Machines

11 pages, 1725 KB

Open AccessArticle

Tool Wear Detection in Milling Using Convolutional Neural Networks and Audible Sound Signals

by Halil Ibrahim Turan and Ali Mamedov

Machines 2026, 14(1), 59; https://doi.org/10.3390/machines14010059 (registering DOI) - 2 Jan 2026

Timely tool wear detection has been an important target for the metal cutting industry for decades because of its significance for part quality and production cost control. With the shift toward intelligent and sustainable manufacturing, reliable tool-condition monitoring has become even more critical. [...] Read more.

Timely tool wear detection has been an important target for the metal cutting industry for decades because of its significance for part quality and production cost control. With the shift toward intelligent and sustainable manufacturing, reliable tool-condition monitoring has become even more critical. One of the main challenges in sound-based tool wear monitoring is the presence of noise interference, instability and the highly volatile nature of machining acoustics, which complicates the extraction of meaningful features. In this study, a Convolutional Neural Network (CNN) model is proposed to classify tool wear conditions in milling operations using acoustic signals. Sound recordings were collected from tools at different wear stages under two cutting speeds, and Mel-Frequency Cepstral Coefficients (MFCCs) were extracted to obtain a compact representation of the short-term power spectrum. These MFCC matrices enabled the CNN to learn discriminative spectral patterns associated with wear. To evaluate model stability and reduce the effects of algorithmic randomness, training was repeated three times for each cutting speed. For the 520 rpm dataset, the model achieved an average validation accuracy of 96.85 ± 2.07%, while for the 635 rpm dataset it achieved 93.69 ± 2.07%. The results demonstrate the feasibility of using acoustic signals, despite inherent noise challenges, as a complementary approach for identifying suitable tool replacement intervals in milling. Full article

(This article belongs to the Special Issue Intelligent Tool Wear Monitoring)

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35 pages, 1283 KB

Open AccessArticle

A Belief Rule Base with Fuzzy Reference Value for Wind Power Generation Forecasting

by Jing Wang, Bing Xu, Wei He, Manlin Chen and Meiqi Li

Machines 2026, 14(1), 58; https://doi.org/10.3390/machines14010058 (registering DOI) - 1 Jan 2026

Abstract

Wind power generation forecasting is a key technology for wind power projects. It directly determines the stability of grid integration and the accuracy of power dispatching. The interval belief rule base (IBRB) is an uncertainty modeling method; it can be applied to wind [...] Read more.

Wind power generation forecasting is a key technology for wind power projects. It directly determines the stability of grid integration and the accuracy of power dispatching. The interval belief rule base (IBRB) is an uncertainty modeling method; it can be applied to wind power generation forecasting. On the one hand, IBRB uses fixed interval matching. This method tends to cause boundary jumps when predicting continuously variable parameters, which threatens the stability of the grid integration. On the other hand, IBRB underutilizes the correlation information of adjacent intervals in modeling, and its rule activation mechanism limits expressions of complex generation mechanisms. To address these issues, a method based on belief rule base with fuzzy reference value (BRB-f) for wind power generation forecasting is proposed. Firstly, the method replaces fixed interval matching with fuzzy membership functions to reduce the impact of wind power output fluctuations on the grid. Then, through a multi-rule-weighted fusion mechanism and optimization algorithms, it improves the accuracy of scheduling under complex generation mechanisms. Finally, the effectiveness and accuracy of the model are validated using a wind turbine power generation forecasting dataset. It provides a better method choice to ensure grid integration safety and enhance the scientific basis of power dispatch decisions. Full article

(This article belongs to the Section Electromechanical Energy Conversion Systems)

19 pages, 2577 KB

Open AccessArticle

A Hybrid Large-Kernel CNN and Markov Feature Framework for Remaining Useful Life Prediction

by Yuke Wang, Che Su, Peng Wang, Junquan Zhen and Dong Wang

Machines 2026, 14(1), 57; https://doi.org/10.3390/machines14010057 (registering DOI) - 1 Jan 2026

Abstract

Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems. Although existing deep learning models, such as Long Short-Term Memory (LSTM) networks and [...] Read more.

Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems. Although existing deep learning models, such as Long Short-Term Memory (LSTM) networks and conventional Convolutional Neural Networks (CNNs), have demonstrated effectiveness in modeling equipment degradation from multivariate sensor data, they still face several limitations. Recurrent architectures often suffer from vanishing gradients and struggle to capture long-term dependencies, while CNN-based methods typically rely on small convolutional kernels and deterministic feature extractors, limiting their ability to model long-range dependencies and stochastic degradation transitions. To address these challenges, this study proposes a novel hybrid deep learning framework that integrates large-kernel convolutional feature extraction with Markov transition modeling for RUL prediction. Specifically, the large-kernel CNN captures both local and global degradation patterns, while the Markov feature module encodes probabilistic state transitions to characterize the stochastic evolution of equipment health. Furthermore, a lightweight channel attention mechanism is incorporated to adaptively emphasize degradation-sensitive sensor information, thereby enhancing feature discriminability. Extensive experiments conducted on the NASA C-MAPSS turbofan engine dataset demonstrate that the proposed model consistently outperforms conventional CNN, LSTM, and hybrid baselines in terms of Root Mean Square Error (RMSE) and the NASA scoring metric. The results verify that combining deep convolutional representations with probabilistic transition information significantly enhances prediction accuracy and robustness in industrial RUL estimation tasks. Full article

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

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31 pages, 13850 KB

Open AccessArticle

Flow-Field Modeling and Mixing Mechanisms of the Twin-Shaft Mixers Based on LBM–LES Coupling

by Wentao Zhao, Jianxiong Ye, Lin Li and Gaoan Zheng

Machines 2026, 14(1), 56; https://doi.org/10.3390/machines14010056 (registering DOI) - 1 Jan 2026

Abstract

In modern industrial systems, twin-shaft mixers are key units for efficient mixing and reactions; their performance directly affects product quality, production cycle, and energy consumption across the chemical, pharmaceutical, food, and lithium-battery-slurry sectors. Systematic elucidation of the mixing mechanisms is hindered by strongly [...] Read more.

In modern industrial systems, twin-shaft mixers are key units for efficient mixing and reactions; their performance directly affects product quality, production cycle, and energy consumption across the chemical, pharmaceutical, food, and lithium-battery-slurry sectors. Systematic elucidation of the mixing mechanisms is hindered by strongly three-dimensional, unsteady, and nonlinear flow fields induced by the complex motions of the two shafts. To address these issues, an advanced coupled numerical model combining the lattice Boltzmann method (LBM) and large-eddy simulation (LES) in an integrated LBM–LES framework is developed, incorporating the Smagorinsky subgrid-scale model to capture small-scale turbulent dissipation under high-Reynolds-number conditions with fidelity. The model enables systematic simulations across configurations with varying blade counts, quantitatively revealing how blade count governs flow structures and mixing performance. The results show that blade count is a key design parameter for performance tuning. A four-blade configuration generates moderately strong, well-distributed turbulence and vortical structures in both the main-shaft and side-shaft regions. The generated turbulence and vortical structures, in turn, promote effective global blending and mass transfer while avoiding localized energy over concentration, unnecessary power loss, and overheating risk, thereby achieving an optimal balance among mixing efficiency, energy consumption, and operational stability. These findings provide a solid theoretical basis and a reliable numerical paradigm for the refined design and performance optimization of industrial mixing equipment. Full article

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

12 pages, 4670 KB

Open AccessArticle

Model Predictive Control of Doubly Fed Induction Motors Based on Fuzzy Logic

by Xueyan Wang, Zhijun Ou, Fobao Zhou, Hang Zhao and Yiming Ma

Machines 2026, 14(1), 55; https://doi.org/10.3390/machines14010055 (registering DOI) - 1 Jan 2026

Abstract

Model predictive control (MPC) has become an attractive solution for doubly fed induction motors (DFIMs) due to its fast dynamic response and multi-variable constraint handling capability. However, the performance of conventional MPC relies on the accuracy of the system model. To further enhance [...] Read more.

