Machines

20 pages, 1774 KB

Open AccessArticle

Title Discrete-Time Fast Terminal Sliding Mode Control Based on Generalized Implicit Reaching Law for Electronic Throttle

by Wenjiang Zhu, Jie Zhang, Yinhui Yu and Wenqian Liu

Machines 2025, 13(12), 1153; https://doi.org/10.3390/machines13121153 - 18 Dec 2025

Viewed by 243

Abstract

To achieve precise tracking control under the hardware of electronic throttle digital control, this paper proposes a novel implicit discrete-time fast terminal sliding mode control method for electronic throttles. Specifically, a recursive discrete-time fast terminal sliding surface is adopted, which can ensure that [...] Read more.

To achieve precise tracking control under the hardware of electronic throttle digital control, this paper proposes a novel implicit discrete-time fast terminal sliding mode control method for electronic throttles. Specifically, a recursive discrete-time fast terminal sliding surface is adopted, which can ensure that the system error converges to a smaller error bandwidth. To further eliminate the chattering problem in actual systems, a new generalized implicit discrete reaching law is designed. Compared with the traditional explicit Euler reaching law, the proposed implicit reaching law can effectively suppress the chattering caused by discretization through backward discretization (implicit discretization). Meanwhile, compared with existing implicit discrete reaching laws, the proposed reaching law has an explicit recursive equation, avoiding the problems of high computational complexity and difficult engineering implementation. Subsequently, strict theoretical analysis proves the existence of the quasi-sliding mode (QSM) and the convergence of the system tracking error. Moreover, experimental results demonstrate that the proposed controller achieves faster transient response, smaller steady-state tracking error, and significantly reduced chattering compared with DFSM, DSTWISTING, and DLSMC, highlighting its clear performance advantages over existing sliding mode methods. Full article

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19 pages, 21965 KB

Open AccessArticle

A Hybrid Strategy for the Design and Optimization of Coaxial Magnetic Gears

by Xinyan Zhang, Renato Galluzzi and Nicola Amati

Machines 2025, 13(12), 1152; https://doi.org/10.3390/machines13121152 - 18 Dec 2025

Viewed by 324

Abstract

Magnetic gear transmissions are promising alternatives to mechanical ones due to their contactless power transfer, reduced acoustic noise and vibration, inherent overload protection, and improved reliability. However, their design requires fast but accurate tools. While three-dimensional finite-element models offer good accuracy, their complexity [...] Read more.

Magnetic gear transmissions are promising alternatives to mechanical ones due to their contactless power transfer, reduced acoustic noise and vibration, inherent overload protection, and improved reliability. However, their design requires fast but accurate tools. While three-dimensional finite-element models offer good accuracy, their complexity hinders their use for design purposes. Two-dimensional representations, on the other hand, tend to overestimate performance due to the lack of end effects in the axial direction. This paper proposes a hybrid design and optimization framework for coaxial magnetic gears that couples a two-dimensional optimizer based on a genetic algorithm with a three-dimensional parametric model. The former model helps identify promising combinations in the design variable space. Then, specific selections are refined through the three-dimensional model. Numerical results show that both approaches exhibit consistent parameter trends, with a resulting prototype yielding a torque density of 213 Nm/L in an envelope contained within 90 mm of diameter and 16.57 mm of active length. Full article

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

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21 pages, 2476 KB

Open AccessArticle

Energy-Model-Based Global Path Planning for Pure Electric Commercial Vehicles Toward 3D Environments

by Kexue Lai, Dongye Sun, Binhao Xu, Feiya Li, Yunfei Liu, Guangliang Liao and Junhang Jian

Machines 2025, 13(12), 1151; https://doi.org/10.3390/machines13121151 - 17 Dec 2025

Viewed by 273

Abstract

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these [...] Read more.

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles. Full article

(This article belongs to the Section Vehicle Engineering)

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18 pages, 3770 KB

Open AccessArticle

Fractional-Order Nonlinear PI Control for Tracking Wind Direction in Large Wind Energy Converters

by Adrian Gambier

Machines 2025, 13(12), 1150; https://doi.org/10.3390/machines13121150 - 17 Dec 2025

Viewed by 234

Abstract

In this work, the yaw control of large wind turbines is studied. The objective is to analyse how to maximise energy conversion by yawing the rotor in response to wind direction while minimising yaw activity. In order to improve the control performance, three [...] Read more.

In this work, the yaw control of large wind turbines is studied. The objective is to analyse how to maximise energy conversion by yawing the rotor in response to wind direction while minimising yaw activity. In order to improve the control performance, three algorithms are used and compared: the classic PI controller, the nonlinear PI controller, and the fractional-order nonlinear PI controller. An adaptive dead-zone and anti-windup procedure for amplitude- and rate-limited actuators are also considered, which helps to reach the main objective. Simulation experiments are carried out on a 20 MW reference wind turbine. The results are very promising, showing clear performance improvements. Full article

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

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23 pages, 7009 KB

Open AccessArticle

Design and Anti-Impact Performance Study of a Parallel Vector Thruster

by Liangxiong Dong and Jubao Li

Machines 2025, 13(12), 1149; https://doi.org/10.3390/machines13121149 - 17 Dec 2025

Viewed by 304

Abstract

With the rapid development of unmanned surface vessels (USVs), a vector thruster was designed in this paper to meet their evolving operational demands. The anti-impact capability of the vector thruster, in which the universal joint plays a critical role in attenuating impact loads, [...] Read more.

