mar 2026

Abstract

The present study investigates the thermo-mechanical behaviour and fatigue life associated with the shrink-fit process of shrink-fit tool holders. These holders are an indispensable component of high-precision and high-speed machining processes in modern manufacturing industries. Shrink-fit holders are subjected to elevated levels of [...] Read more.

The present study investigates the thermo-mechanical behaviour and fatigue life associated with the shrink-fit process of shrink-fit tool holders. These holders are an indispensable component of high-precision and high-speed machining processes in modern manufacturing industries. Shrink-fit holders are subjected to elevated levels of stress as a consequence of repeated heating and cooling cycles, which can result in clamping fatigue over time. In this study, a three-dimensional finite element model (FEM) of a holder manufactured from H13 tool steel in accordance with BT40 standards was created using ANSYS software. The numerical analyses included transient thermal and structural analyses, consisting of a 4.5-s induction heating stage at 10 kW power, followed by a 1200-s cooling process. The analysis yielded results that were corroborated by the experimental data. It was established that, upon the conclusion of the heating process, the temperature in the conical region of the holder attained a range of approximately 388–417 °C. Furthermore, it was ascertained that a radial expansion of approximately 17.2–22 µm, which is required for the successful insertion of the cutting tool into the inner bore, was achieved. The fatigue life prediction, which constitutes the main focus of the study, applied the Soderberg criterion and evaluated two basic loading scenarios: the first tool assembly and repeated tool assembly cycles. The calculations yielded a life estimate of approximately 12,407 cycles for the first tool assembly cycle and approximately 19,400 cycles for the repeated tool assembly cycle. Accordingly, the repeated tool assembly condition exhibited a longer fatigue life than the first tool assembly condition. The enhanced longevity observed in the repeated tool assembly scenario is attributed to the stress cycle not fully reaching zero during this process, resulting in a lower stress amplitude. Full article

(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)

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31 pages, 4224 KB

Open AccessArticle

Path Tracking Control for Differential Steering Autonomous Vehicles with Active Body Inward Tilt

by Rizwan Ali, Chenyu Huang, Tong Wu and Jie Tian

Machines 2026, 14(3), 357; https://doi.org/10.3390/machines14030357 - 23 Mar 2026

Abstract

Considering the problems that the inner wheel load decreases due to centrifugal force during the steering of differential steering autonomous vehicles, which may result in differential steering failure or even vehicle rollover in severe cases, a path-tracking strategy for differential steering autonomous vehicles [...] Read more.

Considering the problems that the inner wheel load decreases due to centrifugal force during the steering of differential steering autonomous vehicles, which may result in differential steering failure or even vehicle rollover in severe cases, a path-tracking strategy for differential steering autonomous vehicles considering active body inward tilt is proposed. Aiming at the problems of fixed parameters and insufficient adaptability of model predictive control in the path-tracking process of autonomous vehicles, this paper proposes a collaborative adaptive model predictive controller (MPC) with preview time and weight matrix based on fuzzy inference as the upper control, so as to realize the tracking control of the reference path by conventionally steered autonomous vehicles. In the lower control, an H_∞/H₂ hybrid controller with particle swarm optimization (PSO)-based parameter self-tuning is employed to control the differential steering autonomous vehicle (DSAV) to track the reference model, achieving differential steering and active body inward tilt simultaneously. Co-simulation results of CarSim and Simulink show that the proposed method outperforms the fixed-preview-time MPC and the manually tuned H_∞/H₂ hybrid controller. Compared with the latter, the maximum absolute values of lateral deviation and yaw angle deviation are reduced by 17.9% and 14.5%, respectively; the maximum deviation in the reference yaw rate is decreased by 21.2%; the maximum absolute value of the inward tilt angle is reduced by 53.4%; and the maximum values of LTR and occupant-perceived lateral acceleration are lowered by 57.1% and 44.2%, respectively. Full article

(This article belongs to the Special Issue Control Engineering and Artificial Intelligence)

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32 pages, 5732 KB

Open AccessArticle

Multi-Objective Optimization of the Grinding Process in a Spring-Rotor Mill Using Regression-Based Modeling

by Aidos Baigunusov, Bekbolat Moldakhanov, Alina Kim, Mikhail Doudkin, Vladimir Yakovlev, Piotr Stryczek and Tadeusz Lesniewski

Machines 2026, 14(3), 356; https://doi.org/10.3390/machines14030356 - 23 Mar 2026

Abstract

This study addresses the problem of improving the efficiency of fine grinding of bulk materials in a spring-rotor mill. The objective is to determine technologically sound operating parameters based on mathematical modeling, design of experiments, and multi-objective optimization. The methodology relies on a [...] Read more.

