Machines

36 pages, 1244 KB

Open AccessArticle

A Multi-Time-Scale Guaranteed-Cost Coordination Strategy for Networked Integrated Energy Systems over Directed Communication Graphs

by Chao Qin, Jiancheng Zhang, Lingyu Ma and Shanchen Pang

Machines 2026, 14(9), 979; https://doi.org/10.3390/machines14090979 (registering DOI) - 28 Aug 2026

Abstract

The coordinated control of networked integrated energy systems (IESs) is complicated by the different response speeds of the electrical, gas, and thermal subsystems and by information exchange over directed communication graphs. Existing studies mainly consider the economic scheduling of a single IES, dynamic [...] Read more.

The coordinated control of networked integrated energy systems (IESs) is complicated by the different response speeds of the electrical, gas, and thermal subsystems and by information exchange over directed communication graphs. Existing studies mainly consider the economic scheduling of a single IES, dynamic modeling of individual systems, or cooperative control of systems with two time scales, and therefore, they do not provide a unified supervisory framework for coordinating three energy domains with an explicit performance bound. This paper investigates whether a common leader-following framework can coordinate the principal variables of the three energy domains while limiting both regulation errors and control effort. Each IES is treated as an agent, the three energy domains are represented by separate reduced dynamic blocks sharing a common directed communication topology, and a distributed state feedback controller is developed using relative information, Riccati-based gain design, and complete Lyapunov analysis. Simulations of a network of four IESs show that the longest settling times in the electrical and gas domains are approximately 0.05 s and 5.41 s, respectively, whereas the thermal disagreement decreases by 96.5% over 300 s; the accumulated cost remains below its calculated upper bound in the nominal case and in all three time-scale settings, while a separate numerical communication reconfiguration case illustrates bounded responses during communication link removal and reconnection. These results demonstrate that the proposed framework can simultaneously coordinate variables with substantially different response speeds while accounting for regulation accuracy and control effort under the stated reduced model and fixed graph assumptions. The communication reconfiguration case provides a numerical illustration and does not constitute a general stability guarantee for arbitrary topology switching. Full article

(This article belongs to the Special Issue Distributed Control, Coordination and Optimization of Multi-Agent Systems)

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26 pages, 2423 KB

Open AccessArticle

A Lightweight Enhanced MobileNetV3 Method for Bearing Fault Diagnosis Integrating Vibration Signals and Multi-Sensor Features

by Tianwen Guo, Yi Lu, Xiaoguang Wu and Xingyue Cui

Machines 2026, 14(9), 978; https://doi.org/10.3390/machines14090978 (registering DOI) - 28 Aug 2026

Abstract

Rolling bearings are vital for the reliable operation of mechanical systems. However, accurately and efficiently identifying faults under complex working conditions remains a significant challenge. This paper proposes a lightweight and high-precision diagnostic framework specifically designed for industrial edge computing. Specifically, a multi-sensor [...] Read more.

Rolling bearings are vital for the reliable operation of mechanical systems. However, accurately and efficiently identifying faults under complex working conditions remains a significant challenge. This paper proposes a lightweight and high-precision diagnostic framework specifically designed for industrial edge computing. Specifically, a multi-sensor data fusion method, termed GADFMap, is introduced. First, the Wavelet Packet Decomposition (WPD) is applied to extract three sub-band signals with the highest kurtosis values, which are then weighted and combined to generate a new signal. Subsequently, the resulting signals are encoded using Gramian Angular Difference Field (GADF) to effectively integrate the multi-sensor data. Moreover, an improved lightweight diagnostic network, Enhanced MobileNetV3, is developed by augmenting MobileNetV3-Small with Efficient Channel Attention (ECA) modules. This improvement reduces model parameters and computational complexity, while strengthening the model’s focus on salient features. Experimental validation demonstrates that by using the GADFMap and the Enhanced MobileNetV3 model, the proposed method achieves higher diagnostic accuracy with fewer parameters and lower computational complexity compared with mainstream models. Full article

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

34 pages, 2369 KB

Open AccessArticle

Design and Optimization of an Additively Manufactured Two-DOF Tuned Mass Damper for Chatter Stability in Boring Process

by Saravanamurugan Sundaram, Shravan Chidambaresh, Krishna Prakash Jayaprakash, Jana Petru and Thenarasu Mohanavelu

Machines 2026, 14(9), 977; https://doi.org/10.3390/machines14090977 (registering DOI) - 28 Aug 2026

Abstract

Passive tuned mass dampers (TMDs) can reduce chatter, but designing and fabricating an accurately tuned absorber remains challenging due to manufacturing constraints. This study proposes a Design of Experiments and Finite Element Analysis (DOE-FEA) based constrained design optimization framework for a passive two-degree-of-freedom [...] Read more.

