Previous Issue
Volume 14, January
 
 

Machines, Volume 14, Issue 2 (February 2026) – 91 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
20 pages, 8698 KB  
Article
Design and Experimental Research of a Track Vibration Energy Harvester Based on a Wideband Magnetic Levitation Structure
by Zhen Li, Lijun Rong, Aoxiang Lan, Mingze Tang and Yougang Sun
Machines 2026, 14(2), 225; https://doi.org/10.3390/machines14020225 (registering DOI) - 13 Feb 2026
Abstract
With the rapid development of rail transit, how to power low-energy monitoring systems for the vast and complex infrastructure in the rail transit system is becoming an urgent problem. To achieve green and intelligent rail transit infrastructure while ensuring long-term operational safety, harvesting [...] Read more.
With the rapid development of rail transit, how to power low-energy monitoring systems for the vast and complex infrastructure in the rail transit system is becoming an urgent problem. To achieve green and intelligent rail transit infrastructure while ensuring long-term operational safety, harvesting vibration energy from tracks to power wireless sensor networks has become a research hotspot. This paper designs a track vibration energy harvester based on a broadband magnetic levitation structure. First, a dynamic model of the harvester is established, and the corresponding dynamic equations, energy–velocity relationship, and system transfer function are derived. Also, by simulating electromagnetic interactions, the distribution pattern of magnetic density inside the energy harvester is revealed. Next, the response characteristics of the energy harvester are analyzed under single-frequency and multi-frequency excitation conditions. Using the Runge-Kutta algorithm for computational analysis, the optimal structural parameters of the energy harvester are designed. Finally, a magnetic levitation energy harvester prototype is constructed. Experimental validation confirmed the feasibility of the energy harvester and its adaptability to low-frequency vibration environments. Full article
(This article belongs to the Section Electromechanical Energy Conversion Systems)
18 pages, 65249 KB  
Article
Modelling and Cavitation Discrepancy Analysis of Multi-Cylinder Engine Based on Variational Mode Decomposition (VMD)
by Lintao Li, Yaoyao Fan, Dong Liu, Guoxing Li, Haiyan Miao and Fengshou Gu
Machines 2026, 14(2), 224; https://doi.org/10.3390/machines14020224 - 13 Feb 2026
Abstract
Variations in liner vibration among cylinders can lead to non-uniform lubrication, accelerated wear, and cavitation in multi-cylinder diesel engines. This study investigated the origin of these variations in a heavy-duty straight-six diesel engine using a transient dynamic model of the cylinder assembly, modal [...] Read more.
Variations in liner vibration among cylinders can lead to non-uniform lubrication, accelerated wear, and cavitation in multi-cylinder diesel engines. This study investigated the origin of these variations in a heavy-duty straight-six diesel engine using a transient dynamic model of the cylinder assembly, modal analysis, and VMD. An elastic transient model of the block, liner, and piston system was developed with measured cylinder pressure, cylinder head bolt preload, and piston thermal deformation applied as boundary conditions. The model was validated against modal testing and bench measurements of liner acceleration. Under nominally identical piston excitation across all six cylinders, the computed liner responses were decomposed using VMD to extract intrinsic mode components and dominant frequency bands. The results show that the primary vibration response is concentrated in the upper-middle region of the liner, while the end cylinders exhibit higher vibration levels than the central cylinders. A dominant component centred at approximately 1800 Hz is identified and linked to an engine block mode whose spatial deformation pattern matches the cylinder-to-cylinder distribution of liner vibration and cavitation risk. These findings indicate that the inter-cylinder discrepancy is linked to engine block modal and non-uniformity constraints. The proposed model provides a basis for reliability-oriented mitigation of vibration and cavitation in multi-cylinder diesel engines. Full article
(This article belongs to the Section Machines Testing and Maintenance)
Show Figures

Figure 1

14 pages, 2167 KB  
Article
Software-Based Automation and Control Architecture for a Magnetic Refrigeration System
by Arda Zaim and Haydar Aras
Machines 2026, 14(2), 223; https://doi.org/10.3390/machines14020223 - 13 Feb 2026
Abstract
In this study, software-based, measurement-driven automation and control architecture is developed for magnetic refrigeration systems. The proposed structure integrates real-time measurement data obtained from magnetic, hydraulic, and thermal sub-processes within a single decision layer. Control actions are generated based on cycle-level performance feedback. [...] Read more.
In this study, software-based, measurement-driven automation and control architecture is developed for magnetic refrigeration systems. The proposed structure integrates real-time measurement data obtained from magnetic, hydraulic, and thermal sub-processes within a single decision layer. Control actions are generated based on cycle-level performance feedback. Instead of directly regulating absolute performance values, the control logic relies on performance trends between successive cycles as the primary decision variable. The method is experimentally implemented on a reciprocating magnetic refrigerator prototype. The system is first operated with fixed parameters, after which cycle-level adaptation is activated using measurement-based decisions. Experimental results show that adaptive control drives the system toward a stable and high-performance regime following a short transient phase. The average coefficient of performance (COP) increases from approximately 0.21 under manual operation to about 1.20 in adaptive operation, while cycle-to-cycle fluctuations are significantly reduced. The results indicate that operation based on fixed timing and preset parameters is insufficient for magnetic refrigeration systems. In contrast, software-based control using cyclic feedback shifts the system to a more stable and efficient regime. The proposed architecture provides a high-level control framework with low hardware dependency and can be adapted to different magnetic refrigeration configurations. Full article
(This article belongs to the Section Automation and Control Systems)
Show Figures