This study addresses the problem of improving the efficiency of fine grinding of bulk materials in a spring-rotor mill. The objective is to determine technologically sound operating parameters based on mathematical modeling, design of experiments, and multi-objective optimization. The methodology relies on a full-factorial experimental design according to the Hartley plan, with five control factors: rotor rotational speed, material loading ratio, overlap of the working zones, grinding chamber clearance, and grinding duration. The analyzed responses include grinding fineness, throughput, power consumption, specific energy consumption, and specific metal intensity. Based on experimental data, adequate second-order polynomial regression models were obtained for all response variables using the least-squares method. Statistical analysis showed that grinding time and rotational speed had the most significant influence on the process. Multi-objective optimization using the weighted-sum method enabled the identification of optimal operating conditions that balance product quality, throughput, and energy consumption. Verification experiments confirmed the adequacy of the developed models. Practical implementation of the optimized regimes increases throughput by 15–20% while simultaneously reducing energy consumption by 8–12% compared with empirically selected operating conditions. The proposed models and recommendations provide a quantitative basis for tuning and controlling grinding equipment in processing industries. Full article

(This article belongs to the Section Material Processing Technology)

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19 pages, 4352 KB

Open AccessArticle

Myoelectric Controlled Bionic Robotic Hand for Voluntary Finger Motion Driven by Neuromuscular Intent

by André Moreira, Marco Pinto, Miguel Fernandes, João Costa, Jorge Fidalgo and Alessandro Fantoni

Machines 2026, 14(3), 355; https://doi.org/10.3390/machines14030355 - 23 Mar 2026

Abstract

Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the [...] Read more.

Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the forearm are processed via amplification, filtering, and digital analysis to enable accurate detection of muscle activity. The system achieves independent and simultaneous actuation of five fingers using a tendon-driven, servo-actuated mechanism in a lightweight ABS structure. Experimental evaluation demonstrates finger actuation delays ranging from 314 ms to 650 ms, maximum holding strengths between 1.75 N and 4.07 N, and minimum gripping distances between 22 mm and 49 mm across all five fingers, with peak motor currents remaining below 0.7 A. Results validate consistent muscle activity detection, successful execution of individual and combined finger movements, and the robustness of the proposed design. Full article

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

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22 pages, 4091 KB

Open AccessArticle

3D Trajectory Tracking Based on Super-Twisting Observer and Non-Singular Terminal Sliding Mode Control for Underactuated Autonomous Underwater Vehicle

by Zehui Yuan, Long He, Ya Zhang, Shizhong Li, Chenrui Bai and Zhuoyan Qi

Machines 2026, 14(3), 354; https://doi.org/10.3390/machines14030354 - 21 Mar 2026

Abstract

This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on [...] Read more.

This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on the kinematic and dynamic models of autonomous underwater vehicle, virtual velocity commands are constructed via backstepping approach to stabilize the position and attitude errors. To circumvent the “differential explosion” problem inherent in conventional backstepping control caused by repeated differentiations of virtual control variables, first-order low-pass filters are introduced to construct dynamic surface control, yielding smooth derivatives of virtual velocity commands. Secondly, to enhance convergence rate and robustness, a non-singular terminal sliding surface is designed at the dynamic level, and a terminal reaching law is formulated to achieve finite-time convergence of velocity tracking errors. Furthermore, to compensate for external disturbances and unmodeled dynamics, a disturbance observer based on the super-twisting algorithm is developed, enabling finite-time high-precision estimation of lumped disturbances, with the estimation results incorporated into the control law for feedforward compensation. Finally, comparative simulations are conducted under two typical disturbance scenarios. The results demonstrate that the proposed method achieves instantaneous disturbance estimation (reducing convergence time from 3 s to near zero), significantly smoother control inputs, and superior tracking accuracy with RMSE as low as 0.6788 m and MAE as low as 0.1468 m, reducing errors by up to 30.6% compared to baseline methods. Full article

(This article belongs to the Special Issue Mastering Vibrations: The Latest Breakthroughs in Control for Mechanical Systems)

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32 pages, 8316 KB

Open AccessArticle

An Adaptive Enhancement Method for Weak Fault Diagnosis of Locomotive Gearbox Bearings Under Wheel–Raisl Excitation

by Yong Li, Wangcai Ding and Yongwen Mao

Machines 2026, 14(3), 353; https://doi.org/10.3390/machines14030353 - 21 Mar 2026

Abstract

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, [...] Read more.