With the rapid development of unmanned surface vessels (USVs), a vector thruster was designed in this paper to meet their evolving operational demands. The anti-impact capability of the vector thruster, in which the universal joint plays a critical role in attenuating impact loads, directly governs the stability and security of power transmission in USVs. A mechanical model of the vector thruster with a universal joint was established, incorporating length and stiffness ratio coefficients to characterize its key dynamics. Based on this model, numerical simulation using the Newmark method was conducted to systematically evaluate the thruster’s mechanical characteristics, particularly the dynamic variation of the inclination angle, under various working conditions and impact loads. The results indicate that an increase in stiffness ratio amplifies the angular displacement amplitude of the driven shaft but shortens the vibration stabilization time. During the operation of the vector thruster, an increase in the inclination angle leads to greater vibration amplitude. Furthermore, systems with a higher, longer ratio exhibit a more pronounced tendency for amplitude growth as the inclination angle increases. Finally, the theoretical model was validated through a test bench, and the variation pattern of dynamic thrust under impact load was revealed. These results emphasize that the stiffness and dimensional parameters must be carefully considered in the design and control optimization of vector thrusters to ensure reliable performance under demanding operational conditions. Full article

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

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64 pages, 4380 KB

Open AccessArticle

Adaptive Multi-Objective Reinforcement Learning for Real-Time Manufacturing Robot Control

by Claudio Urrea

Machines 2025, 13(12), 1148; https://doi.org/10.3390/machines13121148 - 17 Dec 2025

Viewed by 919

Abstract

Modern manufacturing robots must dynamically balance multiple conflicting objectives amid rapidly evolving production demands. Traditional control approaches lack the adaptability required for real-time decision-making in Industry 4.0 environments. This study presents an adaptive multi-objective reinforcement learning (MORL) framework integrating dynamic preference weighting with [...] Read more.

Modern manufacturing robots must dynamically balance multiple conflicting objectives amid rapidly evolving production demands. Traditional control approaches lack the adaptability required for real-time decision-making in Industry 4.0 environments. This study presents an adaptive multi-objective reinforcement learning (MORL) framework integrating dynamic preference weighting with Pareto-optimal policy discovery for real-time adaptation without manual reconfiguration. Experimental validation employed a UR5 manipulator with RG2 gripper performing quality-aware object sorting in CoppeliaSim with realistic physics (friction μ = 0.4, Bullet engine), manipulating 12 objects across four geometric types on a dynamic conveyor. Thirty independent runs per algorithm (seven baselines, 30,000+ manipulation cycles) demonstrated +24.59% to +34.75% improvements (p < 0.001, d = 0.89–1.52), achieving hypervolume 0.076 ± 0.015 (19.7% coefficient of variation—lowest among all methods) and 95% optimal performance within 180 episodes—five times faster than evolutionary baselines. Four independent verification methods (WFG, PyMOO, Monte Carlo, HSO) confirmed measurement reliability (<0.26% variance). The framework maintains edge computing compatibility (<2 GB RAM, <50 ms latency) and seamless integration with Manufacturing Execution Systems and digital twins. This research establishes new benchmarks for adaptive robotic control in sustainable Industry 4.0/5.0 manufacturing. Full article

(This article belongs to the Section Advanced Manufacturing)

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16 pages, 11763 KB

Open AccessArticle

Prescribed Performance Trajectory Tracking Control for Electro-Hydraulic Servo Pump-Controlled Systems with Input and State Delays

by Gengting Qiu, Yujie Hao, Gexin Chen, Guishan Yan and Yao Chen

Machines 2025, 13(12), 1147; https://doi.org/10.3390/machines13121147 - 17 Dec 2025

Viewed by 420

Abstract

Electro-hydraulic servo pump-controlled systems have advantages such as energy saving and high integration and are widely applied in aerospace, engineering machinery, and other fields. However, the input and state delays introduced by drive circuit, control period, and oil leakage result in lower dynamic [...] Read more.

Electro-hydraulic servo pump-controlled systems have advantages such as energy saving and high integration and are widely applied in aerospace, engineering machinery, and other fields. However, the input and state delays introduced by drive circuit, control period, and oil leakage result in lower dynamic response speed compared to traditional valve control systems, which restricts the promotion of the system. In this paper, a prescribed performance trajectory tracking control method is proposed to improve the transient and steady-state performance of the system. A performance function is designed to constrain the range of trajectory tracking errors. The constrained space is mapped to an unconstrained space via a homeomorphic transformation, and the control laws are designed by integrating it with the backstepping method. In the final step of the backstepping design, the input and state delays are processed using Lyapunov–Krasovskii functionals. The simulation and experimental results show that under the condition of fixed input delay and state delay, the trajectory tracking errors converge within the preset range, and all states of the system are uniformly bounded. The results demonstrate the effectiveness of the proposed method in this paper. Full article

(This article belongs to the Special Issue Advances in the Control of Electro-Hydraulic Servo Systems)

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22 pages, 1721 KB

Open AccessArticle

ADP-Based Event-Triggered Optimal Control of Grid-Connected Voltage Source Inverters

by Zemeng Mi, Jiawei Wang, Hanguang Su, Dongyuan Zhang, Wencheng Yan and Yuanyuan Bai

Machines 2025, 13(12), 1146; https://doi.org/10.3390/machines13121146 - 17 Dec 2025

Viewed by 249

Abstract

In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to [...] Read more.

In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to ensure mutual cooperation between active power and reactive power. To achieve optimal performance, the power components are driven to track their desired references while minimizing the individual performance index function. Accurate tracking of active and reactive powers not only stabilizes the grid but also guarantees compliant renewable integration. An adaptive dynamic programming (ADP) approach is adopted, where the critic neural network (NN) approximates the value function and provides optimal control policies. Moreover, an event-triggered mechanism with a dead-zone operation is incorporated to reduce redundant updates, thereby saving computational and communication resources. The stability of the closed-loop system and a strictly positive minimum inter-event interval are guaranteed. Simulation results verify that the proposed method achieves accurate power tracking, improved dynamic performance, and efficient implementation. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)

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23 pages, 14105 KB

Open AccessArticle

A Comprehensive Study on Meshing Performances Compensation for Face-Hobbed Hypoid Gears: Coupled Analysis of Spatial Installation Errors and Manufactured Tooth Flank Characteristics

by Chengcheng Liang, Yihao Zhang, Longhua Liu, Chaosheng Song and Siyuan Liu

Machines 2025, 13(12), 1145; https://doi.org/10.3390/machines13121145 - 16 Dec 2025

Viewed by 239

Abstract

In manufacturing face-hobbing hypoid gears, the coupling between tooth flank errors and installation errors has a significant impact on dynamic meshing behavior, yet quantitative models for their synergistic effects remain scarce. This study elucidates the combined effects of three-dimensional (3D) installation errors and [...] Read more.