Figure 1

29 pages, 30945 KB  
Article
Robust Autonomous Perception for Indoor Service Machines via Geometry-Aware RGB-D SLAM and Probabilistic Dynamic Modeling
by Zhiyu Wang, Weili Ding and Wenna Wang
Machines 2026, 14(2), 222; https://doi.org/10.3390/machines14020222 - 12 Feb 2026
Abstract
Reliable autonomous perception is essential for indoor service machines operating in human-centered environments, where weak textures, repetitive structures, and frequent dynamic interference often degrade localization stability. Conventional RGB-D SLAM systems typically rely on static-scene assumptions or binary semantic masking, which are insufficient for [...] Read more.
Reliable autonomous perception is essential for indoor service machines operating in human-centered environments, where weak textures, repetitive structures, and frequent dynamic interference often degrade localization stability. Conventional RGB-D SLAM systems typically rely on static-scene assumptions or binary semantic masking, which are insufficient for handling persistent and fine-grained environmental dynamics. This paper presents a robust autonomous perception framework based on geometry-aware RGB-D SLAM, with a particular emphasis on probabilistic dynamic modeling at the feature level. The proposed system integrates multi-granularity geometric representations, including point features, parallel-line structures, and planar regions, to enhance geometric observability in low-texture indoor environments. On this basis, a probabilistic dynamic model is introduced to explicitly characterize feature reliability under motion, where dynamic probabilities are initialized by object detection and continuously updated through temporal consistency, spatial propagation, and multi-view geometric verification. Large-scale planar structures further serve as stable anchors to support robust pose estimation. Experimental results on the TUM RGB-D dynamic benchmark demonstrate that the proposed method significantly improves localization robustness, reducing the average ATE RMSE by approximately 66% compared with representative dynamic SLAM baselines. Additional evaluations on a real-world indoor dataset further validate its effectiveness for long-term autonomous perception under dense motion and frequent occlusions. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
35 pages, 3609 KB  
Article
Adaptive Variable Admittance Control for Intent-Aware Human–Robot Collaboration
by Mohammad Jahani Moghaddam and Filippo Arrichiello
Machines 2026, 14(2), 221; https://doi.org/10.3390/machines14020221 - 12 Feb 2026
Abstract
This paper presents a comprehensive framework for evaluating the robustness and adaptability of human–robot collaboration (HRC) controllers under a spectrum of dynamic and unpredictable human intentions. Building upon variable admittance controller (VAC) frameworks augmented with Radial Basis Function Neural Network (RBFNN) online adaptation, [...] Read more.
This paper presents a comprehensive framework for evaluating the robustness and adaptability of human–robot collaboration (HRC) controllers under a spectrum of dynamic and unpredictable human intentions. Building upon variable admittance controller (VAC) frameworks augmented with Radial Basis Function Neural Network (RBFNN) online adaptation, we introduce two key innovations: (1) an intent-aware human force generator capable of simulating aggressive, hesitant, oscillatory, conflicting, and nominal behaviors, through the modulation of force gains and the introduction of stochastic noise, and (2) the extension of VAC to incorporate variable stiffness as an adaptive control parameter alongside damping and inertia. The adaptive parameters are jointly tuned online using a self-supervised learning (SSL) mechanism driven by motion error metrics and interaction dynamics. The framework is simulated in a dual-arm collaborative manipulation scenario involving two 7-DoF Franka Emika Panda robots transporting a shared object in a high-fidelity simulation environment. Simulation results demonstrate the system’s capability to maintain stable behavior and minimize tracking error despite abrupt changes in human intent. This work provides a novel and systematic tool for stress-testing adaptive controllers in HRC, with implications for the design of resilient, safe, and reliable robotic systems in real-world collaborative environments. Full article
Show Figures

Figure 1

30 pages, 4508 KB  
Article
Closed-Form Dynamic Analysis of a Novel Planar TTR Manipulator Based on Virtual Work and Hamiltonian Mechanics
by Mahsa Hejazian, Ahad Zare Jond, Siamak Pedrammehr and Kais I. Abdul-Lateef Al-Abdullah
Machines 2026, 14(2), 220; https://doi.org/10.3390/machines14020220 - 12 Feb 2026
Abstract
This study presents the modeling, analysis, and control of a novel planar three-degrees-of-freedom TTR (Translational–Translational–Rotational) mechanism. A comprehensive kinematic and dynamic formulation is developed, with the governing equations derived analytically using the principles of virtual work and Hamiltonian mechanics. Due to the nonlinear [...] Read more.
This study presents the modeling, analysis, and control of a novel planar three-degrees-of-freedom TTR (Translational–Translational–Rotational) mechanism. A comprehensive kinematic and dynamic formulation is developed, with the governing equations derived analytically using the principles of virtual work and Hamiltonian mechanics. Due to the nonlinear nature of the inverse kinematics, a numerical solution based on the modified Newton–Raphson method is employed to compute joint trajectories. To ensure robust trajectory tracking in the presence of modeling uncertainties and external disturbances, a sliding-mode control strategy is designed and implemented. The proposed approach is evaluated through numerical simulations and experiments conducted on a custom-built prototype. Quantitative performance metrics, including mean squared error, are used to assess tracking accuracy and to compare simulation and experimental results. The consistency between analytical modeling, numerical solutions, and experimental observations demonstrates the feasibility of the proposed framework for planar robotic motion control applications. Full article
(This article belongs to the Special Issue Advances in Dynamic Analysis of Multibody Mechanical Systems)
Show Figures

Figure 1

23 pages, 32647 KB  
Article
Application of CILQR-Based Motion Planning and Tracking Control to Intelligent Tracked Vehicles
by Haoyu Jiang, Qunxin Liu, Guiyin Wang, Weiwei Han, Xiaoyu Yan, Pengcheng Yu and Yougang Bian
Machines 2026, 14(2), 219; https://doi.org/10.3390/machines14020219 - 12 Feb 2026
Abstract
To improve the safety of planned paths and the accuracy of tracking control for intelligent tracked vehicles, this paper investigates the application of a CILQR-based motion-planning and tracking-control framework to intelligent tracked vehicles. Firstly, based on an improved discrete-point quadratic smoothing algorithm and [...] Read more.
To improve the safety of planned paths and the accuracy of tracking control for intelligent tracked vehicles, this paper investigates the application of a CILQR-based motion-planning and tracking-control framework to intelligent tracked vehicles. Firstly, based on an improved discrete-point quadratic smoothing algorithm and the adapted CILQR, collision-free multi-objective optimal path generation in dynamic environment is achieved. Secondly, based on the discretization error model of the intelligent tracked vehicle, an LQR-MPC hybrid control method is proposed based on switching strategy. Finally, an experimental platform is formed, and real-vehicle tests are carried out. Experimental results demonstrate the efficiency and accuracy of the proposed framework. The adapted CILQR algorithm significantly reduces computation time to approximately 1.5 ms per iteration, ensuring real-time performance. Furthermore, field tests confirm that the hierarchical LQR-MPC controller achieves robust tracking with an average lateral error of only 5.7 cm at a speed of 0.5 m/s, effectively validating the system’s capability in obstacle avoidance and precise trajectory tracking. Full article
Show Figures

