Machines

25 pages, 4139 KB

Open AccessArticle

Gain-Enhanced Correlation Fusion for PMSM Inter-Turn Faults Severity Detection Using Machine Learning Algorithms

by Vasileios I. Vlachou, Theoklitos S. Karakatsanis, Karolina Kudelina, Dimitrios E. Efstathiou and Stavros D. Vologiannidis

Machines 2026, 14(1), 134; https://doi.org/10.3390/machines14010134 - 22 Jan 2026

Viewed by 198

Abstract

Diagnosing faults in Permanent Magnet Synchronous Motors (PMSMs) is critical for ensuring their reliable operation, particularly in detecting internal short-circuit faults in the stator windings. These faults, such as inter-turn and inter-coil short circuits, can significantly affect motor performance and lead to costly [...] Read more.

Diagnosing faults in Permanent Magnet Synchronous Motors (PMSMs) is critical for ensuring their reliable operation, particularly in detecting internal short-circuit faults in the stator windings. These faults, such as inter-turn and inter-coil short circuits, can significantly affect motor performance and lead to costly downtime if not detected early. However, detecting these faults accurately, especially in the presence of operational noise and varying load conditions, remains a challenging task. To address this, a novel methodology is proposed for diagnosing and classifying fault severity in PMSMs using vibration and current data. The key innovation of the method is the combination of signal processing for both vibration and current data, to enhance fault detection by applying advanced feature extraction techniques such as root mean square (RMS), peak-to peak values, and spectral entropy in both time and frequency domains. Furthermore, a cooperative gain transformation is applied to amplify weak correlations between vibration and current signals, improving detection sensitivity, especially during early fault progression. In this study, the publicly available dataset on Mendeley, which consists of vibration and current measurements from three PMSMs with different power ratings of 1.0 kW, 1.5 kW, and 3.0 kW, was used. The dataset includes eight different levels of stator fault severity, ranging from 0% up to 37.66%, and covers normal operation, inter-coil short circuit, and inter-turn short circuit. The results demonstrate the effectiveness of the proposed methodology, achieving an accuracy of 96.6% in fault classification. The performance values for vibration and current measurements, along with the corresponding fault severities, validate the method’s ability to accurately detect faults across various operating conditions. Full article

(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)

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20 pages, 5007 KB

Open AccessArticle

Longitudinal, Lateral, and Vertical Coordinated Control of Active Hydro-Pneumatic Suspension System Based on Model Predictive Control for Mining Dump Truck

by Lin Yang, Guangjia Wang, Hao Cui, Wei Liu and Lanchun Zhang

Machines 2026, 14(1), 133; https://doi.org/10.3390/machines14010133 - 22 Jan 2026

Viewed by 102

Abstract

Considering the variability of driving conditions in mining areas, existing control strategies are difficult to meet the comprehensive performance requirements of mining dump trucks in the longitudinal, lateral, and vertical directions. Longitudinal, lateral, and vertical (LLV) coordinated control of active hydro-pneumatic suspension system [...] Read more.

Considering the variability of driving conditions in mining areas, existing control strategies are difficult to meet the comprehensive performance requirements of mining dump trucks in the longitudinal, lateral, and vertical directions. Longitudinal, lateral, and vertical (LLV) coordinated control of active hydro-pneumatic suspension system based on model predictive control (MPC) is constructed in this paper. The vehicle dynamic response under random road surface input based on wheelbase characteristics is established, and the rationality of the active hydro-pneumatic suspension LLV coordinated control strategy based on MPC is analyzed. Handling stability is taken as the overall control objective for active hydro-pneumatic suspension on C-class road surfaces. The dynamic tire loads of the six wheels of the mining dump truck are reduced by 25.8%, 29.1%, 30.6%, 27.6%, 29.9%, and 28.1%, respectively, in the unloaded state, while the longitudinal, lateral, and vertical body accelerations have not deteriorated. Under the E-class road surface, the overall control objective of the mining dump truck is comfort, and the longitudinal, lateral, and vertical accelerations in the unloaded state have been optimized by 34.6%, 31.4%, and 34.1%, respectively. Full article

(This article belongs to the Section Vehicle Engineering)

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20 pages, 6579 KB

Open AccessArticle

Study on Torque and Contact Characteristics of Thrust Bearing with Skewed Rollers in No-Back Brake

by Tianming Ren, Shuanglu Li, Ziyu Cheng and Ming Feng

Machines 2026, 14(1), 132; https://doi.org/10.3390/machines14010132 - 22 Jan 2026

Viewed by 118

Abstract

To investigate the performance of skewed roller thrust bearings (SRTBs) in the no-back brake of horizontal stabilizer trim actuators (HSTAs), this study conducts systematic theoretical modelling, experimental validation, and numerical simulation focusing on torque and contact characteristic optimization. First, a theoretical model for [...] Read more.

To investigate the performance of skewed roller thrust bearings (SRTBs) in the no-back brake of horizontal stabilizer trim actuators (HSTAs), this study conducts systematic theoretical modelling, experimental validation, and numerical simulation focusing on torque and contact characteristic optimization. First, a theoretical model for resistance torque of the SRTB was established based on the kinematics and load behaviours, followed by a systematic investigation into the effects of roller centre position and skew angle on the bearing’s resistance torque. An experimental platform was built, and tests were carried out on the bearings to verify the results of the theoretical analysis. Subsequently, a tangent arc profile was applied to the rollers to mitigate stress concentration at their ends, and the influences of crown drop and straight segment length on roller contact stress were explored by finite element method. Finally, considering the actual operating conditions of no-back brake components, the effect of roller centre position on brake deformation and roller contact stress was studied. The results show that the resistance torque increases with both roller skew angle and centre position, but is insensitive to rotational speed. Roller contact stress first decreases rapidly and then increases gradually with crown drop, indicating the existence of an optimal crown drop value. This optimal value first decreases and then increases with increasing straight segment length, with the optimal parameters determined as 9 μm (crown drop) and 4 mm (straight segment length). In practical applications, asymmetric loading on the two sides of the ratchet disc causes uneven roller contact distribution and stress concentration. Adjusting the roller centre position to balance the deformation of the ratchet disc and rod shoulder can effectively reduce contact stress, with the optimal position being approximately 48 mm (slightly offset from the load centre of 49 mm). This study provides valuable insights for the optimal design of SRTBs and no-back brakes. Full article

(This article belongs to the Section Friction and Tribology)

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18 pages, 5390 KB

Open AccessArticle

Multilevel Modeling and Validation of Thermo-Mechanical Nonlinear Dynamics in Flexible Supports

by Xiangyu Meng, Qingyu Zhu, Qingkai Han and Junzhe Lin

Machines 2026, 14(1), 131; https://doi.org/10.3390/machines14010131 - 22 Jan 2026

Viewed by 104

Abstract

Prediction accuracy for complex flexible support systems is often limited by insufficiently characterized thermo-mechanical couplings and nonlinearities. To address this, we propose a multilevel hybrid parallel–serial model that integrates the thermo-viscous effects of a Squeeze Film Damper (SFD) via a coupled Reynolds–Walther equation, [...] Read more.