Passive tuned mass dampers (TMDs) can reduce chatter, but designing and fabricating an accurately tuned absorber remains challenging due to manufacturing constraints. This study proposes a Design of Experiments and Finite Element Analysis (DOE-FEA) based constrained design optimization framework for a passive two-degree-of-freedom (TDOF) TMD to suppress regenerative chatter in boring operations by considering practical and manufacturing constraints on absorber position, mass ratio, moment of inertia and fixed inter-spring distance. The proposed, additively manufactured TMD housing, made from polylactic acid (PLA), includes a mass block supported by two spring-damper elements that enable coupled translational and rotational interactions with the boring bar. A finite-element forced vibration analysis of the boring bar TMD system is developed to obtain the real and imaginary parts of the frequency response function (FRF), which are then used to construct the stability lobes. The minimum limiting depth of cut over the spindle speed range is used as the optimization criterion, and response surface methodology (RSM) is used to obtain optimum absorber parameters within realistic design constraints. The dynamic behaviour of the TDOF TMD is experimentally and numerically evaluated and compared with that of a single-degree-of-freedom (SDOF) TMD, attributing the relative improvement in performance primarily to the combined effects of independent absorber architecture, mass, stiffness and damping distribution and dynamic tuning. The results showed that the optimal TDOF TMD achieved a DOC of 11.054 mm, while the SDOF TMD achieved 4.335 mm. The experimental investigation of additively manufactured TDOF and SDOF TMDs demonstrated qualitatively similar dynamic phenomena to those of the corresponding numerically optimized absorbers. Time-domain acceleration response, spectrogram and power spectrum were used to compare these dynamic phenomena demonstrated by the SDOF and TDOF TMDs. A reduction in corresponding first and second amplitude peaks from −2.8 dB (670 Hz) and −22.7 dB (1360 Hz) in the case of the SDOF TMD to −17.6 dB (600 Hz) and −23.8 dB (1150 Hz) for the TDOF TMD verified the vibration attenuation and frequency redistribution phenomenon as exhibited by the FE-model. Full article

(This article belongs to the Special Issue Emerging Technologies in Vibration Control: Advances in Suppression and Isolation)

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Graphical abstract

33 pages, 11371 KB

Open AccessArticle

Research on AGV Driving Stability from Multi-Modal Crack Perception to Vibration Constraint Speed Decision

by Penghui Chen, Yang Yang, Changning Zhou, Xiangyu Zhang, Jianglong Li, Yong Liu and Xinyi Liao

Machines 2026, 14(9), 976; https://doi.org/10.3390/machines14090976 (registering DOI) - 28 Aug 2026

Abstract

Pavement cracks pose significant challenges to the driving stability and operational safety of automated guided vehicles (AGV) in industrial and logistics environments. The complex geometry of cracks and their nonlinear coupling with vehicle dynamics make conventional rule-based or single-modal approaches insufficient for reliable [...] Read more.

Pavement cracks pose significant challenges to the driving stability and operational safety of automated guided vehicles (AGV) in industrial and logistics environments. The complex geometry of cracks and their nonlinear coupling with vehicle dynamics make conventional rule-based or single-modal approaches insufficient for reliable engineering decision-making. To address this issue, this study proposes an artificial intelligence–driven multi-modal perception and engineering analysis framework for AGV speed optimization under representative operating conditions. From the artificial intelligence perspective, an improved lightweight instance segmentation model based on YOLO11 is developed by integrating a dynamic upsampling strategy, a hybrid multi-scale feature representation module, and a large-kernel attention mechanism, enabling robust and fine-grained crack extraction in complex pavement scenes. In addition, a multi-modal learning strategy is adopted to fuse two-dimensional visual features with three-dimensional point cloud-derived geometric parameters, allowing accurate quantification of crack width, depth, and surface damage. From the engineering analysis perspective, the relationship between AI-extracted crack geometric characteristics and AGV dynamic responses is established to investigate the influence of pavement defects on vehicle vibration behavior. A triaxial vibration acquisition system is constructed under controlled experimental conditions, and the relationship between crack severity, vibration characteristics, and driving speed is quantitatively analyzed. Based on vibration constraints, a hierarchical speed optimization strategy is formulated for different crack levels. Experimental results demonstrate that the proposed method achieves accurate crack perception and effective geometric feature characterization, providing a quantitative basis for AGV speed adjustment under different pavement conditions. Full article

(This article belongs to the Section Vehicle Engineering)

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26 pages, 54888 KB

Open AccessArticle

Unmanned Ground Vehicle Following System Based on Adaptive Kalman Filtering and State Perception Control

by Zhigang Zhang, Shiqun Tang, Xiaoxia Yu, Liping Liu and Zhige Chen

Machines 2026, 14(9), 975; https://doi.org/10.3390/machines14090975 (registering DOI) - 28 Aug 2026

Abstract

Intelligent following control for electric-drive unmanned ground vehicles (UGVs) is important for human–robot collaboration and autonomous mobility. However, target occlusion, detection noise, and short-term detection failures can reduce following stability and reliability. To address these challenges, this paper proposes a vision-based following method [...] Read more.