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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37 pages, 2896 KB

Open AccessArticle

Energy-Efficient Resilience Scheduling for Elevator Group Control via Queueing-Based Planning and Safe Reinforcement Learning

by Tingjie Zhang, Tiantian Zhang, Hao Zou, Chuanjiang Li and Jun Huang

Machines 2026, 14(3), 352; https://doi.org/10.3390/machines14030352 - 21 Mar 2026

Abstract

High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs [...] Read more.

High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs and carbon constraints sharpen the tension between peak-power control, energy savings, and service capacity. This paper proposes a two-layer resilience scheduling framework that integrates queueing-based planning with safe reinforcement learning (RL) fine-tuning. In the planning layer, parsimonious queueing approximations and scenario-based evaluation construct a finite set of implementable mode cards and emergency switching cards; Sample Average Approximation (SAA) combined with Conditional Value-at-Risk (CVaR) constraints filter candidates to enforce tail-risk-aware service limits while keeping power demand within a prescribed envelope. In the execution layer, online dispatch is formulated as a constrained Markov decision process; within the planning layer limits, action masking and Lagrangian safe RL learn small adaptive adjustments to suppress tail-waiting risk and improve recovery dynamics without increasing peak-power commitments. The experiments under morning peaks and post-event surges confirm tail risk reduction and accelerated recovery. For partial outages, the framework prioritizes SLA coverage and recovery speed, accepting a bounded increase in tail risk as a manageable trade-off. Throughout all tests, peak power remains within the prescribed limits. Improvements persist across random seeds and demand fluctuations, indicating distributional robustness and cross-scenario generalization. Ablation studies further reveal complementary roles: removing the planning layer CVaR screening worsens tail performance, while removing the execution layer action masking increases constraint violations and destabilizes recovery. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)

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25 pages, 8662 KB

Open AccessArticle

A Simple Comparative Study on the Effectiveness of Bearing Fault Detection Using Different Sensors on a Roller Bearing

by Haobin Wen, Khalid Almutairi and Jyoti K. Sinha

Machines 2026, 14(3), 351; https://doi.org/10.3390/machines14030351 - 20 Mar 2026

Abstract

Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and [...] Read more.

Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and measurement technologies is essential. This paper presents a comparative study on the applications of four sensor types in bearing condition monitoring. These four sensor types are vibration accelerometer, encoder, acoustic emission (AE) sensor and motor current probe. Their effectiveness and practicability in bearing fault detection are evaluted. Data simultaneously measured from these four sensor types on a split roller bearing within an experimental rig are used for the analysis. Different factors such as machine operating speeds, bearing fault sizes and their location are considered during the experiments to understand the effectiveness and fault detectability of different sensors on a common bearing. Both the accelerometer and the AE sensor are observed to effectively detect all bearing faults from small to extended sizes and from low to high operating speeds. However, the other two sensors, the encoder and motor current probe, have been found to be sensitive only to relatively larger fault sizes and higher operating speeds. The study presents valuable insights into their advantages and limitations through a systematic comparison of roller bearing fault detection. The study provides a basis for sensor selection in bearing condition monitoring and fault detection to enhance the reliability of industrial maintenance activities. Full article

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

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28 pages, 4866 KB

Open AccessArticle

Trajectory Optimization with Feasibility Guidance for Agile UAV Path Planning Under Geometric Constraints

by Shoshi Kawarabayashi, Kenji Uchiyama and Kai Masuda

Machines 2026, 14(3), 350; https://doi.org/10.3390/machines14030350 - 20 Mar 2026

Abstract

This paper presents a practical optimization framework for improving trajectory feasibility in constrained nonlinear optimal control problems for agile unmanned aerial vehicles (UAVs). The proposed method addresses trajectory optimization problems with non-convex geometric constraints, where gradient-based solvers often fail to converge to feasible [...] Read more.