Prediction accuracy for complex flexible support systems is often limited by insufficiently characterized thermo-mechanical couplings and nonlinearities. To address this, we propose a multilevel hybrid parallel–serial model that integrates the thermo-viscous effects of a Squeeze Film Damper (SFD) via a coupled Reynolds–Walther equation, the structural flexibility of a squirrel-cage support using Finite Element analysis, and the load-dependent stiffness of a four-point contact ball bearing based on Hertzian theory. The resulting state-dependent system is solved using a force-controlled iterative numerical algorithm. For validation, a dedicated bidirectional excitation test rig was constructed to decouple and characterize the support’s dynamics via frequency-domain impedance identification. Experimental results indicate that equivalent damping is temperature-sensitive, decreasing by approximately 50% as the lubricant temperature rises from 30 °C to 100 °C. In contrast, the system exhibits pronounced stiffness hardening under increasing loads. Theoretical analysis attributes this nonlinearity primarily to the bearing’s Hertzian contact mechanics, which accounts for a stiffness increase of nearly 240%. This coupled model offers a distinct advancement over traditional linear approaches, providing a validated framework for the design and vibration control of aero-engine flexible supports. Full article

(This article belongs to the Special Issue Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery)

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34 pages, 10715 KB

Open AccessArticle

Features of the Data Collection and Transmission Technology in an Intelligent Thermal Conditioning System for Engines and Vehicles Operating on Thermal Energy Storage Technology Based on a Digital Twin

by Igor Gritsuk and Justas Žaglinskis

Machines 2026, 14(1), 130; https://doi.org/10.3390/machines14010130 - 22 Jan 2026

Viewed by 102

Abstract

This article examines an integrated approach to data acquisition and transmission within an intelligent thermal conditioning system for engines and vehicles that operates using thermal energy storage and the digital twin concept. The system is characterized by its use of multiple primary energy [...] Read more.

This article examines an integrated approach to data acquisition and transmission within an intelligent thermal conditioning system for engines and vehicles that operates using thermal energy storage and the digital twin concept. The system is characterized by its use of multiple primary energy sources to power internal subsystems and maintain optimal engine and vehicle temperature conditions. Building on a formalized conceptual model of the intelligent thermal conditioning system, the study identifies key technological features required for implementing complex operational processes, as well as the stages necessary for applying the proposed approach during the design and modernization phases throughout the system’s life cycle. A core block diagram of the system’s digital twin is presented, developed using mathematical models that describe support and monitoring processes under real operating conditions. Additionally, an architectural framework for organizing data collection and transmission is proposed, highlighting the integration of digital twin technologies into the thermal conditioning workflow. The article also introduces methods for adaptive data formation, transfer, and processing, supported by a specialized onboard software-diagnostic complex that enables structured information management. The practical implementation of the proposed solutions has the potential to enhance the energy efficiency of thermal conditioning processes and improve the reliability of vehicles employing thermal energy storage technologies. Full article

(This article belongs to the Special Issue Data-Driven Fault Diagnosis for Machines and Systems, 2nd Edition)

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31 pages, 5019 KB

Open AccessArticle

Automatic Synthesis of Planar Multi-Loop Fractionated Kinematic Chains with Multiple Joints: Topological Graph Atlas and a Mine Scaler Manipulator Case Study

by Xiaoxiong Li, Jisong Ding and Huafeng Ding

Machines 2026, 14(1), 129; https://doi.org/10.3390/machines14010129 - 22 Jan 2026

Viewed by 103

Abstract

Planar multi-loop fractionated kinematic chains (FKCs)—kinematic chains that can be decomposed into two or more coupled subchains by separating joints or links—are widely used in heavy-duty manipulators, yet their large design space makes automatic synthesis and application-oriented screening challenging. The novelty of this [...] Read more.

Planar multi-loop fractionated kinematic chains (FKCs)—kinematic chains that can be decomposed into two or more coupled subchains by separating joints or links—are widely used in heavy-duty manipulators, yet their large design space makes automatic synthesis and application-oriented screening challenging. The novelty of this paper is a general automated synthesis-and-screening framework for planar fractionated kinematic chains, regardless of whether multiple joints are present; multiple-joint chains are handled via an equivalent transformation to single-joint models, enabling the construction of a deduplicated topological graph atlas. In the mine scaler manipulator case study, an 18-link, 5-DOF (N18_M5) FKC with two multiple joints is taken as the target and converted into a single-joint equivalent N20_M7 model consisting of three subchains (KC1–KC3). Atlases of the required non-fractionated kinematic chains (NFKCs) for KC1 and KC3 are generated according to their link counts and DOFs. The subchains are then combined as building blocks under joint-fractionation (A-mode) and link-fractionation (B-mode) to enumerate fractionated candidates, and a WL-hash-based procedure is employed for isomorphism discrimination to obtain a non-isomorphic N20_M7 atlas. Finally, a connectivity-calculation-based screening is performed under task-driven structural and functional constraints, yielding 249 feasible configurations for the overall manipulator arm. The proposed pipeline provides standardized representations and reproducible outputs, offering a practical and transferable route from large-scale enumeration to engineering-feasible configuration sets for planar multi-loop FKCs, including those with multiple joints. Full article

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

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28 pages, 2068 KB

Open AccessArticle

Autonomous Offroad Vehicle Real-Time Multi-Physics Digital Twin: Modeling and Validation

by Mattias Lehto, Torbjörn Lindbäck, Håkan Lideskog and Magnus Karlberg

Machines 2026, 14(1), 128; https://doi.org/10.3390/machines14010128 - 22 Jan 2026

Viewed by 83

Abstract

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, [...] Read more.