In manufacturing face-hobbing hypoid gears, the coupling between tooth flank errors and installation errors has a significant impact on dynamic meshing behavior, yet quantitative models for their synergistic effects remain scarce. This study elucidates the combined effects of three-dimensional (3D) installation errors and real tooth flank deviations on transmission error. First, a geometric model of the real tooth flank, incorporating midpoint pitch deviation, is established based on theoretical flank equations and coordinate transformations. Then, a finite element model integrating 3D installation errors is developed. Finally, the combined effects of installation errors and real tooth flanks on meshing performance are analyzed. Results reveal a dual role of installation errors: when compensating for midpoint pitch deviation, the peak-to-peak transmission error (PPTE) decreases by 3.78%, while the contact pattern length and area increase. Under certain conditions, despite a 26.28% increase in PPTE, the contact pattern length grows by 2.29%, accompanied by a notable reduction in maximum contact stress on the tooth flanks. Full article

(This article belongs to the Section Advanced Manufacturing)

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19 pages, 12677 KB

Open AccessArticle

Approaches and Issues Regarding Center of Mass Behavior in an Exoskeleton Design for a Child’s Body

by Cristian Copilusi, Sorin Dumitru, Ionut Geonea, Slavi Lyubomirov and Cristian Mic

Machines 2025, 13(12), 1144; https://doi.org/10.3390/machines13121144 - 16 Dec 2025

Viewed by 260

Abstract

This research aims to identify a suitable design solution that models the behavior of a human’s center of mass. This solution can be implemented in an exoskeleton structure that is especially designed for children who require walking assistance and rehabilitation. The primary problem [...] Read more.

This research aims to identify a suitable design solution that models the behavior of a human’s center of mass. This solution can be implemented in an exoskeleton structure that is especially designed for children who require walking assistance and rehabilitation. The primary problem posed by exoskeleton designs is representing the effect of ground–foot contact on exoskeleton behavior under kinematic and dynamic conditions. To mitigate this, our main research objective was to develop a mechanical system that demonstrates the human center of mass (CoM) behavior on an exoskeleton designed for children with Duchenne Muscular Dystrophy. The research focuses on modeling human CoM behavior under kinematic circumstances and transferring this into a mechanical system conceptual design. The obtained results validate the proposed mechanical system through a comparative analysis between numerical processing, virtual prototyping, and experimental specific methods and procedures. Full article

(This article belongs to the Special Issue Advanced Rehabilitation Exoskeleton Robots)

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15 pages, 3456 KB

Open AccessArticle

Reinforcement Learning Optimization of Coaxial Magnetic Gear Geometry with Finite Element Analysis

by Georgi Ivanov, Valentin Mateev, Iliana Marinova, Wolfgang Gruber, Edmund Marth and Stefan Mallinger

Machines 2025, 13(12), 1143; https://doi.org/10.3390/machines13121143 - 16 Dec 2025

Viewed by 309

Abstract

This manuscript presents a reinforcement learning (RL) agent method to optimize the geometry of a coaxial magnetic gear using a 2D finite element magnetic (FEM) simulation. The proposed optimization algorithm aims to improve the maximum torque within given boundaries of the magnetic gear [...] Read more.

This manuscript presents a reinforcement learning (RL) agent method to optimize the geometry of a coaxial magnetic gear using a 2D finite element magnetic (FEM) simulation. The proposed optimization algorithm aims to improve the maximum torque within given boundaries of the magnetic gear geometry by adjusting parameterized radii. A linear actor–critic gradient algorithm is implemented, where the actor learns a policy to adjust and discover the values of five geometric parameters of the magnetic gear model, and the critic evaluates the performance of the resulting designs. The RL agent interacts with an environment integrated with a 2D FEM simulation, which provides feedback by calculating the total torque of the new geometry discovered. The optimization algorithm uses a greedy exploration method that uses the total torque as a reward system, which the RL agent aims to maximize. The results obtained for the magnetic gear optimization demonstrate the effectiveness of the proposed RL algorithm, which can be applied to automate multiparameter geometric optimization using artificial intelligence systems. Full article

(This article belongs to the Special Issue Neural Networks Applied in Manufacturing and Design)

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21 pages, 125689 KB

Open AccessArticle

Design and Validation of a Soft Pneumatic Submodule for Adaptive Humanoid Foot Compliance

by Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse and Gentiane Venture

Machines 2025, 13(12), 1142; https://doi.org/10.3390/machines13121142 - 16 Dec 2025

Viewed by 440

Abstract

Achieving stable contact on uneven terrain remains a key challenge in humanoid robotics, as most feet rely on rigid or passively compliant structures with fixed stiffness. This work presents the design, fabrication, and analytical modeling of a compact soft pneumatic submodule capable of [...] Read more.