Model predictive control (MPC) has become an attractive solution for doubly fed induction motors (DFIMs) due to its fast dynamic response and multi-variable constraint handling capability. However, the performance of conventional MPC relies on the accuracy of the system model. To further enhance the control performance and adaptability, this paper proposes a fuzzy logic-based model predictive control (FL-MPC) strategy. The proposed method continuously monitors the current tracking errors and their rates of change, utilizing a fuzzy inference system to dynamically optimize the weight distribution within the predictive model. This enables the controller to autonomously adjust its behavior for optimal performance across a wide range of operating conditions. Both simulation and experimental results demonstrate that, compared to the conventional MPC, the proposed FL-MPC strategy achieves superior dynamic response. Full article

(This article belongs to the Special Issue Diagnosis of Sensor Failure in Induction Motor Drives)

21 pages, 1995 KB

Open AccessArticle

A Self-Supervised Contrastive Learning Framework Based on Multi-Scale Convolution and Informer for Quantitative Identification of Mining Wire Rope Damage

by Chun Zhao, Jie Tian and Hongyao Wang

Machines 2026, 14(1), 54; https://doi.org/10.3390/machines14010054 (registering DOI) - 31 Dec 2025

Abstract

As a critical part of hoisting equipment in mines, the damage of mining wire rope (MWR) directly poses a threat to production safety and economic efficiency within the coal industry. Therefore, developing efficient and reliable methods for MWR damage identification is of great [...] Read more.

As a critical part of hoisting equipment in mines, the damage of mining wire rope (MWR) directly poses a threat to production safety and economic efficiency within the coal industry. Therefore, developing efficient and reliable methods for MWR damage identification is of great significance. However, acquiring substantial labeled damage data in real industrial scenarios is often impractical, and accurate damage identification with limited labeled data remains an urgent challenge. To address this, this paper proposes a self-supervised contrastive learning method based on multi-scale convolution and Informer (MS-Informer) for the quantitative damage identification of MWR. This method first generates positive and negative sample pairs through a combined data augmentation strategy to increase data diversity and enhance feature generalization. Subsequently, multi-scale convolution is combined with Informer to simultaneously capture local details and global features. Finally, limited labeled data are incorporated during the fine-tuning phase to accurately identify MWR damage. The experimental results on multiple MWR damage datasets demonstrate that the proposed method achieves superior identification performance under conditions of limited labeled data. This framework not only enhances the accuracy and reliability of MWR damage detection but also provides crucial guidance for the safe operation and subsequent maintenance of mine hoisting equipment. Full article

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

21 pages, 1935 KB

Open AccessArticle

Robust Flux-Weakening Control Strategy Against Multiple Parameter Variations for Interior Permanent Magnet Synchronous Motors

by Jinqiu Gao, Huichao Li, Shicai Yin, Yao Ming, Gerui Zhang, Chao Gong, Ke Tang and Pengcheng Guo

Machines 2026, 14(1), 53; https://doi.org/10.3390/machines14010053 (registering DOI) - 31 Dec 2025

Abstract

Interior permanent magnet synchronous motors (IPMSMs) are widely adopted in electric vehicles due to their high torque density and efficiency, and they require flux-weakening operation to achieve high-speed performance under certain driving conditions. However, the traditional current vector control (CVC)-based flux-weakening strategies suffer [...] Read more.

Interior permanent magnet synchronous motors (IPMSMs) are widely adopted in electric vehicles due to their high torque density and efficiency, and they require flux-weakening operation to achieve high-speed performance under certain driving conditions. However, the traditional current vector control (CVC)-based flux-weakening strategies suffer from performance degradation when motor parameters, such as inductances and flux linkage, vary with temperature and operating conditions. To address this issue, this paper proposes a robust flux-weakening control strategy against multiple parameter variations. First, three sequential sliding-mode observers (SMOs) that form a sliding-mode observer suite (SMOS), whose stability is analyzed using Lyapunov theory, are designed to estimate the flux linkage, q-axis inductance, and d-axis inductance, respectively. Second, an error-analysis extraction (EAE) is developed to refine the parameter estimation accuracy by analytically solving a set of linear equations derived from observer results. Third, the accurately estimated parameters are applied to the CVC framework to generate adaptive reference currents, achieving robust and stable flux-weakening control performance. Finally, simulation and experiment are conducted to demonstrate that the proposed strategy effectively enhances control performance under multiple parameter variations. Full article

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

25 pages, 10449 KB

Open AccessArticle

A Model-Based Digital Toolbox for Unified Kinematics and Dimensional Synthesis in Parallel Robot Design

by Zhen He, Chengjin Hu, Tengfei Tang, Hanliang Fang, Yibo Jiang, Fufu Yang and Jun Zhang

Machines 2026, 14(1), 52; https://doi.org/10.3390/machines14010052 - 31 Dec 2025

Abstract

A unified digital toolbox is introduced for kinematics analysis and dimension synthesis of parallel robots, addressing challenges in configuration diversity and computational complexity. By integrating hierarchical kinematic modeling with screw theory, the toolbox establishes standardized analytical frameworks for mobility, inverse kinematics and dexterity [...] Read more.