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, an absence of versatility for multi-purpose use is even more prevalent in current digital models. In response to these challenges, this work presents a novel real-time multi-physics digital twin of an offroad vehicle with high levels of fidelity and validity, both regarding the vehicle dynamics and hydraulics, as well as regarding the visual representation of the environment and the exteroceptive sensor emulation. The versatility of the digital twin enables its usage for vehicle development tasks concerning mechanical components and driveline, as well as for visual machine learning tasks, such as generation of auto-annotated visual training data. Development of control algorithms leveraging both visual input and mechanical systems is also enabled. Furthermore, the real-time capability allows for Hardware-in-the-Loop and Vehicle-in-the-Loop simulation. The modeling, calibration, and real-world validation of the digital twin is presented, with an emphasis on the vehicle dynamics and hydraulics. The shown validity enables advancements in the development of autonomous offroad vehicles. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control, 2nd Edition)

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18 pages, 3948 KB

Open AccessArticle

A Model-Based Spatio-Temporal Behavior Decider for Autonomous Driving

by Yiwen Huang, Huikang Zhang, Junchan Liao, Ruhong Zhuang, Honggou Yang and Xianming Liu

Machines 2026, 14(1), 127; https://doi.org/10.3390/machines14010127 - 22 Jan 2026

Viewed by 83

Abstract

Spatio-temporal planning has emerged as a robust methodology for solving trajectory planning challenges in complex autonomous driving scenarios. By integrating both spatial and temporal variables, this approach facilitates the generation of highly accurate, human-like, and interpretable trajectory decisions. This paper presents a novel [...] Read more.

Spatio-temporal planning has emerged as a robust methodology for solving trajectory planning challenges in complex autonomous driving scenarios. By integrating both spatial and temporal variables, this approach facilitates the generation of highly accurate, human-like, and interpretable trajectory decisions. This paper presents a novel learned planning model-based spatio-temporal behavior decider, engineered to produce optimal and explainable driving trajectories with enhanced efficiency and passenger comfort. The proposed decider systematically evaluates the action space of the ego-vehicle, selecting the trajectory that optimizes overall driving performance. This method is particularly significant for autonomous driving systems, as it ensures the generation of human-like trajectories while maintaining high driving efficiency. The efficacy of the proposed framework has been comprehensively validated through rigorous simulations and real-world experimental trials on a commercial passenger vehicle platform, demonstrating its practical utility and performance advantages. Full article

(This article belongs to the Special Issue Trajectory Planning for Autonomous Vehicles: State of the Art)

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21 pages, 2194 KB

Open AccessArticle

Convolutional Autoencoder-Based Method for Predicting Faults of Cyber-Physical Systems Based on the Extraction of a Semantic State Vector

by Konstantin Zadiran and Maxim Shcherbakov

Machines 2026, 14(1), 126; https://doi.org/10.3390/machines14010126 - 22 Jan 2026

Viewed by 76

Abstract

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life [...] Read more.

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL), are used as a crucial step in a framework of reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high-frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for fault prediction, which is based on extraction of semantic state vectors (SSVs) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state, is proposed. In this method, working cycles are detected in input data stream, and then they are converted to images, on which an autoencoder is trained. The output of an intermediate layer of an autoencoder is extracted and processed into SSVs. SSVs are then combined into a time series on which RUL is forecasted. After optimization of hyperparameters, the proposed method shows the following results: RMSE = 1.799, MAE = 1.374. These values are significantly more accurate than those obtained using existing methods: RMSE = 14.02 and MAE = 10.71. Therefore, SSV extraction is a viable technique for forecasting RUL. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions)

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20 pages, 2671 KB

Open AccessArticle

Semantic-Aligned Multimodal Vision–Language Framework for Autonomous Driving Decision-Making

by Feng Peng, Shangju She and Zejian Deng

Machines 2026, 14(1), 125; https://doi.org/10.3390/machines14010125 - 21 Jan 2026

Viewed by 174

Abstract

Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from limited semantic consistency, weak task controllability, and insufficient interpretability. To address these challenges, we propose [...] Read more.

Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from limited semantic consistency, weak task controllability, and insufficient interpretability. To address these challenges, we propose SemAlign-E2E (Semantic-Aligned End-to-End), a semantic-aligned multimodal LVLM framework that unifies visual, LiDAR, and task-oriented textual inputs through cross-modal attention. This design enables end-to-end reasoning from scene understanding to high-level driving command generation. Beyond producing structured control instructions, the framework also provides natural-language explanations to enhance interpretability. We conduct extensive evaluations on the nuScenes dataset and CARLA simulation platform. Experimental results show that SemAlign-E2E achieves substantial improvements in driving stability, safety, multi-task generalization, and semantic comprehension, consistently outperforming state-of-the-art baselines. Notably, the framework exhibits superior behavioral consistency and risk-aware decision-making in complex traffic scenarios. These findings highlight the potential of LVLM-driven semantic reasoning for autonomous driving and provide a scalable pathway toward future semantic-enhanced end-to-end driving systems. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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22 pages, 2039 KB

Open AccessArticle

A Machine Learning Framework for the Prediction of Propeller Blade Natural Frequencies

by Nícolas Lima Oliveira, Afonso Celso de Castro Lemonge, Patricia Habib Hallak, Konstantinos G. Kyprianidis and Stavros Vouros

Machines 2026, 14(1), 124; https://doi.org/10.3390/machines14010124 - 21 Jan 2026

Viewed by 244

Abstract

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design [...] Read more.

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves

R^{2} > 0.90

and

N R M S E < 0.08

for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article

(This article belongs to the Section Turbomachinery)

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16 pages, 3114 KB

Open AccessArticle

Nonlinear Disturbance Observer-Based Adaptive Anti-Lock Braking Control of Electro-Hydraulic Brake Systems with Unknown Tire–Road-Friction Coefficient

by Soon Gu Kwon and Sung Jin Yoo

Machines 2026, 14(1), 123; https://doi.org/10.3390/machines14010123 - 21 Jan 2026

Viewed by 107

Abstract

This paper addresses a recursive adaptive anti-lock braking (AB) control design problem for electro-hydraulic brake (EHB) systems subject to unknown tire–road-friction coefficients and disturbances. Compared with the relevant literature, the primary contributions are (i) the development of a novel nonlinear AB model integrated [...] Read more.