Figure 1

20 pages, 3772 KB  
Article
Multibody Based Parameter Estimation of Stewart Platform Using Particles Swarm Optimization
by Mohamed M. Elshami, Haitham El-Hussieny, Hiroyuki Ishii and Ayman Nada
Machines 2026, 14(2), 218; https://doi.org/10.3390/machines14020218 - 12 Feb 2026
Abstract
Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework [...] Read more.
Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework for generalized dynamic modeling and inertial parameter estimation of parallel robotic manipulators, demonstrated on the DeltaLab-SMT EX800 Stewart platform. A systematic constrained multibody dynamic formulation is developed using an iterative kinematic–dynamic coupling scheme to compute generalized coordinates and their time derivatives under prescribed motion trajectories. The proposed identification manifold is experimentally validated on the physical test rig, in which the platform motion is executed via the control/DAQ system, while inertial measurements are acquired using an external 6-axis motion sensor to obtain direct acceleration data from the moving platform. Platform acceleration measurements are mapped through the inverse dynamics of the multibody model to derive the corresponding generalized forces, providing a practical and cost-effective alternative to direct force measurement with transducers. A Kalman filter is subsequently employed to combine the measured and the model-predicted data, yielding optimally filtered estimates of the inertial coordinates for accurate parameter identification. Inertial parameters are estimated using particle swarm optimization and bench marked against a gradient-based Levenberg–Marquardt approach, with comparison in terms of convergence behavior, robustness, and estimation accuracy. The results support the proposed framework as a measurement-informed benchmark methodology for parameter estimation of parallel manipulators. Full article
(This article belongs to the Special Issue Advanced Design, Control, and Optimization for Parallel Manipulators)
15 pages, 947 KB  
Article
EKF- and ESKF-Based GNSS/INS Integrated Navigation Under the Interaction Multi-Filter Framework
by Shichao Zhang, Zi Yang and Chenxiao Cai
Machines 2026, 14(2), 217; https://doi.org/10.3390/machines14020217 - 12 Feb 2026
Abstract
In multirotor unmanned aerial vehicle (UAV) GNSS/INS integrated navigation systems, a single filter such as the extended Kalman filter (EKF) or the error-state extended Kalman filter (ESKF) is commonly adopted. However, both methods have inherent performance limitations. The EKF suffers from significant linearization [...] Read more.
In multirotor unmanned aerial vehicle (UAV) GNSS/INS integrated navigation systems, a single filter such as the extended Kalman filter (EKF) or the error-state extended Kalman filter (ESKF) is commonly adopted. However, both methods have inherent performance limitations. The EKF suffers from significant linearization errors in highly nonlinear flight scenarios, leading to degraded estimation accuracy. Although ESKF achieves higher precision during steady flight, its model assumptions may no longer strictly hold during aggressive maneuvers, causing performance degradation in complex flight missions. To address the limitations of using a single filter, this study proposes a dynamic filter selection strategy under the interaction multi-filter (IMF) framework. The approach builds on the interactive multiple model (IMM) method and establishes a cooperative mechanism between EKF and ESKF. By computing the filter likelihoods at each time step and updating the probability switching matrix, the framework adaptively selects the optimal filter based on the current flight conditions. Simulation results demonstrate that the proposed IMF-based strategy effectively avoids the performance bottlenecks of individual filters. In highly nonlinear environments, it reduces linearization errors and suppresses divergence trends; compared with traditional ESKF, the proposed algorithm 3D RMSE is reduced by 57.2%, compared with the adaptive robust EKF (AREKF), the proposed approach reduces positioning errors by up to 21.3%. The results confirm that IMF-based adaptive switching between EKF and ESKF yields a robust, high-precision solution for UAV navigation in complex operational scenarios. Full article
(This article belongs to the Section Automation and Control Systems)
Show Figures

Figure 1

20 pages, 1113 KB  
Article
Experimental Cross-Domain Bearing Fault Diagnosis Method Based on Local Mean Decomposition and Improved Transfer Component Analysis
by Jia-Peng Liu, Zi-Hang Lv, Jia-Li Wang, Xin-Cheng Yang, Zhen-Kun He and Run-Sen Zhang
Machines 2026, 14(2), 216; https://doi.org/10.3390/machines14020216 - 12 Feb 2026
Abstract
To address the issue of reduced fault diagnosis accuracy caused by insufficient samples in laboratory datasets, this study proposes an improved Transfer Component Analysis (TCA) algorithm with dynamic kernel parameter adjustment, combined with Local Mean Decomposition (LMD). Firstly, the original signals are decomposed [...] Read more.
To address the issue of reduced fault diagnosis accuracy caused by insufficient samples in laboratory datasets, this study proposes an improved Transfer Component Analysis (TCA) algorithm with dynamic kernel parameter adjustment, combined with Local Mean Decomposition (LMD). Firstly, the original signals are decomposed using LMD, and representative signal components are reconstructed based on the Pearson’s correlation coefficient to enhance feature representativeness. Then, multidimensional features, including Root Mean Square (RMS), kurtosis, and main frequency (MF), are extracted from the reconstructed signals to comprehensively reflect signal characteristics in terms of energy distribution, impact properties, and frequency structure. Subsequently, a dynamic kernel parameter adjustment strategy is incorporated into TCA to adaptively optimize the kernel parameters, effectively reducing the distribution discrepancy between the source and target domains and enhancing the generalization capability of cross-domain feature transfer. Finally, a Least Squares Support Vector Machine (LSSVM) classifier is employed to perform fault diagnosis on the reconstructed features. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic accuracy than traditional approaches under various operating conditions, especially when signals are complex and distribution differences are large, showing strong robustness and adaptability. Full article
(This article belongs to the Section Machines Testing and Maintenance)
Show Figures

Figure 1

15 pages, 2785 KB  
Article
The DC Voltage Balance Strategy Based on Model Predictive Control with a Smooth Switching Sequence in Three-Phase 3LNPC-CR
by Xu Peng, Hang Li, Lu Liu, Xiaohan Liu, Siqi An and Weidong Peng
Machines 2026, 14(2), 215; https://doi.org/10.3390/machines14020215 - 12 Feb 2026
Abstract
The three-level neutral-point-clamped cascaded rectifier (3LNPC-CR) is a key component in power electronic transformers (PET) due to its high efficiency and modular configuration. However, voltage imbalance among submodule DC links may cause system instability and degrade power quality. To address this issue, this [...] Read more.
The three-level neutral-point-clamped cascaded rectifier (3LNPC-CR) is a key component in power electronic transformers (PET) due to its high efficiency and modular configuration. However, voltage imbalance among submodule DC links may cause system instability and degrade power quality. To address this issue, this paper proposes a voltage balancing strategy based on Model Predictive Control with a Smooth Switching Sequence (MPC-3S). First, a negative-sequence current control strategy is introduced to equalize the voltages among phases. In addition, an improved modulation scheme is developed to predict and optimize system states in real time within the control horizon, dynamically selecting the optimal switching sequence to achieve rapid voltage equalization. Finally, simulation and experimental results on a three-phase, three-module 3LNPC-CR prototype demonstrate that the proposed MPC-3S strategy can achieve fast intra-phase voltage balancing within 0.1 s under load imbalance, while maintaining high-quality grid-side current. These results verify that the proposed method significantly enhances both the dynamic and steady-state performance of 3LNPC-CR systems, providing a practical and efficient solution to the voltage-balancing challenge in PET applications. Full article
(This article belongs to the Special Issue Research Progress and Prospects of Multi-Level Converters)
Show Figures