A unified digital toolbox is introduced for kinematics analysis and dimension synthesis of parallel robots, addressing challenges in configuration diversity and computational complexity. By integrating hierarchical kinematic modeling with screw theory, the toolbox establishes standardized analytical frameworks for mobility, inverse kinematics and dexterity evaluation. A modular toolbox architecture—comprising interactive, data, external module, database and functional layers—enables systematic design, workspace estimation and dexterity-driven optimization. A hybrid MATLAB-C++ interface ensures computational efficiency and scalability. The efficacy of the toolbox is demonstrated through a case study on a novel 2UPR-2RPS parallel mechanism, achieving optimized dimensional parameters (k₁ = 0.85, k₂ = 1.3, k₃ = 0.85, k₄ = 1.3) with a mean dexterity index of 0.637 and validated workspace symmetry. Results confirm that the toolbox streamlines the design process, ensures computational accuracy and enables rapid adaptation to new robotic configurations. This work provides a robust foundation for advanced parallel robot design, offering significant potential for industrial and research applications requiring high-precision motion control. Full article

(This article belongs to the Special Issue Intelligent Design and Application of Parallel Robots)

16 pages, 1260 KB

Open AccessArticle

DAR-Swin: Dual-Attention Revamped Swin Transformer for Intelligent Vehicle Perception Under NVH Disturbances

by Xinglong Zhang, Zhiguo Zhang, Huihui Zuo, Chaotan Xue, Zhenjiang Wu, Zhiyu Cheng and Yan Wang

Machines 2026, 14(1), 51; https://doi.org/10.3390/machines14010051 - 31 Dec 2025

Abstract

In recent years, deep learning-based image classification has made significant progress, especially in safety-critical perception fields such as intelligent vehicles. Factors such as vibrations caused by NVH (noise, vibration, and harshness), sensor noise, and road surface roughness pose challenges to robustness and real-time [...] Read more.

In recent years, deep learning-based image classification has made significant progress, especially in safety-critical perception fields such as intelligent vehicles. Factors such as vibrations caused by NVH (noise, vibration, and harshness), sensor noise, and road surface roughness pose challenges to robustness and real-time deployment. The Transformer architecture has become a fundamental component of high-performance models. However, in complex visual environments, shifted window attention mechanisms exhibit inherent limitations: although computationally efficient, local window constraints impede cross-region semantic integration, while deep feature processing obstructs robust representation learning. To address these challenges, we propose DAR-Swin (Dual-Attention Revamped Swin Transformer), enhancing the framework through two complementary attention mechanisms. First, Scalable Self-Attention universally substitutes the standard Window-based Multi-head Self-Attention via sub-quadratic complexity operators. These operators decouple spatial positions from feature associations, enabling position-adaptive receptive fields for comprehensive contextual modeling. Second, Latent Proxy Attention integrated before the classification head adopts a learnable spatial proxy to integrate global semantic information into a fixed-size representation, while preserving relational semantics and achieving linear computational complexity through efficient proxy interactions. Extensive experiments demonstrate significant improvements over Swin Transformer Base, achieving 87.3% top-1 accuracy on CIFAR-100 (+1.5% absolute improvement) and 57.0% mAP on COCO2017 (+1.3% absolute improvement). These characteristics are particularly important for the active and passive safety features of intelligent vehicles. Full article

(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)

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18 pages, 4451 KB

Open AccessArticle

Analysis and Experimental Research on Splice Strength of Aramid Rubber Conveyor Belt

by Xiaoxia Zhao, Hongru Tang, Xuan Yin, Wenjun Meng and Hong Ren

Machines 2026, 14(1), 50; https://doi.org/10.3390/machines14010050 - 31 Dec 2025

Abstract

Aramid fiber is increasingly regarded as an important skeleton material in the conveyor belt industry. However, its application is limited by problems such as short splice service life and low strength retention. This study investigates the finger splice of an aramid rubber conveyor [...] Read more.