This paper presents a practical optimization framework for improving trajectory feasibility in constrained nonlinear optimal control problems for agile unmanned aerial vehicles (UAVs). The proposed method addresses trajectory optimization problems with non-convex geometric constraints, where gradient-based solvers often fail to converge to feasible solutions. The framework combines Model Predictive Path Integral (MPPI) control and the Augmented Lagrangian iterative Linear Quadratic Regulator (AL-iLQR). MPPI is employed as a fast sampling-based guidance mechanism to explore feasible regions of the trajectory space, while AL-iLQR is used to efficiently refine locally optimal solutions with high numerical accuracy. By decoupling feasibility exploration from local optimal refinement, the proposed method mitigates the sensitivity of gradient-based trajectory optimization to initialization in highly constrained environments. Numerical simulations involving both simplified two-dimensional dynamics and full quadrotor models demonstrate that the proposed approach significantly improves the probability of converging to feasible and dynamically consistent trajectories compared with AL-iLQR alone. The proposed method does not aim to provide theoretical guarantees of global optimality; instead, it offers a practical and computationally efficient strategy for enhancing feasibility and robustness in real-time UAV trajectory optimization. Full article

(This article belongs to the Special Issue Flight Control and Path Planning of Unmanned Aerial Vehicles)

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23 pages, 3937 KB

Open AccessArticle

Deep Learning-Enhanced Fault Detection and Localization in Induction Motor Drives: A ResMLP and TCN Framework

by Hamza Adaika, Khaled Laadjal, Zoheir Tir and Mohamed Sahraoui

Machines 2026, 14(3), 349; https://doi.org/10.3390/machines14030349 - 20 Mar 2026

Abstract

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances (

Z_{a}, Z_{b}, Z_{c}

) and detection [...] Read more.

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances (

Z_{a}, Z_{b}, Z_{c}

) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article

(This article belongs to the Special Issue New Trends in Reliability and Lifetime Improvement in Power-Electronic-Controlled Machines and Devices)

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27 pages, 4265 KB

Open AccessArticle

Condition Monitoring Model Development for Belt Systems Using Hybrid CNN–BiLSTM Deep-Learning Techniques

by Mortda Mohammed Sahib, Philipp Plänitz, Matthias Hackert-Oschätzchen and Christoph Lerez

Machines 2026, 14(3), 348; https://doi.org/10.3390/machines14030348 - 19 Mar 2026

Abstract

Predictive maintenance aims to monitor equipment conditions through data-driven analysis and estimate failure in advance, which enables the scheduling of maintenance prior to equipment breakdown. In this work, a deep-learning neural network is used to predict the condition of the belt-drive system. A [...] Read more.

Predictive maintenance aims to monitor equipment conditions through data-driven analysis and estimate failure in advance, which enables the scheduling of maintenance prior to equipment breakdown. In this work, a deep-learning neural network is used to predict the condition of the belt-drive system. A combined Convolutional Neural Network with Bi-directional Long Short-Term Memory (CNN-BiLSTM) model is assigned for processing the operational parameters along with vibrational signals to predict belt-drive system conditions in two separate binary classifications: faulty or healthy and unbalanced or balanced conditions. The data flow in the CNN-BiLSTM model initiates with the CNN to extract the features from the vibration signals and performs essential pattern detection. Consequently, the BiLSTM’s role is to capture long-term temporal relationships that cannot be captured by the CNN alone. To predict the targeted conditions, a fully connected layer with a classifier is built at the BiLSTM outputs. For efficient model training, the data is preprocessed through denoising, augmentation, and normalization. Additionally, hyperparameter tuning is conducted to explore different model configurations and select the optimal ones on the basis of relevant performance. An ablation study is conducted to investigate the use of CNN and BiLSTM models individually, confirming that combining both components is essential for accurate classification. Moreover, the cross-validation technique is used to assess the proposed model’s generality by organizing unseen data across rotational speeds, which also depicts robust performance under varying operating conditions. The key added value of this study lies in integrating deep-learning techniques to address a knowledge gap by using raw vibrational signals to establish intelligent monitoring systems, which represents a new scientific contribution to the predictive maintenance of belt-drive systems. Full article

(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)

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40 pages, 6534 KB

Open AccessArticle

Telehandler Stability Analysis Using a Virtual Tilt & Rotation Platform

by Beatriz Puras, Gustavo Raush, Germán Filippini, Javier Freire, Pedro Roquet, Manel Tirado, Oriol Casadesús and Esteve Codina

Machines 2026, 14(3), 347; https://doi.org/10.3390/machines14030347 - 19 Mar 2026

Abstract

This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches [...] Read more.