Intelligent following control for electric-drive unmanned ground vehicles (UGVs) is important for human–robot collaboration and autonomous mobility. However, target occlusion, detection noise, and short-term detection failures can reduce following stability and reliability. To address these challenges, this paper proposes a vision-based following method that integrates adaptive Kalman filtering with target-state-aware control. The proposed method updates the observation noise covariance online using an innovation-sequence sliding window and evaluates the validity of visual measurements based on the intersection over union between adjacent frames, thereby enabling target position prediction and compensation under unstable observation conditions. Meanwhile, the longitudinal velocity and lateral angular velocity are dynamically adjusted according to the target detection state, image-center offset, and distance variation to achieve continuous and stable following control. Experimental results show that the proposed method reduces the target position prediction error by approximately 10% compared with conventional Kalman filtering and maintains the longitudinal following distance at about 3 m, improving the robustness, continuity, and motion smoothness of electric-drive UGV target following in complex environments. Full article

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

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20 pages, 3927 KB

Open AccessArticle

Real-Time Thermal Comfort-Oriented NMPC for Electric Vehicle Heat Pump Systems Using a Control-Oriented PMV Model

by Tai-Gon Kim, Hyunsang Wang, Wansik Choi and Changsun Ahn

Machines 2026, 14(9), 974; https://doi.org/10.3390/machines14090974 (registering DOI) - 28 Aug 2026

Abstract

Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for [...] Read more.

Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for passenger thermal comfort. This study proposes a real-time thermal comfort-oriented nonlinear model predictive control (NMPC) framework for EV heat pump systems. To enable real-time implementation, a control-oriented model is developed by combining a data-driven model for heat pump performance with a linearized Predicted Mean Vote (PMV) model. The proposed NMPC optimizes the trade-off between passenger thermal comfort and compressor energy consumption while satisfying actuator constraints. A high-fidelity physics-based virtual plant is employed to evaluate the proposed strategy. Simulation results show that the proposed NMPC maintains thermal comfort within the recommended PMV range while reducing total energy consumption by 8.3% compared with a conventional rule-based controller. Furthermore, a parametric study involving 1210 gain-tuning cases of a rule-based controller and 100 NMPC weighting scenarios demonstrates a consistently superior comfort–energy trade-off. The average computation time remains below 0.15 s on the simulation platform, indicating that the proposed NMPC formulation can be solved within the selected sampling interval. These results highlight the potential of thermal comfort-oriented NMPC for improving both passenger comfort and energy efficiency in EV heat pump systems. Full article

(This article belongs to the Special Issue Dynamics and Control of Electric Vehicles)

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31 pages, 11338 KB

Open AccessArticle

Multi-Objective Optimization of Wire Electrical Discharge Machining Parameters in Machining of Inconel 625 Using Bio-Inspired Optimization Algorithms

by Thangamuthu Mohanraj, Kumararathinam Narendran, Gandhi Ajay, Murugesan Rakshan, Ayyagounder Shanmugam and Patrick Siarry

Machines 2026, 14(9), 973; https://doi.org/10.3390/machines14090973 (registering DOI) - 28 Aug 2026

Abstract

Wire Electrical Discharge Machining (WEDM) is highly significant for machining Inconel, which is widely used across industries for its excellent strength and heat resistance. Traditional machining of Inconel poses several difficulties; therefore, WEDM becomes a better option. One of the major challenges in [...] Read more.

Wire Electrical Discharge Machining (WEDM) is highly significant for machining Inconel, which is widely used across industries for its excellent strength and heat resistance. Traditional machining of Inconel poses several difficulties; therefore, WEDM becomes a better option. One of the major challenges in WEDM is achieving optimal Material Removal Rate (MRR) and Surface Roughness (SR) simultaneously. In the present investigation, WEDM operation was conducted on Inconel 625 using a molybdenum wire as the electrode material, with three essential process variables: Pulse ON Time (P_ON), Pulse OFF Time (P_OFF), and peak current (I_P). Multi-response optimization was performed using Response Surface Methodology (RSM) with the Face-centred Central Composite (FCC) design, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Particle Swarm Optimization (PSO). The optimal values obtained using RSM were 45.71 µs for P_ON, 8.02 µs for P_OFF, and 4.78 A for I_P, providing an SR of 3.997 µm and an MRR of 5.612 mm³/min. PSO focused more on the quality of the processed surface and gave a better SR of 3.098 µm, but the resulting MRR was 4.437 mm³/min using 20 µs for P_ON, 12 µs for P_OFF, and 3 A for I_P. Conversely, the NSGA-II optimized for maximal machining productivity and generated the highest MRR of 5.907 mm³/min with an SR of 3.867 µm using 49.42 µs for P_ON, 8 µs for P_OFF, and 5 A for I_P. Novelty lies in the use of statistics and metaheuristics to compare the performance of optimization methods in nonlinear parameter response analysis. Although RSM demonstrated high predictive accuracy, both the NSGA-II and PSO improved the search for the trade-off between MRR and SR. Overall, the best compromise between MRR and SR was achieved using the solution based on the NSGA-II results. The obtained results will help in developing advanced machining technologies aimed at precision processing of superalloys that contribute to SDG 9—Industry, Innovation and Infrastructure. Full article