Aramid fiber is increasingly regarded as an important skeleton material in the conveyor belt industry. However, its application is limited by problems such as short splice service life and low strength retention. This study investigates the finger splice of an aramid rubber conveyor belt. A finite element model was established to analyze the effects of different rubber hardness values (60, 65, 70, 75), environmental temperatures (−20 to 40 °C), and finger widths (10 mm, 12 mm, 15 mm, 20 mm, 30 mm) on splice mechanical performance. The results show that the maximum stress is concentrated at the end surfaces of the reinforcement layer and the fingertips of the skeleton material. The splice strength increases with higher rubber hardness but decreases with rising environmental temperature. To improve splice strength, the rubber hardness and environmental temperature should be controlled within 60–70 and 0–20 °C, respectively. Although reducing finger width increases splice strength, excessively small widths lead to stress concentration and manufacturing difficulties. Therefore, finger width selection should consider actual working conditions. The experimental results are compared with simulation results, and the trend consistency verifies the correctness of the simulation. This study provides a theoretical basis for parameter selection and structural design of the splice under various working conditions. Full article

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

20 pages, 2106 KB

Open AccessArticle

A Hybrid Fractal-NURBS Model for Characterizing Material-Specific Mechanical Surface Contact

by Leilei Zhang, Yingkun Mu, Kui Luo, Guang Ren and Zisheng Wang

Machines 2026, 14(1), 49; https://doi.org/10.3390/machines14010049 - 30 Dec 2025

Abstract

The reliability of mechanical systems hinges on analyzing the actual surface-to-surface contact area, which critically influences dynamic behavior, friction, material performance, and thermal dissipation. Uneven surfaces lead to incomplete contact, where only a fraction of asperities touch, creating a nominal contact area. This [...] Read more.

The reliability of mechanical systems hinges on analyzing the actual surface-to-surface contact area, which critically influences dynamic behavior, friction, material performance, and thermal dissipation. Uneven surfaces lead to incomplete contact, where only a fraction of asperities touch, creating a nominal contact area. This study proposes a novel fractal contact model for various mechanical behaviors between mechanical contact surfaces, integrating the Weierstrass–Mandelbrot fractal function and nonuniform rational B-spline interpolation (NURBS) to model material-dependent actual contact conditions. Furthermore, this research delved into the changes in thermal conductivity across the surfaces of metal materials within a simulated setting. It maintained a contact ratio ranging from 0.038% to 15.2%, a factor that remained unaffected by contact pressure. Both experimental and simulated findings unveiled an actual contact rate spanning from 0.44% to 1.06%, thereby underscoring the distinctive interface behaviors specific to different materials. The proposed approach provides fresh perspectives for investigating material–contact interactions and tackling associated engineering hurdles. Full article

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

18 pages, 2537 KB

Open AccessArticle

Structural Optimization Design of Rotary Drilling Rig Drill Pipes Based on an Improved Enhanced Knowledge Gain Sharing Algorithm

by Heng Yang, Haorong Yang, Gening Xu and Mingliang Yang

Machines 2026, 14(1), 48; https://doi.org/10.3390/machines14010048 - 30 Dec 2025

Abstract

To address the technical challenge of synergistic optimization between lightweight design and structural performance for mechanical locking drill pipes of rotary drilling rigs, this study takes such drill pipes as the research object. Seven typical operating conditions are classified to construct a multidimensional [...] Read more.

To address the technical challenge of synergistic optimization between lightweight design and structural performance for mechanical locking drill pipes of rotary drilling rigs, this study takes such drill pipes as the research object. Seven typical operating conditions are classified to construct a multidimensional verification model encompassing static strength, stiffness, stability, and fatigue strength, while a lightweight optimization model with multi-performance constraints is established to minimize the cross-sectional area. Aiming at the limitations of the Enhanced Gaining–Sharing Knowledge (eGSK) algorithm in initial population distribution, integer constraint adaptation, and local exploration, an Improved Enhanced Gaining–Sharing Knowledge algorithm (ieGSK) is proposed, integrating hybrid initialization, integer solution processing, and elite local search mechanisms. Comparative tests with Enzyme-Activated Optimization (EAO), State-Based Optimization (SBO), and eGSK algorithms demonstrate that ieGSK converges to engineering-practical integer solutions in 258 iterations (computational efficiency, 4.475 s). Compared with EAO, SBO, and eGSK algorithms, the computational efficiency is improved by 61.6%, 43.1%, and 9.6%, respectively, while achieving a 9.8% weight reduction and maintaining optimal stability and robustness. This verifies the superiority of ieGSK in drill pipe structural optimization, offering technical support for the lightweight design of core components in rotary drilling rigs. Full article