Achieving stable contact on uneven terrain remains a key challenge in humanoid robotics, as most feet rely on rigid or passively compliant structures with fixed stiffness. This work presents the design, fabrication, and analytical modeling of a compact soft pneumatic submodule capable of tunable longitudinal stiffness, developed as a proof-of-concept unit for adaptive humanoid feet. The submodule features a tri-layer architecture with two antagonistic pneumatic chambers separated by an inextensible layer and reinforced by rigid inserts. A single-step wax-core casting process integrates all materials into a monolithic soft–rigid structure, ensuring precise geometry and repeatable performance. An analytical model relating internal pressure to equivalent stiffness was derived and experimentally validated, showing a linear stiffness–pressure relation with mean error below 10% across 0–30 kPa. Static and dynamic tests confirmed tunable stiffness between 0.18 and 0.43 N·m/rad, a rapid symmetric response (2.9–3.4 ms), and stable stiffness under cyclic loading at gait-relevant frequencies. These results demonstrate the submodule’s suitability as a scalable building block for distributed, real-time stiffness modulation in next-generation humanoid feet. Full article

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

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Graphical abstract

51 pages, 2572 KB

Open AccessEditor’s ChoiceReview

Digital Twin Approaches for Gear NVH Optimization: A Literature Review of Modeling, Data Integration, and Validation Gaps

by Krisztian Horvath and Ambrus Zelei

Machines 2025, 13(12), 1141; https://doi.org/10.3390/machines13121141 - 15 Dec 2025

Viewed by 495

Abstract

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating [...] Read more.

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

(This article belongs to the Special Issue Digital Twin-Driven Machine Performance and Reliability: Replication, Prediction and Front Running Simulation)

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36 pages, 3105 KB

Open AccessReview

Reinforcement Learning for Industrial Automation: A Comprehensive Review of Adaptive Control and Decision-Making in Smart Factories

by Yasser M. Alginahi, Omar Sabri and Wael Said

Machines 2025, 13(12), 1140; https://doi.org/10.3390/machines13121140 - 15 Dec 2025

Viewed by 1883

Abstract

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, [...] Read more.

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article

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

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17 pages, 3780 KB

Open AccessArticle

A Weighted Control Strategy Based on Current Imbalance Degree for Vienna Rectifiers Under Unbalanced Grid

by Haigang Wang, Zongwei Liu and Muqin Tian

Machines 2025, 13(12), 1139; https://doi.org/10.3390/machines13121139 - 12 Dec 2025

Viewed by 326

Abstract

Under unbalanced grid conditions, the three-phase Vienna rectifier exhibits significant voltage fluctuations in dc-link and asymmetric input currents. Traditional control methods cannot simultaneously suppress the voltage ripples in dc-link and balance the input currents. Therefore, a weighted control strategy based on the degree [...] Read more.

Under unbalanced grid conditions, the three-phase Vienna rectifier exhibits significant voltage fluctuations in dc-link and asymmetric input currents. Traditional control methods cannot simultaneously suppress the voltage ripples in dc-link and balance the input currents. Therefore, a weighted control strategy based on the degree of current imbalance is proposed in this paper. The strategy is implemented within a dual closed-loop architecture, featuring a finite-set model predictive control (FS-MPC) method in the current loop and a sliding mode control (SMC) method in the voltage loop. In the current loop, the two control objectives of voltage in dc-link and input current are weighted, and the weighting factor is dynamically adjusted based on the degree of current imbalance. This strategy can simultaneously achieve control for input current symmetry and dc-link voltage balance under unbalanced grid conditions. Finally, a 2 kW Vienna rectifier experimental platform was independently constructed. Simulation and experimental results indicate that under unbalanced grid conditions, the proposed control strategy achieves approximately 10% lower total harmonic distortion (THD) and maintains DC-link voltage fluctuation within 5 V, compared to traditional control methods. Full article

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

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20 pages, 7938 KB

Open AccessArticle

Combination of Finite Element Spindle Model with Drive-Based Cutting Force Estimation for Assessing Spindle Bearing Load of Machine Tools

by Chris Schöberlein, Daniel Klíč, Michal Holub, Holger Schlegel and Martin Dix

Machines 2025, 13(12), 1138; https://doi.org/10.3390/machines13121138 - 12 Dec 2025

Viewed by 494

Abstract

Monitoring spindle bearing load is essential for ensuring machining accuracy, reliability, and predictive maintenance in machine tools. This paper presents an approach that combines drive-based cutting force estimation with a finite element method (FEM) spindle model. The drive-based method reconstructs process forces from [...] Read more.

Monitoring spindle bearing load is essential for ensuring machining accuracy, reliability, and predictive maintenance in machine tools. This paper presents an approach that combines drive-based cutting force estimation with a finite element method (FEM) spindle model. The drive-based method reconstructs process forces from the motor torque signal of the feed axes by modeling and compensating motion-related torque components, including static friction, acceleration, gravitation, standstill, and periodic disturbances. The inverse mechanical and control transfer behavior is also considered. Input signals include the actual motor torque, axis position, and position setpoint, recorded by the control system’s internal measurement function at the interpolator clock rate. Cutting forces are then calculated in MATLAB/Simulink and used as inputs for the FEM spindle model. Rolling elements are replaced by bushing joints with stiffness derived from datasheets and adjusted through experiments. Force estimation was validated on a DMC 850 V machining center using a standardized test workpiece, with results compared against a dynamometer. The spindle model was validated separately on a MCV 754 Quick machine under static loading. The combined approach produced consistent results and identified the front bearing as the most critically loaded. The method enables practical spindle bearing load estimation without external sensors, lowering system complexity and cost. Full article

(This article belongs to the Special Issue Machines and Applications—New Results from a Worldwide Perspective)

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23 pages, 6327 KB

Open AccessArticle

The Product Variety Costing Method (PVCM): A Data-Driven Approach to Resource Allocation and Cost Evaluation

by Morten Nørgaard, Jakob Meinertz Grønvald, Carsten Keinicke Fjord Christensen and Niels Henrik Mortensen

Machines 2025, 13(12), 1137; https://doi.org/10.3390/machines13121137 - 12 Dec 2025

Viewed by 468

Abstract

This study introduces the Product Variety Costing Method (PVCM), a data-driven framework that addresses the limitations of existing costing approaches, which fail to accurately present the cost of product and part variety, thereby constraining cost-informed decision-making in modular product development. Traditional cost allocation [...] Read more.