This paper addresses a recursive adaptive anti-lock braking (AB) control design problem for electro-hydraulic brake (EHB) systems subject to unknown tire–road-friction coefficients and disturbances. Compared with the relevant literature, the primary contributions are (i) the development of a novel nonlinear AB model integrated with a bond-graph-based EHB (BGEHB) system, and (ii) the proposal of an adaptive neural AB control approach incorporating a nonlinear disturbance observer (NDO). The AB and BGEHB models are unified into a single nonlinear braking model, with the wheel speed as the system output and the duty ratios of the BGEHB inlet and outlet valves as control inputs. To maintain an optimal slip ratio during braking, we design the NDO-based adaptive AB controller to regulate the wheel speed in a recursive manner. The designed controller incorporates a delay-compensation term to address the time-delay characteristics of the hydraulic system, employs a neural-network function approximator in the NDO and controller to compensate for the unknown tire–road-friction coefficient, and applies NDOs to compensate for disturbances due to the vehicle motion and BGEHB dynamics. The stability of the proposed control scheme is established via the Lyapunov theory, and a simulation comparison is presented to demonstrate the effectiveness of the proposed design approach. Full article

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

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15 pages, 9324 KB

Open AccessArticle

Melt Pool Dynamics and Quantitative Prediction of Surface Topography in Laser Selective Forming of Optical Glass

by Lianshuang Ning, Weijie Fu and Xinming Zhang

Machines 2026, 14(1), 122; https://doi.org/10.3390/machines14010122 - 21 Jan 2026

Viewed by 138

Abstract

Laser local forming is an effective method for reshaping optical glass, yet the deformation of the material during the cooling phase remains poorly understood. This study investigates the dynamic evolution of the molten pool, specifically focusing on the transition from an initial convex [...] Read more.

Laser local forming is an effective method for reshaping optical glass, yet the deformation of the material during the cooling phase remains poorly understood. This study investigates the dynamic evolution of the molten pool, specifically focusing on the transition from an initial convex shape to a final “M-shaped” profile. A combined approach using thermal-fluid simulation and high-speed imaging experiments was employed to track the surface changes throughout the heating and cooling cycles. The results show that while the surface bulges outward during laser irradiation, the material redistributes after the laser is switched off due to non-uniform cooling and volumetric shrinkage. The specific roles of viscosity and surface tension in driving this reverse flow were identified. Furthermore, the study established a quantitative model linking laser parameters to the final surface dimensions, providing a reliable tool for predicting and controlling the precision of glass forming. Full article

(This article belongs to the Section Advanced Manufacturing)

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30 pages, 5810 KB

Open AccessArticle

Scalable Dual-Servo Pectoral Fin Platform for Biomimetic Robotic Fish: Hydrodynamic Experiments and Quasi-Steady CFD

by Chaohui Zhang, Zhanlin Bai, Zhenghe Liu, Jinbo Kuang, Pei Li, Qifang Yan, Gaochao Zhao and Elena Atroshchenko

Machines 2026, 14(1), 121; https://doi.org/10.3390/machines14010121 - 21 Jan 2026

Viewed by 136

Abstract

Biomimetic pectoral fin propulsion offers a low-noise, highly maneuverable alternative to conventional propellers for next-generation underwater robotic systems. This study develops a manta ray-inspired dual-servo pectoral fin module with a CPG-based controller and employs it as a single-fin test article in a recirculating [...] Read more.

Biomimetic pectoral fin propulsion offers a low-noise, highly maneuverable alternative to conventional propellers for next-generation underwater robotic systems. This study develops a manta ray-inspired dual-servo pectoral fin module with a CPG-based controller and employs it as a single-fin test article in a recirculating water tunnel to quantify its hydrodynamic performance. Controlled experiments demonstrate that the fin generates stable thrust over a range of flapping amplitudes, with mean thrust increasing markedly as the amplitude rises, while also revealing an optimal frequency band in which thrust and thrust work are maximized and beyond which efficiency saturates. To interpret these trends, a quasi-steady CFD analysis using the k–ω SST turbulence model is conducted for a series of static angles of attack representative of the instantaneous effective angles experienced during flapping. The simulations show a transition from attached flow with favorable lift-to-drag ratios at moderate angles of attack to massive separation, deep stall, and high drag at extreme angles, corresponding to high-amplitude fin motion. By linking the experimentally observed thrust saturation to the onset of deep stall in the numerical flow fields, this work establishes a unified experimental–numerical framework that clarifies the hydrodynamic limits of pectoral fin propulsion and provides guidance for the design and operation of low-noise, highly maneuverable biomimetic underwater robots. Full article

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

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18 pages, 4272 KB

Open AccessArticle

Multibody Dynamic Analysis of an E-Scooter Considering Asymmetric Tire Stiffness, Speed, and Surface Roughness

by Eduardo Xavier Vaca Michilena and Juan David Cano-Moreno

Machines 2026, 14(1), 120; https://doi.org/10.3390/machines14010120 - 20 Jan 2026

Viewed by 280

Abstract

E-scooters have become a widely adopted form of urban mobility, increasing the need to understand how vibration exposure affects comfort and safety. While most studies have examined the effects of speed, pavement roughness, and overall tire stiffness, none have evaluated how differing stiffness [...] Read more.

E-scooters have become a widely adopted form of urban mobility, increasing the need to understand how vibration exposure affects comfort and safety. While most studies have examined the effects of speed, pavement roughness, and overall tire stiffness, none have evaluated how differing stiffness curves between the front and rear wheels influence rider comfort. This article uses real stiffness curves for rigid and inflatable tires at various pressures (30 psi, 60 psi, and rigid) to assess how front–rear stiffness asymmetry affects vibration transmission across speeds (10–20–30 km/h) and two roughness levels (low and high). The analysis, following the standard UNE-ISO 2631-1:2008 and supported by a multiple-regression model (adjusted R² = 93.84%, homoscedastic residuals), shows that speed and roughness dominate the comfort response (98.9%), while tire stiffness offers a secondary (1.1%) but useful tuning parameter, inducing comfort index variations exceeding 14% between front–rear pressure combinations under typical urban conditions (~20 km/h, low roughness). In this case, the most favorable configuration corresponds to inflatable tires with slightly higher front pressure (+2.9–4.35 psi), whereas solid tires consistently yield the poorest comfort. These findings underscore the role of front–rear stiffness management in improving ride quality and provide practical guidance for optimal inflation strategies in urban e-scooters. Full article

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

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15 pages, 2951 KB

Open AccessArticle

Thermal Management of High-Power Electric Machines (>100 kW) Using Oil Spray Cooling

by Kunal Sandip Garud and Moo-Yeon Lee

Machines 2026, 14(1), 119; https://doi.org/10.3390/machines14010119 - 20 Jan 2026

Viewed by 146

Abstract

In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, [...] Read more.