Figure 1

24 pages, 5450 KB  
Article
Interpretable and Noise-Robust Bearing Fault Diagnosis for CNC Machine Tools via Adaptive Shapelet-Based Deep Learning Model
by Weiqi Hu, Huicheng Zhou and Jianzhong Yang
Machines 2026, 14(2), 214; https://doi.org/10.3390/machines14020214 - 12 Feb 2026
Abstract
Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for [...] Read more.
Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for bearing fault diagnosis. The proposed model integrates three key components: (1) an adaptive multi-scale shapelet extraction module for discriminative pattern learning, (2) a gated parallel CNN with depthwise separable convolutions for multi-scale spatial feature extraction, (3) an enhanced bidirectional long short-term memory network with residual connections for temporal dependency modeling. A composite loss function combining cross-entropy, supervised contrastive learning, and multi-scale consistency regularization is employed for training. To simulate real-world industrial noise conditions, Gaussian, uniform, and impulse noise were injected into the signals. Experiments conducted on the CWRU and IMS datasets demonstrate that, compared with state-of-the-art methods, the proposed approach achieves stronger noise robustness, higher fault classification accuracy, and more stable performance under severe noise contamination. Full article
(This article belongs to the Section Advanced Manufacturing)
Show Figures

Figure 1

27 pages, 4377 KB  
Article
Modeling of an Impact Wrench for Use in Reducing Hand–Arm Vibrations
by Tashari ter Braack and Donald L. Margolis
Machines 2026, 14(2), 213; https://doi.org/10.3390/machines14020213 - 12 Feb 2026
Abstract
Impact wrenches are widely used in construction and automotive industries, yet they generate harmful vibrations that pose health risks to operators and reduce tool usability. This paper develops a practical, low-order bond-graph model of impact-wrench dynamics that captures interactions among the motor, hammer, [...] Read more.
Impact wrenches are widely used in construction and automotive industries, yet they generate harmful vibrations that pose health risks to operators and reduce tool usability. This paper develops a practical, low-order bond-graph model of impact-wrench dynamics that captures interactions among the motor, hammer, anvil, and hand/arm constraints, and validates it against measurements during bolt tightening into a steel plate. Predictions match measured RMS accelerations and spectral modes up to 200 Hz with a maximum relative RMS error of 11%. The analysis attributes dominant vibration sources to rotational and translational impacts between the hammer and anvil; notably, the translational (z-axis) impact contributes substantially to felt vibration while not being required for bolt tightening. The model provides physical insight into vibration origins and supports actionable design decisions, such as reducing the linear (z-axis) impact and adding rotational damping or control, consistent with standardized testing practice. Full article
(This article belongs to the Section Machine Design and Theory)
Show Figures

Figure 1

12 pages, 1533 KB  
Article
Using an Accelerometer for State Estimation on an Icosahedral Tensegrity Structure
by Brett Layer, Austin Brown and Jeffrey R. Hill
Machines 2026, 14(2), 212; https://doi.org/10.3390/machines14020212 - 11 Feb 2026
Viewed by 31
Abstract
This paper presents a methodology for implementing a Kalman filter on an icosahedral tensegrity system capable of performing state estimation on an icosahedral structure. A motion model based on the geometry of the system and a measurement model based on the Inertial Measurement [...] Read more.
This paper presents a methodology for implementing a Kalman filter on an icosahedral tensegrity system capable of performing state estimation on an icosahedral structure. A motion model based on the geometry of the system and a measurement model based on the Inertial Measurement Unit (IMU) data are derived for this purpose. Due to the nature of icosahedral tensegrity robots, accurately predicting the state of the robot through conventional models is difficult, primarily because the entire structure is rotated in 3D space during movement. As such, adding bearing or distance sensors to this robot is very difficult, since the location of those sensors with respect to the base frame changes with every rotation. This paper instead uses a simple kinematic model based on the predicted geometry of the structure to act as the motion model and uses the data from an accelerometer to create a measurement model. These models are then used in a Kalman filter to estimate the state of the system. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
Show Figures

Figure 1

17 pages, 608 KB  
Article
Physics-Informed Bayesian Inference for Virtual Testing and Prediction of Train Performance
by Kian Sepahvand, Christoph Schwarz, Oliver Urspruch and Frank Guenther
Machines 2026, 14(2), 211; https://doi.org/10.3390/machines14020211 - 11 Feb 2026
Viewed by 40
Abstract
This paper proposes a physics-informed Bayesian framework for virtual testing and predictive modeling of train performance, specifically addressing stopping-distance prediction. The approach unifies physical simulation models with data-driven statistical inference to achieve uncertainty-aware predictions under limited or noisy measurements. By embedding governing equations [...] Read more.
This paper proposes a physics-informed Bayesian framework for virtual testing and predictive modeling of train performance, specifically addressing stopping-distance prediction. The approach unifies physical simulation models with data-driven statistical inference to achieve uncertainty-aware predictions under limited or noisy measurements. By embedding governing equations of motion into a hierarchical Bayesian structure, the method systematically accounts for both model-form and data uncertainty, allowing explicit decomposition into aleatoric and epistemic components. A Gaussian process surrogate is employed to efficiently emulate high-fidelity physics simulations while preserving key dynamic behaviors and parameter sensitivities. The Bayesian formulation enables probabilistic calibration and validation, providing predictive distributions and confidence bounds. As a representative application, the framework is applied to the virtual prediction of train stopping distances, demonstrating how the proposed methodology captures nonlinear braking dynamics and quantifies uncertainty in safety-relevant performance metrics directly compatible with statistical verification standards such as EN 16834. The results confirm that the physics-informed Bayesian approach enables accurate, interpretable, and standards-aligned virtual testing across a wide range of dynamical systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Rail Transportation)
Show Figures