This study introduces the Product Variety Costing Method (PVCM), a data-driven framework that addresses the limitations of existing costing approaches, which fail to accurately present the cost of product and part variety, thereby constraining cost-informed decision-making in modular product development. Traditional cost allocation methods often lack one or more of the following: a full life-cycle perspective, a lower level of granularity according to the product structure, or a combined integration of qualitative and quantitative data. The PVCM bridges these gaps by combining Time-Driven Activity-Based Costing (TDABC) with hierarchical product structures and empirical enterprise data, enabling the quantification of variety-induced resource consumption across components, subsystems, and complete products. An industrial application demonstrates that the PVCM enhances cost accuracy and transparency by linking resource use directly to specific product abstraction levels, thereby highlighting the true cost impact of product variety. In this case, results revealed deviations of up to 60% in the adjusted contribution margin ratio relative to traditional overhead-based methods, clearly indicating the influence of product variety on cost assessments. The method supports design and managerial decision-making by allowing evaluation of modularization based on detailed cost insights. While the study’s scope is limited to selected life-cycle phases and a single company case, the findings highlight the method’s future potential as a generalizable tool for evaluating economic benefits of modularization. Ultimately, the PVCM contributes to a more transparent and analytically grounded understanding of the cost of variety in complex product portfolios. Full article

(This article belongs to the Special Issue Assessing New Trends in Sustainable and Smart Manufacturing)

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22 pages, 1402 KB

Open AccessArticle

Spacecraft Health Status Monitoring Method Based on Multidimensional Data Fusion

by Hanyu Liang, Chengrui Liu, Wenjing Liu, Wenbo Li and Yan Zhang

Machines 2025, 13(12), 1136; https://doi.org/10.3390/machines13121136 - 12 Dec 2025

Viewed by 420

Abstract

To address the difficulty of detecting on-orbit faults of spacecraft under complex operating conditions in time, rational monitoring and assessment of spacecraft health status are essential for ensuring its safe, stable, and reliable operation. Considering the complexity, coupling, and multidimensionality of telemetry data, [...] Read more.

To address the difficulty of detecting on-orbit faults of spacecraft under complex operating conditions in time, rational monitoring and assessment of spacecraft health status are essential for ensuring its safe, stable, and reliable operation. Considering the complexity, coupling, and multidimensionality of telemetry data, this paper proposes a method for monitoring the health status of spacecraft based on multidimensional data fusion for a key electromechanical component of a spacecraft control system. The method first extracts the explicit and implicit features of the multidimensional coupled telemetry parameters via physical feature formulas and a stacked autoencoder. Then, the extracted features are fused and filtered to obtain the health factor—a performance degradation trend described the evolution law of key component health status over runtime. Moreover, the different degradation stages are identified via an unsupervised clustering algorithm. Finally, a Bidirectional Long Short-Term Memory (Bi-LSTM) is used to construct a health status prediction model in stages. By taking Control Moment Gyroscopes (CMGs) as experimental verification subjects, the proposed method demonstrates significantly superior performance compared to other methods across prediction accuracy metrics including MSE, RMSE, and

R^{2}

. This study provides robust technical support for health status monitoring of key spacecraft electromechanical components under specific fault modes. Full article

(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)

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16 pages, 565 KB

Open AccessArticle

Analytical Regression and Geometric Validation of the Blade Arc Segment

\overset{⌢}{BC}

in a Michell–Banki Turbine

by Mauricio A. Díaz Raby, Gonzalo A. Moya Navarrete and Jacobo Hernandez-Montelongo

Machines 2025, 13(12), 1135; https://doi.org/10.3390/machines13121135 - 12 Dec 2025

Viewed by 458

Abstract

This study introduces a systematic methodology for modelling the radius of curvature of the arc-shaped section

\overset{⌢}{BC}

in a Michell–Banki cross-flow turbine blade. The method combines geometric modeling in polar coordinates with nonlinear regression, using both two- and three-parameter formulations estimated through [...] Read more.

This study introduces a systematic methodology for modelling the radius of curvature of the arc-shaped section

\overset{⌢}{BC}

in a Michell–Banki cross-flow turbine blade. The method combines geometric modeling in polar coordinates with nonlinear regression, using both two- and three-parameter formulations estimated through the Ordinary Least Squares (OLS) method. Model performance is assessed through two complementary criteria: the coefficient of determination (

R^{2}

) and the computed arc length, ensuring that statistical accuracy aligns with geometric fidelity. The methodology was validated on digital measurements obtained from CATIA, using datasets with

N = 187

and a reduced subset of

N = 48

points. Results demonstrate that even with fewer data points, the regression model maintains high predictive accuracy and geometric consistency. The best-performing three-parameter model achieved

R^{2} = 0.958

, with a five-point Gauss–Legendre quadrature yielding an arc length of approximately

145 mm

, representing 98.8% agreement with the reference value of

146.78 mm

. By representing the arc as a single smooth exponential function rather than a piecewise mapping, the approach simplifies analysis and enhances reproducibility. Coupling regression precision with arc-length verification provides a robust and reproducible basis for curvature modeling. This methodology supports turbine blade design, manufacturing, and quality control by ensuring that the blade geometry is validated with high statistical confidence and physical accuracy. Future research will focus on deriving analytical arc-length integrals and integrating the procedure into automated design and inspection workflows. Full article

(This article belongs to the Special Issue Non-Conventional Machining Technologies for Advanced Materials)

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30 pages, 4814 KB

Open AccessArticle

Cross-Embodiment Kinematic Behavioral Cloning (X-EKBC): An Energy-Based Framework for Human–Robot Imitation Learning with the Embodiment Gap

by Yoshiki Tsunekawa, Masaki Tanaka and Kosuke Sekiyama

Machines 2025, 13(12), 1134; https://doi.org/10.3390/machines13121134 - 10 Dec 2025

Viewed by 726

Abstract

In imitation learning with the embodiment gap, directly transferring human motions to robots is challenging due to differences in body structures. Therefore, it is necessary to reconstruct human motions in accordance with each robot’s embodiment. Our previous work focused on the right arm [...] Read more.