In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, nozzle numbers, heights of nozzle combinations, and oil flow rates. In addition, an artificial neural network (ANN) model based on two algorithms is developed for predicting thermal performance under various operating conditions. The flat jet nozzle shows the lowest maximum winding temperature of 120.3 °C and a superior heat transfer coefficient of 3028.6 W/m²-K compared to both full cone nozzles. The power consumption for the flat jet nozzle is higher at 123.9 W compared to other nozzle types. The combination of four flat jet nozzles shows improved oil spray distribution and enhanced cooling compared to combinations of two and six flat jet nozzles. Further, the thermal performance of oil spray cooling with four flat jet nozzles improves when height and oil flow rate are increased. Oil spray cooling with the best configuration shows a winding temperature, heat transfer coefficient, and injection pressure of 98.9 °C, 3408.6 W/m²-K and 4.86 bar, respectively, at a flow rate of 20 LPM. The proposed neural network model with a Levenberg–Marquardt (LM) training variant and logarithmic–sigmoidal (Log) transfer function shows the lowest prediction error within ±2%. Full article

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

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53 pages, 3615 KB

Open AccessReview

Progress in Aero-Engine Fault Signal Recognition and Intelligent Diagnosis

by Shunming Li, Wenbei Shi, Jiantao Lu, Haibo Zhang, Yanfeng Wang, Peng Zhang, Mengqi Feng and Yan Wang

Machines 2026, 14(1), 118; https://doi.org/10.3390/machines14010118 - 19 Jan 2026

Viewed by 190

Abstract

Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and [...] Read more.

Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and emphasis on the critical importance of aero-engine fault signal recognition and diagnosis, this paper comprehensively reviews and discusses the classification and evolution of aero-engine fault signal recognition techniques. The review traces this evolution along its developmental trajectory, from classical methods to emerging approaches such as quantum signal processing for weak feature extraction. It also examines characteristics of different types of aviation engine failures and the progression of diagnostic research over time. This review provides multiple tables to compare the applicability, advantages, and limitations of various signal recognition methods and deep learning diagnostic architectures. Detailed discussions synthesize the relative merits of different approaches and their selection trade-offs. Based on this overview, the paper outlines the complexity of real aero-engine faults and key research directions. Building on these developments in fault signal recognition and diagnosis, the paper addresses the complexity and the research areas receiving particular attention within real aero-engine faults. It highlights key research areas, including handling data imbalance, adapting to variable and cross-domain conditions, and advancing diagnostic and data enhancement methods for weak composite faults. Finally, the paper analyzes the multifaceted challenges in the field and identifies future trends in aero-engine fault signal recognition and intelligent diagnosis. Full article

(This article belongs to the Special Issue Recent Advances in Machinery Condition Monitoring and Fault Diagnosis: From Typical Algorithms to the Era of AI Blooming)

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18 pages, 14158 KB

Open AccessArticle

Vision-Based Perception and Execution Decision-Making for Fruit Picking Robots Using Generative AI Models

by Yunhe Zhou, Chunjiang Yu, Jiaming Zhang, Yuanhang Liu, Jiangming Kan, Xiangjun Zou, Kang Zhang, Hanyan Liang, Sheng Zhang and Fengyun Wu

Machines 2026, 14(1), 117; https://doi.org/10.3390/machines14010117 - 19 Jan 2026

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Abstract

At present, fruit picking mainly relies on manual operation. Taking the litchi (litchi chinensis Sonn.)-picking robot as an example, visual perception is often affected by illumination variations, low recognition accuracy, complex maturity judgment, and occlusion, which lead to inaccurate fruit localization. This study [...] Read more.

At present, fruit picking mainly relies on manual operation. Taking the litchi (litchi chinensis Sonn.)-picking robot as an example, visual perception is often affected by illumination variations, low recognition accuracy, complex maturity judgment, and occlusion, which lead to inaccurate fruit localization. This study aims to establish an embodied perception mechanism based on “perception-reasoning-execution” to enhance the visual perception and decision-making capability of the robot in complex orchard environments. First, a Y-LitchiC instance segmentation method is proposed to achieve high-precision segmentation of litchi clusters. Second, a generative artificial intelligence model is introduced to intelligently assess fruit maturity and occlusion, providing auxiliary support for automatic picking. Based on the auxiliary judgments provided by the generative AI model, two types of dynamic harvesting decisions are formulated for subsequent operations. For unoccluded main fruit-bearing branches, a skeleton thinning algorithm is applied within the segmented region to extract the skeleton line, and the midpoint of the skeleton is used to perform the first type of localization and harvesting decision. In contrast, for main fruit-bearing branches occluded by leaves, threshold-based segmentation combined with maximum connected component extraction is employed to obtain the target region, followed by skeleton thinning, thereby completing the second type of dynamic picking decision. Experimental results show that the Y-LitchiC model improves the mean average precision (mAP) by 1.6% compared with the YOLOv11s-seg model, achieving higher accuracy in litchi cluster segmentation and recognition. The generative artificial intelligence model provides higher-level reasoning and decision-making capabilities for automatic picking. Overall, the proposed embodied perception mechanism and dynamic picking strategies effectively enhance the autonomous perception and decision-making of the picking robot in complex orchard environments, providing a reliable theoretical basis and technical support for accurate fruit localization and precision picking. Full article

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

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18 pages, 3397 KB

Open AccessArticle

Privacy-Preserving State Estimation with Application to Target Tracking

by Yulong Wang, Yuman Li, Sen Wang and Hong Lin

Machines 2026, 14(1), 116; https://doi.org/10.3390/machines14010116 - 19 Jan 2026

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Abstract

This paper studies the design of a privacy-preserving optimal state estimator for discrete-time linear systems.Insome traditional methods, such as noise injection, privacy is protected by adding noise to observations and the resulting data is deceptive information. The features of the proposed privacy protection [...] Read more.

This paper studies the design of a privacy-preserving optimal state estimator for discrete-time linear systems.Insome traditional methods, such as noise injection, privacy is protected by adding noise to observations and the resulting data is deceptive information. The features of the proposed privacy protection in this paper are twofold. (i) Privacy is protected without providing deceptive information, that is, the information of the resulting protected observations is authentic. The privacy protection consists of two steps. First, the direction deviation of the observations, rather than the raw observation, is computed. Then, this deviation is random and is not always transmitted to the estimator. (ii) An optimal estimator is designed with desired privacy-preserving degree. By tuning a privacy-protection parameter, a given privacy-preserving degree and an estimation accuracy upper bound can be achieved simultaneously. Finally, drone-tracking experiments are provided to demonstrate the effectiveness of the proposed method, and some comparisons with the existing methods are presented. Full article

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

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28 pages, 3530 KB

Open AccessArticle

A Reinforcement Learning-Based Crushing Method for Robots Operating Within Smart Fully Mechanized Mining Faces

by Yuan Wang, Jun Liu, Zhiyuan Wang and Zhengxiong Lu

Machines 2026, 14(1), 115; https://doi.org/10.3390/machines14010115 - 19 Jan 2026

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Abstract

The current method of manually handling or using open-loop automation to deal with abnormal coal lumps on the scraper conveyor is inefficient due to constraints, such as safety concerns and equipment wear. To address inefficiencies in the handling of abnormal coal blocks on [...] Read more.