Figure 1

19 pages, 6519 KB  
Article
Control Method and Simulation of Reconfigurable Façade Cable-Driven Parallel Robots Based on Heuristic Local Rules
by Yujun Li, Chaofeng Liu, Yang Liu, Shengcong Li, Fujun Yang, Mingheng Yu, Zhiyuan Chen, Longhui Shao and Jingke Yan
Machines 2026, 14(2), 210; https://doi.org/10.3390/machines14020210 - 11 Feb 2026
Viewed by 41
Abstract
Traditional control strategies for Cable-Driven Parallel Robots (CDPRs) rely heavily on global kinematic modeling and precise calibration, severely limiting their adaptability in unstructured or dynamic environments. This study addresses the challenge of rapid deployment without geometric priors by proposing a reconfigurable CDPR system [...] Read more.
Traditional control strategies for Cable-Driven Parallel Robots (CDPRs) rely heavily on global kinematic modeling and precise calibration, severely limiting their adaptability in unstructured or dynamic environments. This study addresses the challenge of rapid deployment without geometric priors by proposing a reconfigurable CDPR system composed of modular units. A novel heuristic control strategy based on “4+2+1” local rules is introduced, comprising translational, attitude correction, and tension maintenance logic. By utilizing local feedback—including cable tension, attitude, and anchor orientation—this method generates control commands without requiring boundary condition calibration, thereby supporting real-time reconfiguration. Numerical simulations of a façade cleaning scenario demonstrate that the system maintains stability across varying topologies, including anchor position changes and unit failures. Compared to a benchmark kinematic method, the proposed strategy reduces trajectory tracking error by approximately 50.5% and suppresses the pitch Root Mean Square Error (RMSE) from a divergent 42.75° (traditional) to 1.52°, effectively preventing the attitude failure typical of uncalibrated model-based control. These findings confirm that the proposed rule-based approach significantly enhances robustness and adaptability, offering a practical solution for deploying CDPRs in complex environments without pre-existing maps. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
Show Figures

Figure 1

35 pages, 7094 KB  
Article
Beyond Linear Limits: Advanced Nonlinear Suspensions for Enhanced Vibration Control
by Farhad S. Samani, Amirali Mehrabian, Antonio Zippo and Francesco Pellicano
Machines 2026, 14(2), 209; https://doi.org/10.3390/machines14020209 - 10 Feb 2026
Viewed by 65
Abstract
The vehicle suspensions have the primary task of attenuating the forces coming from the road surface. The performance is directly linked to the stiffness of the suspension system. Traditional suspensions, composed of linear elements, effectively damp high frequencies but perform poorly at low [...] Read more.
The vehicle suspensions have the primary task of attenuating the forces coming from the road surface. The performance is directly linked to the stiffness of the suspension system. Traditional suspensions, composed of linear elements, effectively damp high frequencies but perform poorly at low frequencies. In this regard, non-linear suspensions, characterized by a non-linear force–displacement relationship, have been introduced. These types of suspensions achieve this characteristic by combining elements with positive stiffness with elements with negative stiffness, resulting in an equivalent system with quasi-zero stiffness (QZS) around the equilibrium. The performance of the QZS suspension system is analyzed here using the Multibody Dynamics software MSC Adams® (2022.2). Static characteristics, transmissibility, and isolation performance are investigated through dynamic tests based on road profiles according to ISO 8608 regulations generated using MATLAB® (R2022b). The proposed quasi-zero stiffness suspension demonstrates an improvement of approximately 19% in vibration attenuation compared to a conventional suspension system under realistic road excitations. Full article
Show Figures

Figure 1

49 pages, 3665 KB  
Article
Enhanced Rotating Machinery Fault Diagnosis Using Hybrid RBSO–MRFO Adaptive Transformer-LSTM for Binary and Multi-Class Classification
by Amir R. Ali and Hossam Kamal
Machines 2026, 14(2), 208; https://doi.org/10.3390/machines14020208 - 10 Feb 2026
Viewed by 69
Abstract
Accurate fault diagnosis in rotating machinery is critical for predictive maintenance and operational reliability in industrial applications. Despite the effectiveness of deep learning, many models underperform due to manually selected hyperparameters, which can lead to premature convergence, overfitting, weak generalization, and inconsistent performance [...] Read more.
Accurate fault diagnosis in rotating machinery is critical for predictive maintenance and operational reliability in industrial applications. Despite the effectiveness of deep learning, many models underperform due to manually selected hyperparameters, which can lead to premature convergence, overfitting, weak generalization, and inconsistent performance across binary and multi-class classification. To address these limitations, the study proposes a novel hybrid hyperparameter optimization framework that combines Robotic Brain Storm Optimization (RBSO) with Manta Ray Foraging Optimization (MRFO) to optimally fine-tune deep learning architectures, including MLP, LSTM, GRU-TCN, CNN-BiLSTM, and Transformer-LSTM models. The framework leverages RBSO for global search to promote diversity and prevent premature convergence, and MRFO for local search to enhance convergence toward optimal solutions, with their combined effect improving predictive model performance and methodological generalization. The approach was validated on three benchmark datasets, including Case Western Reserve University (CWRU), industrial machine fault detection (TMFD), and the Machinery Fault Dataset (MaFaulDa). Before optimization, Transformer-LSTM model achieved 98.35% and 97.21% accuracy on CWRU binary and multi-class classification, 99.52% and 98.57% on TMFD, and 98.18% and 92.82% on MaFaulDa. Following hybrid optimization, Transformer-LSTM exhibited superior performance, with accuracies increasing to 99.72% for both CWRU tasks, 99.97% for TMFD, and 99.98% and 98.60% for MaFaulDa, substantially reducing misclassification. These results demonstrate that the proposed RBSO–MRFO framework provides a scalable, robust, and high-accuracy solution for intelligent fault diagnosis in rotating machinery. Full article
(This article belongs to the Section Machines Testing and Maintenance)
Show Figures

Figure 1

20 pages, 3375 KB  
Article
Importance Measures for Vehicle Dust Pump Impeller Blade Fixture Parameters Based on BP Neural Network
by Feng Zhang, Jinze Liu, Xunhao Zhang, Yuxiang Tian and Ruijie Du
Machines 2026, 14(2), 207; https://doi.org/10.3390/machines14020207 - 10 Feb 2026
Viewed by 76
Abstract
The reliability of the dust pump in an engine air filtration system significantly affects vehicle performance. Therefore, the extent to which the parameters of the dust pump impeller blade fixture affect its reliability is a critical consideration during blade design. This study investigated [...] Read more.
The reliability of the dust pump in an engine air filtration system significantly affects vehicle performance. Therefore, the extent to which the parameters of the dust pump impeller blade fixture affect its reliability is a critical consideration during blade design. This study investigated the influences of various impeller blade fixture parameters on reliability. First, a three-dimensional finite element model of the vehicle dust pump was established to analyse the reliability of the impeller blade fixture in terms of deflection and stress according to parameter value. Next, a parametric model was established, and parameter uncertainties were defined for reliability analysis. The relationships between the different parameters and the reliability of the impeller blade fixture were subsequently predicted by a BP neural network model trained and tested using 400 and 100 samples, respectively. Finally, the output of the BP neural network model was applied to analyse the principal and total importance measures of each considered impeller blade parameter to fixture reliability. This study shows that in the reliability design of the dust pump impeller blades, priority should be given to rotational speed, blade thickness, and material density, as these factors have the greatest impact on the reliability of the blade mounting system. Full article
(This article belongs to the Section Machine Design and Theory)
Show Figures