In imitation learning with the embodiment gap, directly transferring human motions to robots is challenging due to differences in body structures. Therefore, it is necessary to reconstruct human motions in accordance with each robot’s embodiment. Our previous work focused on the right arm of a humanoid robot, which limited the generality of the approach. To address this, we propose Cross-Embodiment Kinematic Behavioral Cloning (X-EKBC), an imitation learning framework that enables movement-level imitation on a one-to-one basis between humans and multiple robots with embodiment gaps. We introduce a joint matrix that represents the structural correspondence between the human and robot bodies, and by solving kinematics based on this matrix, the system can efficiently reconstruct motions adapted to each robot’s embodiment. Furthermore, by employing Implicit Behavioral Cloning (IBC), the proposed method achieves both imitation learning of the reconstructed motions and quantitative evaluation of embodiment gaps using energy-based modeling. As a result, motion reconstruction through the joint matrix became feasible, enabling both imitation learning and quantitative embodiment evaluation based on reconstructed behaviors. Future work will aim to extend this framework toward motion-level imitation that captures higher-level behavioral outcomes. Full article

(This article belongs to the Special Issue Robots with Intelligence: Developments and Applications)

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25 pages, 2873 KB

Open AccessArticle

Dynamic Attention Analysis of Body Parts in Transformer-Based Human–Robot Imitation Learning with the Embodiment Gap

by Yoshiki Tsunekawa and Kosuke Sekiyama

Machines 2025, 13(12), 1133; https://doi.org/10.3390/machines13121133 - 10 Dec 2025

Viewed by 790

Abstract

In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic [...] Read more.

In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic attention to body parts in imitation learning between humans and robots based on a Transformer model. To adapt human imitation movements to a robot, we solved forward and inverse kinematics using the Levenberg–Marquardt method and performed feature extraction using the k-means method to make the data suitable for Transformer input. The imitation learning process is carried out using the Transformer. UMAP is employed to visualize the attention layer within the Transformer. As a result, this system enabled imitation of movements while focusing on multiple body parts between humans and robots with an embodiment gap, revealing the transitions of body parts receiving attention and their relationships in the robot’s acquired imitation movements. Full article

(This article belongs to the Special Issue Robots with Intelligence: Developments and Applications)

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19 pages, 2938 KB

Open AccessArticle

Adaptive Funnel Control of Hydraulic Excavator Based on Neural Network

by Yuhe Li and Xiaowen Qi

Machines 2025, 13(12), 1132; https://doi.org/10.3390/machines13121132 - 9 Dec 2025

Viewed by 408

Abstract

To address the challenge of controlling the hydraulic excavator’s precise motion, a nonlinear backstepping control algorithm is designed, combining a funnel function and a neural network (NN), which effectively compensates for the influence of unmodeled dynamics and external disturbances on the hydraulic excavator’s [...] Read more.

To address the challenge of controlling the hydraulic excavator’s precise motion, a nonlinear backstepping control algorithm is designed, combining a funnel function and a neural network (NN), which effectively compensates for the influence of unmodeled dynamics and external disturbances on the hydraulic excavator’s control system. Specifically, an improved funnel function is introduced to characterize both the steady-state and transient performance of the system simultaneously, thereby limiting the joint tracking error within predetermined performance constraints and enhancing the trajectory tracking accuracy. Two RBFNN estimators are employed to address the uncertain coupled mechanical dynamics and nonlinear hydraulic dynamics, respectively. The weight updating law is generated based on the gradient descent method, which can prevent high-gain feedback and enhance the system’s robustness. Finally, the stability of the closed-loop system is rigorously proven using the Lyapunov function analysis method. To verify the effectiveness of the proposed algorithm, simulations and experimental research are conducted under various external disturbances, using the excavator’s flat working condition as a case study. The results demonstrate that the controller maintains good control performance and robustness even in the presence of uncertainties and external disturbances within the system. Full article

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

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20 pages, 20968 KB

Open AccessArticle

Influence of the Tool Geometry on the Surface Properties in Ultrasonic Vibration Superimposed Machining of Bronze

by Hendrik Liborius, Jonas Maximilian Werner, Andreas Nestler, Welf-Guntram Drossel and Andreas Schubert

Machines 2025, 13(12), 1131; https://doi.org/10.3390/machines13121131 - 9 Dec 2025

Viewed by 260

Abstract

Ultrasonic vibration superimposed turning represents a highly efficient method for surface microstructuring, which enables a combination with finish machining. However, there are almost no industrial applications of this process due to the special kinematics. Furthermore, the effects of the varying cutting conditions combined [...] Read more.

Ultrasonic vibration superimposed turning represents a highly efficient method for surface microstructuring, which enables a combination with finish machining. However, there are almost no industrial applications of this process due to the special kinematics. Furthermore, the effects of the varying cutting conditions combined with the tool geometry on the resulting surfaces and process stability are not yet fully understood. In experimental investigations, specimens consisting of bronze (CuSn7Pb15-C) are machined by ultrasonic vibration superimposed turning. The influence of the geometry of the MCD-tipped indexable inserts on the surface microstructure is analyzed. Indexable inserts with different rake angles (0°, −10°, and −20°) and artificially generated flank wear lands (widths 50 µm and 100 µm) are used. Moreover, the influences of the cutting speed (120 m/min, 480 m/min) and the feed (0.05 mm, 0.1 mm) are analyzed. While machining, the strain of the sonotrode is detected by an integrated fiber Bragg grating. Subsequent to machining, geometrical surface properties are determined by SEM and 3D surface analysis using focus variation. Furthermore, kinematic simulations are realized, enabling the comparison with the generated surfaces. Generally, there is a high concordance between the simulated and the generated surfaces. However, in particular when the tool flank face gets in contact with the specimen, deviations are visible, especially the formation of burr. Summarized, the research improves the understanding of the mechanisms in ultrasonic vibration superimposed turning and the formation of the surface microstructures. Full article