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

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15 pages, 7054 KB

Open AccessArticle

See, Spawn, Synchronize: A Digital Twin Pipeline for Smart Production Cells

by Malte Herrmann, Dominykas Strazdas and Ayoub Al-Hamadi

Machines 2026, 14(9), 972; https://doi.org/10.3390/machines14090972 (registering DOI) - 28 Aug 2026

Abstract

With Industry 5.0, human–robot collaboration has become the center of attention. This has introduced new challenges, where workspaces become highly dynamic, leading to safety concerns for robots and especially for humans. This makes it important to have a realistic and accurate digital representation [...] Read more.

With Industry 5.0, human–robot collaboration has become the center of attention. This has introduced new challenges, where workspaces become highly dynamic, leading to safety concerns for robots and especially for humans. This makes it important to have a realistic and accurate digital representation of a production cell and its components in environments for movement planning and remote supervision. This paper presents a digital twin of a smart production cell, synchronizing objects bidirectionally between a real and virtual workspace with minimal effort using only a single RGB-D camera. A fine-tuned YOLO-based detector identifies tools and items in the scene, estimates their spatial position, and spawns them in Unity relative to the robot via coordinate transformation. Experiments with different scanning velocities demonstrate a mean planar spawn deviation of

5.82 mm

(standard deviation

2.24 mm

) at

0.1 m / s

at

0.7 m

height, while maintaining a constant depth bias of

- 0.54 mm

. Once spawned, objects can be manipulated freely via drag-and-drop within the simulation. Upon confirmation, a motion planning module calculates trajectories to execute these changes physically. Across 95 trials and 1805 object placements, the system achieves 100% success within working bounds, successfully executing complex tasks such as repositioning objects and stacking them into pyramid structures. The presented system provides a framework to see, spawn, and synchronize industrial workspaces, enabling rapid setup and safe remote supervision of smart production cells in highly dynamic industrial environments. Full article

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

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24 pages, 2000 KB

Open AccessArticle

A Boundary Virtual Load Method for Prestress-Field Prediction in Corner-Tensioned Membranes

by Wenyao Zhang, Kun Guo, Chunlong Wang, Chuang Shi, Hongwei Guo and Rongqiang Liu

Machines 2026, 14(9), 971; https://doi.org/10.3390/machines14090971 - 27 Aug 2026

Abstract

Flexible membrane structures, such as solar sails and membrane reflectors, rely on accurate characterization of in-plane prestress for structural reliability and functional performance. This study proposes a Boundary Virtual Load Method (BVLM) for the semi-analytical prediction of prestress fields in corner-tensioned rectangular membranes. [...] Read more.

Flexible membrane structures, such as solar sails and membrane reflectors, rely on accurate characterization of in-plane prestress for structural reliability and functional performance. This study proposes a Boundary Virtual Load Method (BVLM) for the semi-analytical prediction of prestress fields in corner-tensioned rectangular membranes. An initial corner-dominated radial stress field is constructed by superposing the stress solutions generated by four corner loads. Residual normal stresses along the nominally free edges are then evaluated, and boundary virtual loads of equal magnitude and opposite direction are introduced to approximate the zero-normal-traction condition. Polynomial representations of these virtual-load distributions are incorporated into the Airy stress-function framework, yielding a boundary-corrected closed-form prestress solution. Comparisons with finite element results for three membrane configurations show that BVLM accurately captures the principal spatial characteristics and aspect-ratio-dependent evolution of the prestress field while requiring substantially lower computational cost for the cases examined. The predicted prestress fields are further incorporated into a free-vibration model that accounts for the added mass of the surrounding air. The resulting natural frequencies generally reproduce the experimentally measured modal-frequency trends, although mode-dependent discrepancies are observed for several modes. The 12 frequency comparisons yield a mean absolute relative error of

8.46 %

, indicating reasonable overall agreement for the approximate analytical formulation. These comparisons provide an indirect dynamic consistency assessment rather than a direct validation of the spatial prestress field. BVLM therefore offers an efficient framework for prestress analysis, preliminary vibration prediction, and parametric design of corner-tensioned membranes. Full article

(This article belongs to the Special Issue Smart Structures and Applications in Aerospace Engineering)

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22 pages, 11600 KB

Open AccessArticle

Experimental Investigation of the Impact of Cable Length and Type on Motor Overvoltages, Shaft Voltage, and Bearing Currents in PWM-Inverter-Fed Drive Systems

by Fawzy A. Abdo, Mehmet Güleç, Kotb B. Tawfiq and Peter Sergeant

Machines 2026, 14(9), 970; https://doi.org/10.3390/machines14090970 - 27 Aug 2026

Abstract

Fast-switching transients and high dv/dt associated with PWM inverters exacerbate the reflected-wave effects in the motor feeder cable. This leads to higher motor-side overvoltages, which increase the stress on the motor winding insulation, potentially accelerating insulation degradation and increasing the risk [...] Read more.