Figure 1

17 pages, 17078 KB  
Article
Theoretical Design and Experimental Validation of a Vibro-Impact Support for Vibration Suppression
by Diego Francisco Ledezma-Ramírez, Emiliano Rustighi and Pablo Ernesto Tapia González
Machines 2026, 14(2), 206; https://doi.org/10.3390/machines14020206 - 10 Feb 2026
Viewed by 68
Abstract
To mitigate the high contact forces and noise inherent in traditional hard-impact dampers, this work evaluates the efficacy of a soft viscoelastic vibro-impact interface for passive vibration suppression. This study investigates the nonlinear dynamic behavior of a cantilever beam equipped with a soft [...] Read more.
To mitigate the high contact forces and noise inherent in traditional hard-impact dampers, this work evaluates the efficacy of a soft viscoelastic vibro-impact interface for passive vibration suppression. This study investigates the nonlinear dynamic behavior of a cantilever beam equipped with a soft vibro-impact interface, combining theoretical modeling and experimental validation to explore energy redistribution and damping enhancement mechanisms. The system is excited under both free and forced vibration conditions, and its response is characterized through tip displacement, acceleration, and impact force measurements. Numerical simulations based on an impact-contact model accurately predict the amplitude-dependent broadening and frequency shift observed in the experiments, demonstrating that the soft impacts introduce nonlinear stiffness and effective damping. The comparison between theoretical and experimental frequency responses confirms that energy is transferred from the primary mode to higher harmonics, leading to broadband vibration attenuation. These findings provide experimental evidence of the nonlinear energy transfer mechanisms previously predicted, including harmonic resonance stimulation and non-resonant energy exchange. The results demonstrate that soft-contact vibro-impact dampers can be effectively tuned to exploit nonlinear dynamics for enhanced passive vibration suppression, bridging the gap between theoretical predictions and practical implementations. Full article
Show Figures

Figure 1

26 pages, 2547 KB  
Article
An Artificial Plant Community with a Random-Pairwise Single-Elimination Tournament System for Conflict-Free Human–Machine Collaborative Manufacturing in Industry 5.0
by Zhengying Cai, Xinfei Dou, Cancan He, Huiyan Deng and Zhen Liu
Machines 2026, 14(2), 205; https://doi.org/10.3390/machines14020205 - 10 Feb 2026
Viewed by 87
Abstract
Human–machine collaborative manufacturing plays an important role in emerging Industry 5.0 and smart manufacturing. However, addressing the conflict-free human–machine collaborative manufacturing problem (CHMCMP) is extremely challenging because the cooperation and conflict between humans and machines are closely intertwined. This article examines the CHMCMP [...] Read more.
Human–machine collaborative manufacturing plays an important role in emerging Industry 5.0 and smart manufacturing. However, addressing the conflict-free human–machine collaborative manufacturing problem (CHMCMP) is extremely challenging because the cooperation and conflict between humans and machines are closely intertwined. This article examines the CHMCMP within the context of integrating the flexible job-shop scheduling problem (FJSP) and the flow-shop scheduling problem (FSP). Firstly, the CHMCMP was modeled as a job-flow-shop scheduling problem (JFSP), where machine processing is an FJSP and human operation is an FSP. Our goal is to complete all manufacturing jobs while pursuing multi-objective optimization, i.e., high manufacturing performance, conflict-free human–machine collaboration, and low no-load energy consumption. Secondly, an improved artificial plant community (APC) algorithm was developed to solve the NP-hard problem. A random-pairwise single-elimination tournament system is introduced for elite selection, with a time complexity of O(S) linearly correlated with the population size (S), superior to the sorting-based elite selection used by most evolutionary algorithms with polynomial time complexity, i.e., O(S3) of the genetic algorithm (GA) and O(S2) of the non-dominated sorting genetic algorithm-II (NSGA-II). Thirdly, a medium-scale benchmark dataset was exploited according to a human–machine collaborative manufacturing scenario. The Gantt charts of machine processing and human operating reveal that the FJSP and the FSP are entangled and are interdependent on each other in the CHMCMP, and solving FJSP and FSP separately cannot eliminate the conflict between the two. Compared with other state-of-the-art algorithms, the APC algorithm improves the makespan by up to 11.38%, the total transfer time of humans by up to 14.09%, and the no-loaded processing energy consumption by up to 12.62% with conflict avoidance. Full article
(This article belongs to the Section Advanced Manufacturing)
Show Figures

Figure 1

17 pages, 4166 KB  
Article
RCSA-Based Analysis of Stability Lobes in Milling Incorporating Tool Clamping Errors
by Jun-Hyun Jo, Ji-Wook Kim, Hong-In Won, Dae-Cheol Ko and Jin-Seok Jang
Machines 2026, 14(2), 204; https://doi.org/10.3390/machines14020204 - 9 Feb 2026
Viewed by 131
Abstract
This study proposes a methodology for selecting robust stable cutting conditions from a Receptance Coupling Substructure Analysis (RCSA)-based Stability Lobe Diagram (SLD) by considering tool clamping errors that may occur during operator tool setup. However, most existing RCSA studies have been conducted under [...] Read more.
This study proposes a methodology for selecting robust stable cutting conditions from a Receptance Coupling Substructure Analysis (RCSA)-based Stability Lobe Diagram (SLD) by considering tool clamping errors that may occur during operator tool setup. However, most existing RCSA studies have been conducted under the assumption of a constant tool clamping length and thus do not sufficiently reflect the clamping length variation observed in practical machining environments. Since the tool tip dynamic characteristics can be sensitive even to small variations in clamping length, operator-induced tool clamping errors in actual processes can introduce such variations and consequently degrade the prediction accuracy of the SLD. Moreover, uncertainty studies in milling stability have largely focused on variations in model parameters, such as cutting coefficients, damping, and modal parameters, whereas experimental quantification of operator-induced clamping length variability and its direct integration into RCSA-based tool tip Frequency Response Function (FRF) and SLD prediction has been relatively limited. Therefore, this study quantifies the distribution of tool clamping errors through clamping experiments and incorporates it into RCSA to derive an SLD band that accounts for tool clamping errors. The width of the SLD band is defined as a physical variation induced by clamping uncertainty, and the corresponding uncertainty range is set as an avoidance region. Robust cutting conditions are then selected from the remaining stable region while considering the physical variation width. The physical variation width was quantified as 60 rpm (minor axis) and 1.62 mm (major axis), representing the dispersion of the stability limit in the spindle speed and axial depth directions caused by clamping errors. As a result, stable cutting conditions that do not cross the stability limit can be determined even in the presence of process variations and disturbances. Full article
(This article belongs to the Section Advanced Manufacturing)
Show Figures