This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches account for gravitational and inertial effects through equivalent external forces and moments applied at the global centre of gravity, enabling efficient evaluation of load redistribution and proximity to rollover thresholds under generalized quasi-static conditions. The application of these methods highlights intrinsic limitations when addressing structurally complex machines such as telehandlers equipped with a pivoting rear axle and evolving mass distribution due to boom motion. In particular, quasi-static approaches require a priori assumptions regarding the effective rollover axis and cannot fully capture the coupled geometric and contact interactions between rear axle articulation limits, centre of gravity migration, tyre–ground interface behaviour, and support polygon evolution. To overcome these limitations, a nonlinear dynamic multibody model based on the three-dimensional Bond Graph (3D Bond Graph) methodology is introduced. The model is implemented within a virtual tilt–rotation test platform and validated against experimental results obtained from ISO 22915-14 stability tests. The comparison confirms compliance with normative requirements and demonstrates that the dynamic framework captures condition-dependent rollover mechanisms and transitions between distinct virtual rollover axes that cannot be fully explained by quasi-static formulations. Unlike most previous studies, which focus on fixed configurations or forward-driving scenarios, the proposed framework analyzes stability evolution under spatial inclination while accounting for structural articulation constraints. The explicit identification of rollover axis transitions induced by rear axle articulation provides a deeper mechanistic interpretation of telehandler stability and supports the use of high-fidelity dynamic simulation as a complementary tool for test interpretation, experimental planning, and the development of predictive stability and operator assistance systems. Full article

(This article belongs to the Section Vehicle Engineering)

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15 pages, 1792 KB

Open AccessArticle

Developing a Digital Twin for Human Performance Assessment in Human–Machine Interaction

by Erik Novak, Aljaž Javernik, Iztok Palčič and Robert Ojsteršek

Machines 2026, 14(3), 346; https://doi.org/10.3390/machines14030346 - 19 Mar 2026

Abstract

Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine [...] Read more.

Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine interaction. A physical system comprising a conveyor belt, sensors, and operator-controlled elements was constructed, and a functionally equivalent digital model was created using Arduino IDE and MATLAB/Simulink. The digital twin records and synchronizes key human–machine interaction variables, including response time, assembly time, and execution consistency. Validation was conducted through simulation testing and an experimental study with 18 participants performing repeated assembly cycles. The results show that the developed digital twin accurately replicates the temporal dynamics of the physical process and reliably captures individual human performance patterns. Overall, the study provides a validated methodological framework for human–machine-integrated digital twins and demonstrates their potential for analyzing human–machine interaction, supporting operator training, and adaptive workplace design in line with Industry 5.0 principles. Full article

(This article belongs to the Special Issue Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized and Intelligent Collaborative Production Systems)

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1 pages, 128 KB

Open AccessCorrection

Correction: Qie et al. Underwater Objective Detection Algorithm Based on YOLOv8-Improved Multimodality Image Fusion Technology. Machines 2025, 13, 982

by Yage Qie, Chao Fang, Jinghua Huang, Donghao Wu and Jian Jiang

Machines 2026, 14(3), 345; https://doi.org/10.3390/machines14030345 - 19 Mar 2026

Abstract

In the original publication [...] Full article

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

20 pages, 4795 KB

Open AccessArticle

Effect of Combined Film Cooling and Swirl on the Thermal Performance of a Contoured High Pressure Turbine Vane of a Modern Turbofan Engine: A Numerical Study

by Djihane Mazouz, Zakaria Mansouri and Salaheddine Azzouz

Machines 2026, 14(3), 344; https://doi.org/10.3390/machines14030344 - 18 Mar 2026

Abstract

Modern high-pressure turbine (HPT) nozzle guide vanes (NGVs) operate under non-uniform inlet conditions, including hot streaks and swirl, which can induce complex flow phenomena and uneven thermal loading. These effects, particularly at the hub-vane corner, can compromise NGV durability, yet the combined influence [...] Read more.