Fast-switching transients and high dv/dt associated with PWM inverters exacerbate the reflected-wave effects in the motor feeder cable. This leads to higher motor-side overvoltages, which increase the stress on the motor winding insulation, potentially accelerating insulation degradation and increasing the risk of partial discharge within the motor windings. Moreover, the common-mode voltages at the motor terminals propagate through parasitic capacitive paths within the motor, inducing shaft voltages that lead to electric discharge machining (EDM) currents and premature bearing failure. This paper experimentally investigates the influence of motor feeder cable length and type (shielded and unshielded) on motor terminal overvoltage, shaft voltage, and bearing current behavior in an inverter-fed 11 kW permanent magnet synchronous motor drive system. Three cable lengths (1 m, 3 m, and 16 m) with shielded and unshielded configurations are evaluated under identical operating conditions. Measurements of line-to-line voltage, line-to-ground voltage, shaft voltage, bearing current, and EDM discharge currents are recorded and statistically analyzed to assess the influence of cable configuration. The study also examines the influence of the motor grounding configuration on common-mode current and bearing current behavior. Overall, the findings provide comprehensive insights into the influence of motor feeder cable on overvoltage, shaft voltage, and bearing discharge behavior, supporting informed cable selection for WBG inverter-fed electric drives. Full article

(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction, 3rd Edition)

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32 pages, 1969 KB

Open AccessArticle

An Adaptive Co-Evolutionary Memetic Algorithm for a Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup and Transportation Times

by Dekun Wang, Yu Lei, Zhengang Yuan, Yuhao Zhao, Yubin Wang, Wenjie Wang and Gang Yuan

Machines 2026, 14(9), 969; https://doi.org/10.3390/machines14090969 - 27 Aug 2026

Abstract

The hybrid flow shop scheduling problem (HFSP) with unrelated parallel machines (UPMs), sequence-dependent setup times (SDSTs), and inter-stage transportation times has recently emerged as a prominent research topic. To address this scheduling problem with the objective of minimizing the maximum completion time (makespan), [...] Read more.

The hybrid flow shop scheduling problem (HFSP) with unrelated parallel machines (UPMs), sequence-dependent setup times (SDSTs), and inter-stage transportation times has recently emerged as a prominent research topic. To address this scheduling problem with the objective of minimizing the maximum completion time (makespan), this paper first formulates a mixed-integer linear programming (MILP) model based on the machine-position modeling idea. Exact solution analyses on small-scale instances reveal that the strong coupling effect of these triple constraints concentrates the computational bottleneck on the time-consuming proof of optimality, thereby underscoring the strongly NP-hard nature of the investigated HFSP-SDST-T problem. To efficiently solve large-scale instances, a novel adaptive co-evolutionary memetic algorithm (ACMA) is proposed. ACMA adopts a dual-population co-evolutionary framework, where a customized genetic algorithm (GA) is designed for global exploration and a Lévy flight-enhanced particle swarm optimization (PSO) improves local search capability. To dynamically balance exploration and exploitation, a Dynamic Role Allocation (DRA) mechanism is developed to adaptively reassign individuals between the two populations according to their evolutionary states. Moreover, a progressive two-stage memetic enhancement strategy is proposed to overcome premature convergence by sequentially activating deep variable neighborhood search (VNS) and a catastrophe-based diversification strategy, enabling adaptive responses to different stagnation levels. Extensive experiments, including ablation studies, comparisons with benchmark algorithms, and computational complexity analysis, are conducted on small- and large-scale instances. The results show that ACMA consistently obtains the exact optimal solutions obtained from the MILP model for small-scale instances and achieves competitive performance on large-scale complex instances. Furthermore, Wilcoxon signed-rank tests confirm the statistical significance of the performance differences, supporting the reliability of the experimental results. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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21 pages, 9871 KB

Open AccessArticle

Research on the Application of Incremental Approximation Models in Hull Form Optimization Design

by Haichao Chang, Qiyang Zhang and Pei Liu

Machines 2026, 14(9), 968; https://doi.org/10.3390/machines14090968 - 26 Aug 2026

Abstract

To address the problems in hull form optimization where an increase in design variables requires approximation models to be rebuilt from scratch and historical CFD samples are insufficiently utilized, this paper proposes an incremental approximation model construction method. Based on the additive decomposition [...] Read more.