Figure 1

21 pages, 7022 KB  
Article
Influence of Lateral Wheelset Force on Track Buckling Behaviour
by Roman Schmid, Faris Karic, Martin Leitner and Ferdinand Pospischil
Machines 2026, 14(2), 203; https://doi.org/10.3390/machines14020203 - 9 Feb 2026
Viewed by 139
Abstract
The Prud’homme criterion, the limit value for lateral wheelset forces, has increasingly become a topic of discussion due to doubts about its correct application in railway vehicle assessment. Interpreted as a safety-related limit value for running dynamics, it is not precisely stated what [...] Read more.
The Prud’homme criterion, the limit value for lateral wheelset forces, has increasingly become a topic of discussion due to doubts about its correct application in railway vehicle assessment. Interpreted as a safety-related limit value for running dynamics, it is not precisely stated what hazard is to be avoided, especially since Prud’homme himself refers to maintenance relevance. The criterion does not apply to sudden track shifts under the wheelset, and the occurrence of track buckling does not depend on it. To help clarify this question, the influence of lateral wheelset forces on track buckling is specifically investigated by means of simulation. A track section is modelled and validated against historical measurements, and the influence of wheelsets on track buckling is calculated. We conclude that this limit cannot be relevant to safety. A revision of this approach is necessary. Full article
(This article belongs to the Section Vehicle Engineering)
Show Figures

Figure 1

38 pages, 2000 KB  
Article
Multi-Strategy Computational Algorithm for Sustainable Logistics: Solving the Green Vehicle Routing Problem with Mixed Fleet
by Yang Guan, Jie Yang, Ge Shi and Jinfa Shi
Machines 2026, 14(2), 202; https://doi.org/10.3390/machines14020202 - 9 Feb 2026
Viewed by 84
Abstract
Cold chain logistics distribution, a vital activity supporting global urbanization, faces complex challenges in balancing economic, environmental, and social objectives. This study investigates the green vehicle routing problem with a mixed fleet, simultaneously optimizing total cost, carbon emissions, and customer satisfaction. To solve [...] Read more.
Cold chain logistics distribution, a vital activity supporting global urbanization, faces complex challenges in balancing economic, environmental, and social objectives. This study investigates the green vehicle routing problem with a mixed fleet, simultaneously optimizing total cost, carbon emissions, and customer satisfaction. To solve this NP-hard problem, a novel multi-strategy NSGA-III algorithm is proposed, which integrates an adaptive pheromone update mechanism, elite route guidance, and genetic operators to significantly enhance search efficiency and solution diversity in complex solution spaces. Computational experiments on benchmark instances and a real-world case demonstrate the algorithm’s superior performance over mainstream multi-objective optimizers like NSGA-III and NSGA-II in metrics such as hypervolume. Sensitivity analysis further elucidates the impact of key operational parameters on system performance and provides a quantitative decision-making basis for greening urban cold chain fleets. This research offers an effective computational tool for complex sustainable logistics problems, with a modeling framework extensible to other industrial systems facing similar multi-objective trade-offs. Full article
(This article belongs to the Section Vehicle Engineering)
Show Figures

Figure 1

40 pages, 21213 KB  
Article
Intuitive, Low-Cost Cobot Control System for Novice Operators, Using Visual Markers and a Portable Localisation Scanner
by Peter George, Chi-Tsun Cheng and Toh Yen Pang
Machines 2026, 14(2), 201; https://doi.org/10.3390/machines14020201 - 9 Feb 2026
Viewed by 210
Abstract
Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of [...] Read more.
Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Manufacturing and Automation)
Show Figures

Figure 1

30 pages, 5059 KB  
Article
Economical Motion Planning for On-Road Autonomous Driving with Distance-Sensitive Spatio-Temporal Resolutions
by Yueshuo Sun and Bai Li
Machines 2026, 14(2), 200; https://doi.org/10.3390/machines14020200 - 9 Feb 2026
Viewed by 104
Abstract
Motion planning for on-road autonomous driving requires generating locally accurate spatio-temporal trajectories over a finite horizon, while facing increasing uncertainty and interaction variability toward distant regions. However, most existing planners employ uniform planning accuracy along the horizon, which implicitly treats far-field predictions with [...] Read more.
Motion planning for on-road autonomous driving requires generating locally accurate spatio-temporal trajectories over a finite horizon, while facing increasing uncertainty and interaction variability toward distant regions. However, most existing planners employ uniform planning accuracy along the horizon, which implicitly treats far-field predictions with the same fidelity as near-term execution. This uniform treatment often leads to unnecessary computational effort and reduced planning efficiency without improving near-field feasibility. This paper presents an economical motion planning framework that allocates planning accuracy according to the spatio-temporal distance from the ego vehicle. The framework preserves high-fidelity planning in the near field where execution is imminent, while progressively reducing resolution and solution depth in the far field where uncertainty dominates and replanning is expected. A two-stage architecture is adopted, combining a distance-aware search for coarse path and velocity generation with distance-sensitive numerical refinement that prioritizes near-field feasibility under receding horizon execution. The simulation results demonstrate improved computational efficiency and planning reliability compared with uniform resolution baselines. Real-world experiments validate the stable online replanning performance in dynamic environments. Full article
Show Figures

Figure 1

40 pages, 5226 KB  
Article
Adaptive Polar Lights Optimizer for Smart Electric Vehicle Charging Under Price Uncertainty and Battery Degradation
by Abdelkrim Benmoulai, Salah Kamel and Francisco Jurado
Machines 2026, 14(2), 199; https://doi.org/10.3390/machines14020199 - 9 Feb 2026
Viewed by 92
Abstract
This paper investigates an algorithmic redesign tailored to cost minimization with degradation awareness EV charging under an uncertainty framework for coordinated grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling. An improved variant of the Polar Lights Optimizer (IPLO) is developed through the integration of Random [...] Read more.
This paper investigates an algorithmic redesign tailored to cost minimization with degradation awareness EV charging under an uncertainty framework for coordinated grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling. An improved variant of the Polar Lights Optimizer (IPLO) is developed through the integration of Random Walk Exploitation (RWE) to enhance local refinement and Periodic Random Parameter Tuning (PRPT) to improve adaptability under uncertainty. In addition, an adaptive control mechanism is incorporated to adjust charging and discharging actions based on battery capacity degradation and dynamic electricity price signals. The presented framework is evaluated through simulation-based case studies and compared with several recent metaheuristic algorithms. The results demonstrate cost reductions of up to 25.42% over the original PLO and 80.78% relative to a non-optimized baseline, faster convergence, and improved robustness to price uncertainty, while mitigating adverse battery degradation effects. A statistical analysis over multiple independent runs confirms the reliability and consistency of the presented approach, highlighting its suitability for smart EV charging optimization in dynamic operating environments. Full article
Show Figures