Modern high-pressure turbine (HPT) nozzle guide vanes (NGVs) operate under non-uniform inlet conditions, including hot streaks and swirl, which can induce complex flow phenomena and uneven thermal loading. These effects, particularly at the hub-vane corner, can compromise NGV durability, yet the combined influence of swirl and film cooling remains underexplored. The objective of this study is to investigate the aerothermal behaviour of contoured first-stage NGVs under varying swirl intensities and directions to improve understanding of hub and corner thermal protection and failure mechanisms. Steady, compressible RANS simulations were conducted with the k-ω SST turbulence model. A vane with a contoured hub and multiple film cooling rows was designed and analysed under axial and swirling inflows, both clockwise and counter-clockwise, with swirl numbers of Sn = ±0.2 and ±0.4. Axial flow achieved the highest area-averaged film cooling effectiveness (FCE) of 0.617. Negative swirl (Sn = −0.4) improved suction-side corner FCE to 0.215 but reduced pressure-side cooling, whereas positive swirl (Sn = 0.4) improved pressure-side cooling but reduced suction-side FCE to 0.043. Corner temperatures under positive swirl reached 1780 K, consistent with promoting failure, while negative swirl reduced corner temperatures to 1516 K. Aerodynamic losses increased with swirl, with negative swirl generating 5.78% higher total pressure losses than the axial baseline. Swirl altered the corner vortex topology, affecting boundary layer interactions and local heat transfer. These results highlight a trade-off between thermal protection and aerodynamic efficiency, emphasising that optimising NGV performance requires careful design of hub cooling and consideration of swirl direction and intensity. Full article

(This article belongs to the Section Turbomachinery)

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25 pages, 4865 KB

Open AccessArticle

Hybrid Attention-Augmented Deep Reinforcement Learning for Intelligent Machining Process Route Planning

by Ruizhe Wang, Minrui Wang, Ziyan Du, Xiaochuan Dong and Yibing Peng

Machines 2026, 14(3), 343; https://doi.org/10.3390/machines14030343 - 18 Mar 2026

Abstract

Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established [...] Read more.

Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established to formally model the “feature–process–resource–constraint” coupling, enhancing the agent’s perception of manufacturing semantics. The architecture synergistically integrates Graph Attention Networks (GAT) to perceive spatial benchmark dependencies and a Transformer-based encoder to capture sequential resource correlations within variable-length machining chains. Furthermore, a dynamic action masking mechanism is integrated to guarantee a 100% constraint satisfaction rate during both training and inference stages. Experimental evaluations across diverse part geometries demonstrate that the proposed method offers significant advantages in cost optimization, inference efficiency, and topological stability compared to traditional heuristic algorithms and standard DRL models. By effectively distilling the search space and maintaining action feasibility, the framework provides an efficient and robust solution for autonomous process planning in complex industrial scenarios. Full article

(This article belongs to the Section Advanced Manufacturing)

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17 pages, 3940 KB

Open AccessArticle

Unsteady Internal Flow and Cavitation Characteristics of a Hydraulic Dynamometer for Measuring High-Power Gas Turbines

by Ye Yuan, Zhenyang Liu and Qirui Chen

Machines 2026, 14(3), 342; https://doi.org/10.3390/machines14030342 - 18 Mar 2026

Abstract

Hydraulic dynamometer is the key equipment to measure the dynamic performance of high-power gas turbines and steam, with its internal flow characteristics directly influencing measurement accuracy and service life. This paper focuses on the power absorption performance and internal flow characteristics of a [...] Read more.

Hydraulic dynamometer is the key equipment to measure the dynamic performance of high-power gas turbines and steam, with its internal flow characteristics directly influencing measurement accuracy and service life. This paper focuses on the power absorption performance and internal flow characteristics of a hydraulic dynamometer with perforated-disk rotor. A hydraulic test platform is established to measure the power absorption performance of megawatt-level hydraulic dynamometers. When the rotor speed reaches a certain value under the full-water condition, the power absorption of the hydraulic dynamometer reaches its limit. Numerical simulations are applied to study the internal flow characteristics and cavitation evolution features of the perforated-disk-type hydraulic dynamometer. The flow within the outermost rotor pores is the primary factor influencing unsteady flow behaviour, with dynamic–static interference playing a key role in inducing flow excitation. Moreover, cavitation mainly occurs in the flow passages of the end rotor and the outermost flow pores of the middle rotor, where the development and collapse of cavitation bubbles lead to flow instability. As the rotation speed decreases, the power absorption performance significantly decreases under cavitation conditions. These findings provide a theoretical basis for the structural optimization and engineering application of high-power hydraulic dynamometers. Full article

(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)

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