The current method of manually handling or using open-loop automation to deal with abnormal coal lumps on the scraper conveyor is inefficient due to constraints, such as safety concerns and equipment wear. To address inefficiencies in the handling of abnormal coal blocks on scraper conveyors, a reinforcement-learning-based method is proposed. Aiming to address the issue that experimenting on abnormal coal handling by scraper conveyors is expensive, this paper designs a variational Auto-Encoder model with the U-MLP network as its core to simulate the processing environment. In addition, given the sparse characteristics of coal block point cloud data, a deep reinforcement learning model based on the LKDG model is designed to control the crushing equipment when dealing with abnormal coal blocks. Through the point cloud data, images, and other information collected by the fully mechanized mining laboratory before and after abnormal processing of coal blocks, we built a simulation environment for abnormal coal blocks, and trained the LKDG model in the simulation environment. To validate the proposed model, we compared LKDG with baseline models in simulation experiments. The results demonstrate that this method can effectively enhance the efficiency of abnormal coal lump processing without human intervention: LKDG achieved a 10.92% higher average reward compared to existing approaches. In terms of engineering applicability, the trained LKDG delivered excellent performance in laboratory tests conducted in a fully mechanized mining environment, increasing the effective crushing count by 67.11% over conventional automated processing methods. Full article

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

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18 pages, 935 KB

Open AccessArticle

A Lightweight Audio Spectrogram Transformer for Robust Pump Anomaly Detection

by Hangyu Zhang and Yi-Horng Lai

Machines 2026, 14(1), 114; https://doi.org/10.3390/machines14010114 - 19 Jan 2026

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Abstract

Industrial pumps are critical components in manufacturing and process plants, where early acoustic anomaly detection is essential for preventing unplanned downtime and reducing maintenance costs. In practice, however, strong background noise, severe class imbalance between rare faults and abundant normal data, and the [...] Read more.

Industrial pumps are critical components in manufacturing and process plants, where early acoustic anomaly detection is essential for preventing unplanned downtime and reducing maintenance costs. In practice, however, strong background noise, severe class imbalance between rare faults and abundant normal data, and the limited computing resources of edge devices make reliable deployment challenging. In this work, a lightweight Audio Spectrogram Transformer (Tiny-AST) is proposed for robust pump anomaly detection under imbalanced supervision. Building on the Audio Spectrogram Transformer, the internal Transformer encoder is redesigned by jointly reducing the embedding dimension, depth, and number of attention heads, and combined with a class frequency-based balanced sampling strategy and time–frequency masking augmentation. Experiments on the pump subset of the MIMII dataset across three SNR levels (−6 dB, 0 dB, 6 dB) demonstrate that Tiny-AST achieves an effective trade-off between computational efficiency and noise robustness. With 1.01 M parameters and 1.68 GFLOPs, it maintains superior performance under heavy noise (−6 dB) compared to ultra-lightweight CNNs (MobileNetV3) and offers significantly lower computational cost than standard compact baselines (ResNet18, EfficientNet-B0). Furthermore, comparisons highlight the performance gains of this lightweight supervised approach over traditional unsupervised benchmarks (e.g., autoencoders, GANs) by effectively leveraging scarce fault samples. These results indicate that a carefully designed lightweight Transformer, together with appropriate sampling and augmentation, can provide competitive acoustic anomaly detection performance while remaining suitable for deployment on resource-constrained industrial edge devices. Full article

(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)

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13 pages, 2304 KB

Open AccessArticle

Hybrid Multi-Scale CNN and Transformer Model for Motor Fault Detection

by Prashant Kumar

Machines 2026, 14(1), 113; https://doi.org/10.3390/machines14010113 - 19 Jan 2026

Viewed by 232

Abstract

Electric motors are the workhorse of industries owing to their precise speed and torque control technologies. Despite their ruggedness, faults are inevitable due to wear and tear, their prolonged usage and multiple factors. Bearing faults are among the most frequently occurring faults in [...] Read more.

Electric motors are the workhorse of industries owing to their precise speed and torque control technologies. Despite their ruggedness, faults are inevitable due to wear and tear, their prolonged usage and multiple factors. Bearing faults are among the most frequently occurring faults in electric motors. Detecting faults at an early stage is crucial for avoiding complete shutdown. Deep learning has gained significant attention in the fault detection domain owing to its inherent advantages. This paper proposes a hybrid multi-scale convolutional neural network and Transformer model for bearing fault detection. The model combines the strengths of multi-scale convolutional front-ends for fine-grained feature extraction with Transformer encoder blocks for capturing long-range temporal dependencies. This approach combines the advantages of both models for effective bearing fault detection. The proposed method was tested on a bearing dataset to show its performance and efficacy. This method achieved high-performance accuracy in bearing fault detection. Full article

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25 pages, 20803 KB

Open AccessArticle

Hierarchical Path Planning for Automatic Parking in Constrained Scenarios via Entry-Point Guidance

by Liang Chen, Lizhi Huang, Chaoyi Chen, Guangwei Wang, Yougang Bian, Mengchi Cai, Qingwen Meng, Qing Xu, Jianqiang Wang and Keqiang Li

Machines 2026, 14(1), 112; https://doi.org/10.3390/machines14010112 - 18 Jan 2026

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Abstract

Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search [...] Read more.

Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search with hybrid A* and reeds-shepp curve to address the above limitations. By rapidly identifying the optimal initial parking pose, the proposed method ensures the kinematic feasibility and smoothness of the resulting trajectories. To further improve efficiency and safety in tight spaces, a hybrid collision detection mechanism is developed by combining a rectangular envelope with multi-circle fitting. The hierarchical geometric modeling approach significantly reduces computational cost while maintaining high detection accuracy. The method is validated through both simulations and real-vehicle experiments in vertical and parallel parking scenarios. Results demonstrate that in typical constrained scenarios, the average planning time is only 0.543 s, and the number of direction changes is maintained between 1 and 6, demonstrating superior computational efficiency and improved trajectory smoothness. These attributes make the algorithm highly suitable for practical deployment in advanced driver assistance systems and autonomous vehicles. Full article

(This article belongs to the Special Issue Optimization-Based Motion Planning & Control for Autonomous Driving in Dynamic Environments)

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19 pages, 2794 KB

Open AccessArticle

Second-Order Nonsingular Terminal Sliding Mode Control for Tracking and Stabilization of Cart–Inverted Pendulum

by Hiep Dai Le and Tamara Nestorović

Machines 2026, 14(1), 111; https://doi.org/10.3390/machines14010111 - 18 Jan 2026

Viewed by 140

Abstract

A second-order nonsingular terminal sliding mode control (SONTSMC) is proposed to solve the stabilization and tracking problems of an inverted pendulum. Although, a first-order sliding mode controller with the integral of the cart position can eliminate the offset in the cart position caused [...] Read more.