(This article belongs to the Special Issue Recent Advances in Surface Integrity with Machining and Milling)

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27 pages, 16096 KB

Open AccessArticle

Effect of Dynamic Tilting Speed on the Flow Field of Distributed Multi-Propeller Tilt-Wing Aircraft During Transition Flight

by Jiahao Zhu, Yongjie Shi, Taihang Ma, Guohua Xu and Zhiyuan Hu

Machines 2025, 13(12), 1130; https://doi.org/10.3390/machines13121130 - 9 Dec 2025

Viewed by 452

Abstract

Advances in distributed electric propulsion and urban air mobility technologies have spurred a surge of research on electric Vertical Take-Off and Landing (eVTOL) aircraft. Distributed Multi-Propeller Tilting-Wing (DMT) eVTOL configurations offer higher forward flight speed and efficiency. However, aerodynamic challenges during the transition [...] Read more.

Advances in distributed electric propulsion and urban air mobility technologies have spurred a surge of research on electric Vertical Take-Off and Landing (eVTOL) aircraft. Distributed Multi-Propeller Tilting-Wing (DMT) eVTOL configurations offer higher forward flight speed and efficiency. However, aerodynamic challenges during the transition phase have limited their practical application. This study develops a high-fidelity body-fitted mesh CFD numerical simulation method for flow field calculations of DMT aircraft. Using the reverse overset assembly method and CPU-GPU collaborative acceleration technology, the accuracy and efficiency of flow field simulations are enhanced. Using the established method, the influence of dynamic tilting speeds on the flow field of this configuration is investigated. This paper presents the variations in the aerodynamic characteristics of the tandem propellers and tilt-wings throughout the full tilt process under different tilting speeds, analyzes the mechanisms behind reductions in the propeller’s aerodynamic performance and tilt-wing lift overshoot, and conducts a detailed comparison of flow field distribution characteristics under fixed-angle tilting, slow tilting, and fast tilting conditions. The study explores the influence mechanism of tilting speed on blade tip vortex-lifting surface interactions and interference between tandem propellers and tilt-wings, providing valuable conclusions for the aerodynamic design and safe transition implementation of DMT aircraft. Full article

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

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25 pages, 3616 KB

Open AccessArticle

A Deep Learning-Driven Semantic Mapping Strategy for Robotic Inspection of Desalination Facilities

by Albandari Alotaibi, Reem Alrashidi, Hanan Alatawi, Lamaa Duwayriat, Aseel Binnouh, Tareq Alhmiedat and Ahmad Al-Qerem

Machines 2025, 13(12), 1129; https://doi.org/10.3390/machines13121129 - 8 Dec 2025

Viewed by 494

Abstract

The area of robot autonomous navigation has become essential for reducing labor-intensive tasks. These robots’ current navigation systems are based on sensed geometrical structures of the environment, with the engagement of an array of sensor units such as laser scanners, range-finders, and light [...] Read more.

The area of robot autonomous navigation has become essential for reducing labor-intensive tasks. These robots’ current navigation systems are based on sensed geometrical structures of the environment, with the engagement of an array of sensor units such as laser scanners, range-finders, and light detection and ranging (LiDAR) in order to obtain the environment layout. Scene understanding is an important task in the development of robots that need to act autonomously. Hence, this paper presents an efficient semantic mapping system that integrates LiDAR, RGB-D, and odometry data to generate precise and information-rich maps. The proposed system enables the automatic detection and labeling of critical infrastructure components, while preserving high spatial accuracy. As a case study, the system was applied to a desalination plant, where it interactively labeled key entities by integrating Simultaneous Localization and Mapping (SLAM) with vision-based techniques in order to determine the location of installed pipes. The developed system was validated using an efficient development environment known as Robot Operating System (ROS) and a two-wheel-drive robot platform. Several simulations and real-world experiments were conducted to validate the efficiency of the developed semantic mapping system. The obtained results are promising, as the developed semantic map generation system achieves an average object detection accuracy of 84.97% and an average localization error of 1.79 m. Full article

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

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19 pages, 1375 KB

Open AccessReview

Recent Developments in Electroadhesion Grippers for Automated Fruit Grasping

by Turac I. Ozcelik, Enrico Masi, Seyyed Masoud Kargar, Chiara Scagliarini, Adyan Fatima, Rocco Vertechy and Giovanni Berselli

Machines 2025, 13(12), 1128; https://doi.org/10.3390/machines13121128 - 8 Dec 2025

Viewed by 870

Abstract

As global food demand rises and agricultural labor shortages intensify, robotic automation has become essential for sustainable fruit grasping. Among emerging technologies, ElectroAdhesion (EA) grippers offer a promising alternative to traditional mechanical end-effectors, enabling gentle, low-pressure handling through electrostatically induced adhesion. This paper [...] Read more.

As global food demand rises and agricultural labor shortages intensify, robotic automation has become essential for sustainable fruit grasping. Among emerging technologies, ElectroAdhesion (EA) grippers offer a promising alternative to traditional mechanical end-effectors, enabling gentle, low-pressure handling through electrostatically induced adhesion. This paper presents a methodical review of EA grippers applied to fruit grasping, focusing on their advantages, limitations, and key design considerations. A targeted literature search identified ten EA-based and hybrid EA gripping systems tested on fruit manipulation, though none has yet been tested in real-world environments such as fields or greenhouses. Despite a significant variability in experimental setups, materials, and grasp types, qualitative insights are drawn from our analysis demonstrating the potentialities of EA technologies. The EA grippers found in the targeted review are effective on diverse fruits, shapes, and surface textures; they can hold load capacities ranging from 10 g (~0.1 N) to 600 g (~6 N) and provide minimal compressive stress at high electrostatic shear forces. Along with custom EA grippers designed accordingly to specific use cases, field and greenhouse testing will be crucial for advancing the technology readiness level of EA grippers and unlocking their full potential in automated crop harvesting. Full article

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

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30 pages, 470 KB

Open AccessArticle

Clustered Reverse Resumable A* Algorithm for Warehouse Robot Pathfinding

by Gábor Csányi and László Z. Varga

Machines 2025, 13(12), 1127; https://doi.org/10.3390/machines13121127 - 8 Dec 2025

Viewed by 536

Abstract

Robots are widely used to carry goods in automated warehouses. Planning collision-free paths for multiple robots which are continuously given new goals is called Lifelong Multi-Agent Pathfinding. In a lifelong environment, conflicts may emerge among the robots, and continuous replanning is needed. We [...] Read more.