To address the problems in hull form optimization where an increase in design variables requires approximation models to be rebuilt from scratch and historical CFD samples are insufficiently utilized, this paper proposes an incremental approximation model construction method. Based on the additive decomposition property of high-dimensional model representation, this method breaks through the rigid structure limitations of traditional approximation models. When dimension expansion occurs in the design space, it fully inherits existing low-order component models and historical sample point databases, requiring only local supplementary sampling and incremental construction for new variables and their strong coupling terms, thereby achieving adaptive cross-dimensional updates of the approximation model. The method is validated through numerical test functions and a container ship hull line resistance optimization case study. Results show that, compared with traditional full-dimensional approximation models, this method reduces full-space resampling overhead and maintains high prediction accuracy while reducing CFD sample requirements, providing an efficient modeling approach for ship hydrodynamic optimization in high-dimensional dynamic spaces. Full article

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21 pages, 2646 KB

Open AccessArticle

The Choice of Static Recovery Term Description to Address the Effect of Hold Time Periods of Load Cycles on the Ratcheting of Steel Samples at Room Temperature

by Petar Jevtic and Ahmad Varvani-Farahani

Machines 2026, 14(9), 967; https://doi.org/10.3390/machines14090967 - 26 Aug 2026

Abstract

The present study evaluates three static recovery term (SRT) formulations incorporated into the Ahmadzadeh–Varvani (A-V) kinematic hardening framework for predicting ratcheting under tensile peak hold loading at room temperature. Linear, power-law, and nonlinear SRTs were assessed using experimental data for austenitic stainless steels [...] Read more.

The present study evaluates three static recovery term (SRT) formulations incorporated into the Ahmadzadeh–Varvani (A-V) kinematic hardening framework for predicting ratcheting under tensile peak hold loading at room temperature. Linear, power-law, and nonlinear SRTs were assessed using experimental data for austenitic stainless steels SUS304 and SS304. The constitutive parameters were first calibrated using monotonic, strain-controlled, hysteresis loop, and no-hold ratcheting data. The SRT coefficients were then calibrated using peak hold experiments involving hold durations of 60 s for SUS304 and 10 s for SS304. All three formulations reproduced the increased hysteresis loop translation and ratcheting strain caused by the tensile holds more accurately than the baseline model without static recovery, which underpredicted ratcheting strain by an absolute strain difference up to 1.5%. The three SRTs produced comparable overall ratcheting predictions after calibration; however, they generated different coefficient evolutions, recovery histories, and intermediate cycle responses. The nonlinear formulation provided improved agreement for portions of the SUS304 loop evolution, while the linear model offered the simplest implementation. The results demonstrate that static recovery is essential for modelling dwell-assisted ratcheting and that model selection should consider internal variable evolution in addition to final accumulated strain. Full article

(This article belongs to the Special Issue Fatigue Life Prediction of Mechanical Components)

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31 pages, 26630 KB

Open AccessArticle

A Self-Referencing Framework for Milling Tool Wear Diagnosis Under Tool-to-Tool Variability Using Physics-Informed Order-Tracked Features

by Soon-Hyun Lim and Jong-Myon Kim

Machines 2026, 14(9), 966; https://doi.org/10.3390/machines14090966 - 26 Aug 2026

Abstract

Tool wear degrades machining quality and, if unchecked, can progress to breakage, causing workpiece defects, downtime, and spindle damage; automatic tool condition monitoring is therefore essential. In real production, new and reground tools are used interchangeably and differ slightly in geometry and material, [...] Read more.

Tool wear degrades machining quality and, if unchecked, can progress to breakage, causing workpiece defects, downtime, and spindle damage; automatic tool condition monitoring is therefore essential. In real production, new and reground tools are used interchangeably and differ slightly in geometry and material, so the “normal” baseline shifts from one tool and mounting to the next. This makes both global-baseline diagnostics and deep-learning methods that require large labeled fault datasets difficult to apply. This study proposes a lightweight, self-referencing framework—whose per-tool baseline is built from acceptable-state data alone—for diagnosing milling tool wear under tool-to-tool variability. Its novelty lies not in the individual techniques—self-referencing, order tracking, and the Mahalanobis distance, which are established—but in their integration into a single framework, designed for fault-label-free operation, that rebuilds a dedicated baseline for every newly mounted tool. Whenever a tool is mounted, its own acceptable data, a short initial segment of machining taken as healthy immediately after mounting, form the baseline; kinematics-based order-tracked features from a single spindle-bearing accelerometer are used to compute the Mahalanobis distance from the acceptable state, which serves as a continuous health index. A warning limit set statistically from the acceptable data alone, together with a defect limit set as a pragmatic engineering multiple of it, separates the acceptable, warning, and defect grades. On four end mills of identical specification, the primary full-baseline analysis yielded a warning-detection AUC of 0.986 and a defect-detection AUC of 0.936; with a persistence rule, defect-grade wear was detected in all four tools with no false alarms in the acceptable state. Because the baseline is built from only a short acceptable segment and the computation is inexpensive, the framework is, in principle, suited to shop floors with frequent tool changes and to on-machine or edge deployment (not yet benchmarked on an edge device); the present validation, however, is limited to four tools and a single workpiece material under fixed cutting conditions with accelerated wear, so broader verification remains necessary. Full article