A second-order nonsingular terminal sliding mode control (SONTSMC) is proposed to solve the stabilization and tracking problems of an inverted pendulum. Although, a first-order sliding mode controller with the integral of the cart position can eliminate the offset in the cart position caused by incorrect calibration of the pendulum angle while balancing the pendulum at the upright equilibrium position, its control precision and chattering reduction can be improved by using a higher-order sliding mode controller. Therefore, the SONTSMC is designed by combining nonsingular sliding mode control and first-order sliding mode control to construct a second-order sliding mode controller that enhances tracking accuracy and reduces the chattering problems associated with sliding mode control. The performance of the proposed control is compared with that of the linear quadratic regulator sliding mode control (LQRSMC) and the integral linear quadratic regulator sliding mode control (ILQRSMC) for CIP’s stabilization and tracking. The results indicate that SONTSMC significantly increases the control performance of CIP while efficiently utilizing control energy. Full article

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

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14 pages, 2202 KB

Open AccessArticle

Brushless Wound-Field Synchronous Machine Topology with Excellent Rotor Flux Regulation Freedom

by Muhammad Ayub, Arsalan Arif, Atiq Ur Rehman, Azka Nadeem, Ghulam Jawad Sirewal, Mohamed A. Abido and Mudassir Raza Siddiqi

Machines 2026, 14(1), 110; https://doi.org/10.3390/machines14010110 - 17 Jan 2026

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Abstract

This paper presents a nine-switch inverter for brushless operation of wound-field synchronous machines with excellent rotor flux regulation freedom. The manufacturing cost of permanent magnet machines is high due to the instability of rare-earth magnet prices in the global market. Moreover, conventional wound-field [...] Read more.

This paper presents a nine-switch inverter for brushless operation of wound-field synchronous machines with excellent rotor flux regulation freedom. The manufacturing cost of permanent magnet machines is high due to the instability of rare-earth magnet prices in the global market. Moreover, conventional wound-field synchronous machines (WFSMs) have problems with their rotor brushes and slip-ring assembly, wherein the assembly starts to malfunction in the long run. Furthermore, recently, some brushless WFSM topologies have been investigated to eliminate the problems associated with rotor brushes and slip rings, but they have either a high cost due to a double-inverter, or low flux regulation freedom due to a single inverter (−i_d). The proposed nine-switch topology achieves a low cost by using a single inverter with nine switches and excellent flux control through three variables (−i_d, i_q, and i_f), making it highly suitable for wide-speed applications. In the proposed topology, the machine’s armature winding is divided into two sets of coils: ABC and XYZ. A 12-slot and 8-pole machine stator is wound with armature winding coils ABC and XYZ, creating six terminals for injecting currents and two neutrals from each ABC and XYZ coil set. The current to the ABC and XYZ coils is supplied by a nine-switch inverter. The inverter is specially designed to supply rated currents to the ABC winding coils and half of the rated current to the XYZ winding coils. The number of turns of the ABC and XYZ winding coils are kept the same so they produce the same winding function. However, the current in the XYZ winding coils is half compared to that of the ABC winding coils, which creates an asymmetrical airgap magnetomotive force (MMF). The asymmetrical airgap MMF contains two working harmonics, i.e., fundamental MMF for torque production and an additional sub-harmonic MMF component for rotor field brushless excitation. The rotor field is controlled by the difference in current of the two armature winding coils: ABC and XYZ. The proposed topology is validated through theoretical analysis and finite element simulations of electromagnetic and flux regulation. A 2D finite-element analysis is performed to verify the idea. The proposed topology is capable of establishing a 9.15 A dc current in the rotor field winding coil, which consequently generates a torque of 7.8 N·m with a 20.30% torque ripple. Rotor field flux regulation was analyzed from the stator ABC and XYZ coils current ratio ζ. The ratio ζ is analyzed as 2 to 1.3; subsequently, the inducted field currents were 9.15 A dc to 4.8 A dc, respectively. Full article

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

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15 pages, 4459 KB

Open AccessArticle

Automated Custom Sunglasses Frame Design Using Artificial Intelligence and Computational Design

by Prodromos Minaoglou, Anastasios Tzotzis, Klodian Dhoska and Panagiotis Kyratsis

Machines 2026, 14(1), 109; https://doi.org/10.3390/machines14010109 - 17 Jan 2026

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Abstract

Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. [...] Read more.

Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. This limitation highlights the necessity of fully personalized design methodologies based on individual anthropometric characteristics. This paper presents a novel application that automates the design of custom-fit sunglasses through the integration of Artificial Intelligence (AI) and Computational Design. The system is implemented using both textual (Python™ version 3.10.11) and visual (Grasshopper 3D™ version 1.0.0007) programming environments. The proposed workflow consists of the following four main stages: (a) acquisition of user facial images, (b) AI-based detection of facial landmarks, (c) three-dimensional reconstruction of facial features via an optimization process, and (d) generation of a personalized sunglass frame, exported as a three-dimensional model. The application demonstrates a robust performance across a diverse set of test images, consistently generating geometries that conformed closely to each user’s facial morphology. The accurate recognition of facial features enables the successful generation of customized sunglass frame designs. The system is further validated through the fabrication of a physical prototype using additive manufacturing, which confirms both the manufacturability and the fit of the final design. Overall, the results indicate that the combined use of AI-driven feature extraction and parametric Computational Design constitutes a powerful framework for the automated development of personalized wearable products. Full article

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

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20 pages, 2489 KB

Open AccessArticle

Modelling, Optimisation, and Construction of a High-Temperature Superconducting Maglev Demonstrator

by Chenxuan Zhang, Qian Dong, Hongye Zhang and Markus Mueller

Machines 2026, 14(1), 108; https://doi.org/10.3390/machines14010108 - 16 Jan 2026

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Abstract

To achieve global carbon-neutrality goals, magnetic levitation (maglev) technologies offer a promising pathway toward sustainable, energy-efficient transportation systems. In this study, a comprehensive methodology was developed to analyse and optimise the levitation performance of high-temperature superconducting (HTS) maglev systems. Several permanent magnet guideway [...] Read more.