Robots are widely used to carry goods in automated warehouses. Planning collision-free paths for multiple robots which are continuously given new goals is called Lifelong Multi-Agent Pathfinding. In a lifelong environment, conflicts may emerge among the robots, and continuous replanning is needed. We propose, develop, implement, and evaluate the novel approach called the Clustered Reverse Resumable A* (CRRA*) algorithm to enhance the continuous computation of the shortest path from the changing position of a robot to its goal. The Priority Inheritance with Backtracking (PIBT) algorithm is the currently known most efficient algorithm to handle the pathfinding of thousands of robots in a warehouse. The PIBT algorithm requires that in each step each robot evaluates the distances from its surrounding positions to its goal; therefore, we integrate the CRRA* algorithm with the PIBT algorithm to evaluate CRRA*. The evaluation results show that the CRRA* leads to a significant reduction in computation time, especially in larger warehouses where the obstacles form well-separated spaces. At the same time, the degradation in solution quality is minimal. The CRRA* algorithm is more efficient in larger warehouses than the plain Reverse Resumable A* (RRA*) algorithm. The faster computation of slightly suboptimal paths can be useful in many practical applications, especially in situations where real-time planning is more important than finding the optimal paths. CRRA* can also be used as a heuristic in any multi-agent pathfinding solution to obtain a faster, nearly accurate heuristic. Full article

(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAVs, 2nd Edition)

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23 pages, 21889 KB

Open AccessArticle

Multi-Stage Domain-Adapted 6D Pose Estimation of Warehouse Load Carriers: A Deep Convolutional Neural Network Approach

by Hisham ElMoaqet, Mohammad Rashed and Mohamed Bakr

Machines 2025, 13(12), 1126; https://doi.org/10.3390/machines13121126 - 8 Dec 2025

Viewed by 512

Abstract

Intelligent autonomous guided vehicles (AGVs) are of huge importance in facilitating the automation of load handling in the era of Industry 4.0. AGVs heavily rely on environmental perception, such as the 6D poses of objects, in order to execute complex tasks efficiently. Therefore, [...] Read more.

Intelligent autonomous guided vehicles (AGVs) are of huge importance in facilitating the automation of load handling in the era of Industry 4.0. AGVs heavily rely on environmental perception, such as the 6D poses of objects, in order to execute complex tasks efficiently. Therefore, estimating the 6D poses of objects in warehouses is crucial for proper load handling in modern intra-logistics warehouse environments. This study presents a deep convolutional neural network approach for estimating the pose of warehouse load carriers. Recognizing the paucity of labeled real 6D pose estimation data, the proposed approach uses only synthetic RGB warehouse data to train the network. Domain adaption was applied using a Contrastive Unpaired Image-to-Image Translation (CUT) Network to generate domain-adapted training data that can bridge the domain gap between synthetic and real environments and help the model generalize better over realistic scenes. In order to increase the detection range, a multi-stage refinement detection pipeline is developed using consistent multi-view multi-object 6D pose estimation (CosyPose) networks. The proposed framework was tested with different training scenarios, and its performance was comprehensively analyzed and compared with a state-of-the-art non-adapted single-stage pose estimation approach, showing an improvement of up to 80% on the ADD-S AUC metric. Using a mix of adapted and non-adapted synthetic data along with splitting the state space into multiple refiners, the proposed approach achieved an ADD-S AUC performance greater than 0.81 over a wide detection range, from one and up to five meters, while still being trained on a relatively small synthetic dataset for a limited number of epochs. Full article

(This article belongs to the Special Issue Industry 4.0: Intelligent Robots in Smart Manufacturing)

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26 pages, 4415 KB

Open AccessArticle

A Module Configuration Design Approach for Complex Equipment of Port Shipping Based on Heterogeneous Customer Requirements and Product Operational Data

by Xiaozhen Lian, Xinyi Luo and Deying Su

Machines 2025, 13(12), 1125; https://doi.org/10.3390/machines13121125 - 7 Dec 2025

Viewed by 328

Abstract

Modularization fails to adequately meet the diverse customer requirements and the product operational data for complex equipment of port shipping (CEPS). To address this challenge, we propose a module configuration design approach (MCDA) that incorporates module parameter planning (MPP) and service module customization [...] Read more.

Modularization fails to adequately meet the diverse customer requirements and the product operational data for complex equipment of port shipping (CEPS). To address this challenge, we propose a module configuration design approach (MCDA) that incorporates module parameter planning (MPP) and service module customization (SMC). Initially, the design ranges and weights of functional requirements are established using fuzzy information derived from customer requirements, facilitated by fuzzy quality function deployment. Subsequently, a multi-objective model of MPP is developed, incorporating the cost utility, information content, and delivery time of module and product based on a probabilistic assessment of module instances from operational data. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to derive the solution set for MPP. The personalized configuration of the Pareto solution set for SMC is derived based on each objective function pair. Finally, we illustrate the effectiveness of the proposed approach through a case study involving a wheel loader and method comparison. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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25 pages, 1472 KB

Open AccessArticle

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

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

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

Viewed by 473

Abstract

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

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

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

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