(This article belongs to the Special Issue Artificial Intelligence Approaches for Tool Condition Monitoring)

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22 pages, 5539 KB

Open AccessArticle

Modular Performance Testing and Comparative Evaluation Method for Wind Turbine Retrofit Schemes

by Fengkun Ji, Fuqing Yang, Zhenfeng Wang, Siyuan Liu, Duowang Xu, Wei Zhou, Linjing Wu, Xuyang Chu, Yuchen Zhong and Yuzhi Ke

Machines 2026, 14(9), 965; https://doi.org/10.3390/machines14090965 - 26 Aug 2026

Abstract

To overcome the limitations of existing evaluation methods and performance testing for wind power generation systems, this study proposes a modular framework for performance testing and comparative assessment. Methodologically, the approach establishes a baseline configuration for simulation and provides optional interfaces for experimental, [...] Read more.

To overcome the limitations of existing evaluation methods and performance testing for wind power generation systems, this study proposes a modular framework for performance testing and comparative assessment. Methodologically, the approach establishes a baseline configuration for simulation and provides optional interfaces for experimental, hardware-in-the-loop, or bench testing under identical boundary conditions. By employing a unified metric system, the proposed method enables a comprehensive evaluation of annual energy production (AEP) gains, power curve deviations, damage equivalent loads (DEL) when cycle-resolved load histories are available, fatigue- and peak load proxy variations, efficiency fluctuations, temperature rise margins, and reliability proxy indicators. A demonstrative case study is conducted using illustrative numerical data parameterized for a generic 2.5 MW-class doubly fed wind turbine to compare three retrofit schemes: blade replacement, gearbox optimization (S2), and a pitch system upgrade. When annual energy production (AEP) is utilized as the sole metric, the blade replacement scheme yields a 3.32% increase. However, it concurrently increases the fatigue load and peak load proxies by 6.11% and 3.72%, respectively. Conversely, a comprehensive assessment incorporating load, temperature rise, vibration, and reliability identifies the gearbox optimization (S2 scheme) as the highest-ranked option under the current weighting configuration, with a reproducible overall score of 0.65. The weight sensitivity analysis further shows that the preferred scheme can change when engineering priorities change. Ultimately, this work demonstrates the proposed method’s capability to highlight the discrepancy between single-metric and holistic performance optimization, providing standardized support for scheme selection, project acceptance, and the evaluation of wind turbine retrofit schemes. Full article

(This article belongs to the Special Issue High Performance and Hybrid Manufacturing Processes, 2nd Edition)

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20 pages, 2580 KB

Open AccessArticle

A Hybrid Attention-Enhanced Transformer for Short-Term Attitude Vibration Prediction of Robotic Aerial Work Platforms

by Jiayu Guo, Mingming Lv, Mengyao Si, Haonan Hu and Wei Zhong

Machines 2026, 14(9), 964; https://doi.org/10.3390/machines14090964 - 25 Aug 2026

Abstract

Robotic Aerial Work Platforms (RAWPs) are subjected to multi-source excitations including wind gusts and inertial loads, which result in strongly nonlinear and time-varying coupled triaxial attitude vibrations. Conventional recurrent architectures such as LSTM and GRU suffer from gradient vanishing when processing long sequences, [...] Read more.

Robotic Aerial Work Platforms (RAWPs) are subjected to multi-source excitations including wind gusts and inertial loads, which result in strongly nonlinear and time-varying coupled triaxial attitude vibrations. Conventional recurrent architectures such as LSTM and GRU suffer from gradient vanishing when processing long sequences, while Transformer utilize self-attention mechanisms to learn simple periodic correlations; however, the vibrations in RAWPs exhibit a complex time-series pattern composed of low-frequency oscillations superimposed with high-frequency impacts and accumulates errors through autoregressive decoding. To address these limitations, this paper proposes an improved Transformer model featuring dual-channel periodic positional encoding and global–local hybrid multi-head attention for one-shot multi-step long-sequence prediction of RAWPs attitude vibrations. The proposed method designs a dual-channel independent sine–cosine positional encoding with a tunable periodic modulation factor to explicitly embed the multi-scale periodicity priors of vibration signals and introduces a global–local hybrid attention mechanism that parallelly extracts transient amplitude impact features in the time domain and periodic fluctuation features in the frequency domain. A full-scale aerial experimental platform is established to collect triaxial vibration data under two operating conditions at a sampling frequency of 20 Hz. The results determine the optimal periodic modulation factor and input window length, and ablation studies validate the synergistic gains of the two proposed modules. Comparative results demonstrate that the proposed model achieves substantially reduced prediction errors. In terms of pitch angle, the proposed model achieves a performance improvement of 53.89% over Transformer, 52.11% over LSTM, and 57.67% over GRU. The proposed model effectively provides a reliable data-driven prediction framework for attitude monitoring and active vibration suppression of aerial work platforms. Full article

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

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22 pages, 1555 KB

Open AccessArticle

Actor–Critic Predefined-Time Adaptive Tracking Control for Partially Unknown Euler–Lagrange Systems

by Tao Wang, Yuan Sun, Yong Qin and Jun Huang

Machines 2026, 14(9), 963; https://doi.org/10.3390/machines14090963 - 25 Aug 2026

Abstract

This paper studies predefined-time trajectory tracking for Euler–Lagrange systems with composite uncertainties encompassing partially known dynamics, parametric variations, and bounded disturbances. A two-step backstepping architecture is developed. In Step 1, a predefined-time virtual control law is constructed for the position subsystem. In Step [...] Read more.