To achieve global carbon-neutrality goals, magnetic levitation (maglev) technologies offer a promising pathway toward sustainable, energy-efficient transportation systems. In this study, a comprehensive methodology was developed to analyse and optimise the levitation performance of high-temperature superconducting (HTS) maglev systems. Several permanent magnet guideway (PMG) configurations were compared, and an optimised PMG Halbach array design was identified that enhances flux concentration and significantly improves levitation performance. To accurately model the electromagnetic interaction between the HTS bulk and the external magnetic field, finite element models based on the H-formulation were established in both two dimensions (2D) and three dimensions (3D). An HTS maglev demonstrator was built using YBCO bulks, and an experimental platform was constructed to measure levitation force. While the 2D model offers fast computation, it shows deviations from the measurements due to geometric simplifications, whereas the 3D model predicts levitation forces for the cylindrical bulk with much higher accuracy, with errors remaining below 10%. The strong agreement between experimental measurements and the 3D simulation across the entire force–height cycle confirms that the proposed model reliably reproduces the electromagnetic coupling and resulting levitation forces in HTS maglev systems. The paper provides a practical and systematic reference for the optimal design and experimental validation of HTS bulk-based maglev systems. Full article

(This article belongs to the Section Vehicle Engineering)

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36 pages, 10413 KB

Open AccessArticle

An Open-Source CAD Framework Based on Point-Cloud Modeling and Script-Based Rendering: Development and Application

by Angkush Kumar Ghosh

Machines 2026, 14(1), 107; https://doi.org/10.3390/machines14010107 - 16 Jan 2026

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Abstract

Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables [...] Read more.

Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables concept-to-model transformation through 2D point-based representations. Unlike previous ad hoc methods, this framework systematically integrates an interactive point-cloud modeling layer with modular systems for curve construction, point generation, transformation, sequencing, and formatting, together with script-based rendering functions. This framework allows users to generate geometrically valid models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based workflows. Structured case studies demonstrate the underlying workflow across mechanical, artistic, and handcrafted forms, contributing empirical evidence of its applicability to diverse tasks ranging from mechanical component modeling to cultural heritage digitization and reverse engineering. Comparative analysis demonstrates that the framework reduces user-facing code volume by over 97% compared to traditional scripting and provides a lightweight, noise-free alternative to traditional hardware-based reverse engineering by allowing users to define clean geometry from the outset. The findings confirm that the framework generates fabrication-ready outputs—including volumetric models and vector representations—suitable for various manufacturing contexts. All systems and rendering functions are made publicly available, enabling the entire pipeline to be performed using free tools. By establishing a practical and reproducible basis for point-based modeling, this study contributes to the advancement of computational design practice and supports the wider adoption of script-based design workflows. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology, 3rd Edition)

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29 pages, 3377 KB

Open AccessReview

Application of Magnetorheological Damper in Aircraft Landing Gear: A Systematic Review

by Quoc-Viet Luong

Machines 2026, 14(1), 106; https://doi.org/10.3390/machines14010106 - 16 Jan 2026

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Abstract

During takeoff and landing, aircraft operate in a variety of situations, posing significant challenges to landing gear systems. Passive hydraulic–pneumatic dampers are commonly used in conventional landing gear to absorb impact energy and reduce vibration. However, due to their fixed damping characteristics and [...] Read more.

During takeoff and landing, aircraft operate in a variety of situations, posing significant challenges to landing gear systems. Passive hydraulic–pneumatic dampers are commonly used in conventional landing gear to absorb impact energy and reduce vibration. However, due to their fixed damping characteristics and inability to adjust to changing operating conditions, these passive systems have several limitations. Recent research has focused on creating intelligent landing gear systems with magnetic dampers (MR) to overcome these limitations. By changing the magnetic field acting on the MR fluid, MR dampers provide semi-active control of the landing gear dynamics and adjust the damping force in real time. This flexibility reduces structural load during landing, increases riding comfort, and improves energy absorption efficiency. This study examines the current state of MR damper application for aircraft landing gear. The review categorizes current control techniques and highlights the structural integration of MR dampers in landing gear assemblies. Purpose: The magnetorheological (MR) damper has become a promising semiactive system to replace the conventional passive damper in aircraft landing gear. However, the mechanical structure and control strategy of the MR damper must be designed to be suitable for aircraft landing gear applications. Methods: Researchers have explored the potential structure designed, the mathematical model of the MR landing gear system, and the control algorithm that was developed for aircraft landing gear applications. Results: According to the mathematical model of the MR damper, three types of models, which are pseudo-static models, parametric models, and unparameterized models, are detailed with their application. Based on these mathematical models, many control algorithms were studied, from classical control, such as PID and skyhook control, to modern control, such as intelligent control and SMC control. Full article

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

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38 pages, 876 KB

Open AccessArticle

A Resilient and Time-Efficient Approach to Product Development Through Availability-Based Design (ABD)

by Pierre Dupont, Hugo Dantinne, Lucas Equeter, Edouard Rivière-Lorphèvre and Pierre Dehombreux

Machines 2026, 14(1), 105; https://doi.org/10.3390/machines14010105 - 16 Jan 2026

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Abstract

The conventional design process (CDP) considers availability issues at the latest stages of the overall machine design project. Designers’ contributions are focused on technical and quality aspects. In most instances, other teams within the supply chain address delivery issues separately. Yet, current machine [...] Read more.

The conventional design process (CDP) considers availability issues at the latest stages of the overall machine design project. Designers’ contributions are focused on technical and quality aspects. In most instances, other teams within the supply chain address delivery issues separately. Yet, current machine design projects are severely bound by deadlines, volatile, and sometimes uncertain. Due to the iterative nature of the design process itself, the number of potential design combinations is large. Their inherent technical checks and evaluations are highly time-consuming. In this paper, to avoid unnecessary design effort, the availability of components is considered at the early stages of the design process. This paper presents the Availability Based Design (ABD), which reorders the design process steps to preclude achieving a design that would be incompatible with the delivery time constraints. A ball screw drive actuator is used as a reference case study to quantitatively compare the performance of ABD to the CDP. The influence of key parameters is studied, including the availability ratio, the automation of key steps of the design process, the number of families of components and the number of technical checks necessary for validating a design. The performance assessment shows that ABD reduces the design time for availability ratios below 0.8 in manual design, and that automating the method makes ABD systematically faster than the CDP. Full article

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

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