Figure 1

26 pages, 2533 KB  
Article
Efficient Solid-Shell ABAQUS Modeling of Electromechanical Behavior in Porous FGM Structures with Smart-Layer Bonding
by Lotfi Ben Said, Alaa Chabir and Fakhreddine Dammak
Machines 2026, 14(2), 198; https://doi.org/10.3390/machines14020198 - 9 Feb 2026
Viewed by 108
Abstract
The present study provides a comprehensive investigation into the electromechanical response of porous Functionally Graded Material (FGM) shell structures with bonded piezoelectric layers, achieved through the implementation of an efficient solid-shell element in the ABAQUS (6.14) software. The basis for the modeled element [...] Read more.
The present study provides a comprehensive investigation into the electromechanical response of porous Functionally Graded Material (FGM) shell structures with bonded piezoelectric layers, achieved through the implementation of an efficient solid-shell element in the ABAQUS (6.14) software. The basis for the modeled element lies in the refinement of the established First Shear Deformation Theory (FSDT), coupled with the application of the assumed natural strain (ANS) and enhanced assumed strain (EAS) methodologies. The synergy between the two approaches results in enhanced efficiency in capturing the transverse shear strain while simultaneously addressing locking problems. Subsequently, the developed solid-shell element is incorporated into the Abaqus code through the user element interface to account for the shear strains across the FGM shell thickness. The computed results have been verified against the solutions reported in existing literature. Through this approach, the impact of the power law index and the degree of porosity on the electromechanical performance of FGM structures containing integrated piezoelectric patches is explored and presented. As a result, the findings reveal that the power law index influences the FGM distribution, and the porosity reduces the overall structural rigidity, which in turn prompts larger deflections in the porous FGM shell structures. Full article
(This article belongs to the Special Issue Design and Manufacturing for Lightweight Components and Structures)
24 pages, 3006 KB  
Article
A Digital-Twin-Enabled AI-Driven Adaptive Planning Platform for Sustainable and Reliable Manufacturing
by Mingyuan Li, Chun-Ming Yang, Wei Lo and Yi-Wei Kao
Machines 2026, 14(2), 197; https://doi.org/10.3390/machines14020197 - 9 Feb 2026
Viewed by 167
Abstract
The manufacturing systems face growing demands due to the instability of the market, the demanding sustainability policies, and the high rate of old equipment, but traditional planning structures are mostly fixed and deterministic, leading to the inefficiency of joint optimization of operational stability [...] Read more.
The manufacturing systems face growing demands due to the instability of the market, the demanding sustainability policies, and the high rate of old equipment, but traditional planning structures are mostly fixed and deterministic, leading to the inefficiency of joint optimization of operational stability and environmental sustainability in unpredictable situations. This research proposed and empirically tested an artificial-intelligence-based adaptive planning platform, which combines a physics-based Digital Twin (DT) and a Pareto-conditioned Multi-Objective Proximal Policy Optimization (MO-PPO) algorithm to be able to co-optimize reliability and sustainability indicators in real-time. The platform reinvents manufacturing planning as a Constrained Multi-Objective Markov Decision Process (CMDP), optimizing an Overall Equipment Effectiveness (OEE) and energy carbon intensity as well as material waste, and strongly adhering to operational restrictions. The study utilizes a four-layer cyber–physical architecture, which includes an edge-based data acquisition layer, a high-fidelity stochastic simulation engine that is calibrated via Bayesian inference, a graph attention network-based state-encoding layer, and a closed-loop execution loop that runs with 60 s long planning cycles. In this study, a statistically significant enhancement was shown in 10,000 stochastic simulation experiments and a 12-week industrial pilot deployment: 96.8% schedule performance, 84.7% OEE, 16.5% cut in specific energy usage (2.38 kWh/kg), 17.1% reduction in material-waste rate (6.8%), and 21.4% enhancement in carbon effectiveness, outperforming all baseline strategies (p = 0.001). The analysis showed that there was a surprising synergistic correlation between waste minimization and OEE enhancement (r = −0.73), and 34.1% of overall OEE improvement could be explained by sustainability strategies. This study provides a robust framework for adaptive, resilient, and eco-friendly manufacturing processes in line with Industry 5.0 ideologies. Full article
(This article belongs to the Special Issue Digital Twins in Smart Manufacturing)
Show Figures

Figure 1

26 pages, 7296 KB  
Article
AI-Driven Tool Wear Prediction Under Severe Data Scarcity with SHAP-Guided Feature Selection and Fold-Safe Augmentation: A Case Study of Titanium Microdrilling
by Saman Fattahi, Bahman Azarhoushang, Masih Paknejad and Heike Kitzig-Frank
Machines 2026, 14(2), 196; https://doi.org/10.3390/machines14020196 - 9 Feb 2026
Viewed by 173
Abstract
Microdrilling of titanium alloys suffers from rapid tool wear that degrades surface quality and dimensional accuracy, while industrial datasets are often too small for conventional data-hungry models. This work proposes a general, AI-driven modelling framework for tool wear prediction under severe data scarcity, [...] Read more.
Microdrilling of titanium alloys suffers from rapid tool wear that degrades surface quality and dimensional accuracy, while industrial datasets are often too small for conventional data-hungry models. This work proposes a general, AI-driven modelling framework for tool wear prediction under severe data scarcity, which is validated using a titanium microdrilling case study. The study focuses on maximum flank-wear prediction (VBmax) using 18 experimental observations (VBmax = 4–13 µm). Three regression models—support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were benchmarked under multiple validation protocols, with leave-one-out cross-validation (LOOCV) used as the primary assessment due to the limited sample size. To improve reliability and transparency, feature selection was performed using SHapley Additive exPlanations (SHAP), yielding a compact, interpretable feature subset dominated by thrust-force descriptors. Robustness was further evaluated using hyperparameter tuning and a conservative, leakage-controlled (“fold-safe”) augmentation strategy applied strictly within training folds. After tuning and fold-safe augmentation, XGBoost achieved the best LOOCV performance (R2 = 0.89, MSE = 0.70 µm2, MAPE = 7.62%). External validation on two additional tools under identical cutting conditions using a frozen model configuration showed bounded prediction errors under geometry and coating shifts. Overall, the results indicate that combining systematic benchmarking, SHAP-guided explainable feature selection, and leakage-controlled augmentation can enable accurate and interpretable VBmax prediction in the investigated titanium microdrilling case study, while broader validation across additional tools and cutting conditions is required to confirm generalization. Full article
Show Figures

Figure 1

Previous Issue
Back to TopTop