This paper studies predefined-time trajectory tracking for Euler–Lagrange systems with composite uncertainties encompassing partially known dynamics, parametric variations, and bounded disturbances. A two-step backstepping architecture is developed. In Step 1, a predefined-time virtual control law is constructed for the position subsystem. In Step 2, an energy-based torque controller is designed for the velocity subsystem using nominal model compensation, adaptive parameter estimation, actor neural network approximation of residual dynamics, a critic network for performance-oriented learning, and a continuous robust term. A rigorous Lyapunov analysis shows that all closed-loop signals are uniformly ultimately bounded and that the tracking errors converge to a computable residual set within a predefined time for all initial conditions contained in the selected compact set. Comparative simulations on a 2-DOF planar manipulator demonstrate that the proposed method provides faster convergence and improved steady-state tracking accuracy than both a PID baseline and a classical Slotine–Li adaptive baseline, while respecting the predefined-time bound. Full article

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

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22 pages, 4971 KB

Open AccessArticle

Temperature Field-Based Detection of Oil Supply Failure in Plain Bearings Considering Varying Component Sizes

by Thao Baszenski, Karl-Heinz Kratz, Georg Jacobs, Tobias Gemmeke, Benjamin Lehmann and Mattheüs Lucassen

Machines 2026, 14(9), 962; https://doi.org/10.3390/machines14090962 - 25 Aug 2026

Abstract

Plain bearings are widely used in heavy-duty applications, e.g., wind turbine drivetrains or ship propulsion systems. Plain bearings offer high load-carrying capacity and good damping, but abnormal events such as oil supply failure (OSF) can rapidly damage the bearing and cause failure of [...] Read more.

Plain bearings are widely used in heavy-duty applications, e.g., wind turbine drivetrains or ship propulsion systems. Plain bearings offer high load-carrying capacity and good damping, but abnormal events such as oil supply failure (OSF) can rapidly damage the bearing and cause failure of the entire drivetrain. An adequate oil supply is essential for the operation of the plain bearing. A failure of the oil supply can cause fatal failure of the bearing due to adhesive wear within a matter of seconds. Existing condition monitoring systems (CMS) for plain bearings generally cannot detect OSF in time, or require costly and complex installation, limiting their practical applicability. This paper presents temperature field measurement (TFM) as a simple, low-cost CMS approach for early OSF detection. The results presented within this work demonstrate that TFM detects the onset of OSF within 30 s of oil supply interruption, at least 30 s before a rise in friction torque can be detected in the corresponding test bearing. The findings demonstrate that TFM is a valid, simple, and effective method for the timely detection of OSF, offering a practical alternative to existing CMS approaches for heavy-duty plain bearing applications. Full article

(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)

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21 pages, 12370 KB

Open AccessArticle

Study on Fatigue Crack Propagation Caused by Sensor Slots in Intelligent Tapered Bearings

by Longkai Wang, Fengyuan Liu, Yangyan Zhang and Yijun Yin

Machines 2026, 14(9), 961; https://doi.org/10.3390/machines14090961 - 25 Aug 2026

Abstract

Electric-shovel top sheave bearings with sensor-embedded slots operate under harsh service loads, making them prone to fatigue crack initiation and propagation. Accurate predictions of crack growth within the bearing body are therefore essential for intelligent bearing design and reliability assessments because the bearing [...] Read more.

Electric-shovel top sheave bearings with sensor-embedded slots operate under harsh service loads, making them prone to fatigue crack initiation and propagation. Accurate predictions of crack growth within the bearing body are therefore essential for intelligent bearing design and reliability assessments because the bearing integrity directly affects shovel service life and safety. This paper presents a sub-modeling-based method that embeds initial cracks while preserving actual roller-ring boundary conditions and ensuring computational efficiency via adaptive mesh refinement. A global model first identifies critical crack-prone zones, after which the sub-model systematically examines the effects of the initial crack angle and sensor-embedded slot depth on the propagation behavior. The results indicate that both factors significantly increased the stress intensity factor (SIF). Among the evaluated designs, the 15 mm -deep slot produced the highest SIFs and the shortest predicted crack-propagation life, indicating that slot depth was a key design parameter under the investigated conditions. The findings provide theoretical support for the structural design and fatigue evaluation of intelligent electric-shovel top sheave bearings. Full article

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

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