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

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25 pages, 8685 KB

Open AccessArticle

Research on Maize Precision Seeding Control Based on RIME-BP-PID

by Yitian Sun, Haiyang Liu, Yongjia Sun, Xianying Feng and Peng Zhang

Machines 2026, 14(1), 47; https://doi.org/10.3390/machines14010047 - 29 Dec 2025

Abstract

This paper addresses the insufficient speed control accuracy observed in traditional seeding systems. This paper proposes an electric drive seeding control method that incorporates a composite control strategy combining the Rime optimization algorithm (RIME) with a backpropagation neural network (BPNN). Firstly, the architecture [...] Read more.

This paper addresses the insufficient speed control accuracy observed in traditional seeding systems. This paper proposes an electric drive seeding control method that incorporates a composite control strategy combining the Rime optimization algorithm (RIME) with a backpropagation neural network (BPNN). Firstly, the architecture including radar/proximity switch dual-mode speed measurement, STM32F103 main control, and asymmetric half-bridge drive was constructed. Based on the kinematic model, a motor speed-plant spacing mapping relationship was derived to complete the selection of a brushless DC motor. Secondly, this study addresses the issues of large overshoot in traditional PID control, response lag in fuzzy PID, and local optima in BP-PID. To overcome these challenges, the RIME algorithm is employed to optimize the weight-updating mechanism of the backpropagation neural network (BPNN). The soft RIME search facilitates multi-directional exploration, while the hard RIME puncture enhances global optimization capability, significantly improving the adaptive accuracy of the parameters. The simulation results showed that the adjustment time of the proposed RIME-BP-PID in the step response is 73.8% shorter than the BP-PID, and the overshoot is reduced to 0.23%. The square wave tracking error is 27.8% of the traditional PID. The bench test was carried out at 6–12 km/h speed and 200–300 mm. The results showed that, compared with BP-PID, the qualified index of RIME-BP-PID increased by 1.67–1.94 percentage points, the missed seeding index decreased by 1.25–1.80 percentage points, and the coefficient of variation decreased by 4.90–5.82 percentage points. The algorithm effectively solves the problem of the strong nonlinear time-varying control of a seeding system and provides theoretical support for the research and development of precision agricultural equipment. Full article

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

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19 pages, 22037 KB

Open AccessArticle

Improved Fixture Layout for a Floating Brake Disc

by Mîndru Tedor Daniel, Ciofu Ciprian Dumitru, Grigorean Ştefan, Marica Mariana, Ilie Dumitru, Tiberiu Mîrze and Nedelcu Dumitru

Machines 2026, 14(1), 46; https://doi.org/10.3390/machines14010046 - 29 Dec 2025

Abstract

The design and construction of a racing car often involve determining the optimal technological solution and designing the subassemblies, taking into account the required specifications. The case presented in this paper details improvements to the fixture layout of a braking system using a [...] Read more.

The design and construction of a racing car often involve determining the optimal technological solution and designing the subassemblies, taking into account the required specifications. The case presented in this paper details improvements to the fixture layout of a braking system using a floating disc, starting from the use of calipers and brake pads that are already available on the market, and the design, modeling, and manufacture of an optimal brake disc for car requirements. Based on their accumulated experience, the authors identified the cause of vibrations under certain braking conditions, as well as the causes leading to mechanical fatigue of the braking system components. Following the simulations, the design of the floating brake disc was improved and subsequently, a car equipped with this new type of brake disc was tested to analyze the behavior of the braking system. The results showed an improvement in the maneuverability of the car, a slower deterioration of the components of the braking system and a temperature reduction in the components during operation on the circuit. Full article

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

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21 pages, 4493 KB

Open AccessArticle

Comparative Study of Motor Current–and RPM–Based Methods for Roll Force Estimation in Rolling Mill

by Gyuhan Nam, Jinpyo Jeon, Dongyun Lee, Seong-Gi Kim, Sang-Min Byon and Youngseog Lee

Machines 2026, 14(1), 45; https://doi.org/10.3390/machines14010045 - 29 Dec 2025

Abstract

This study proposes an indirect approach that estimates roll force from motor current signals in a rolling mill. Motor current is first converted to motor torque using an induction-motor equivalent-circuit model, then to roll torque via the gear ratio, and finally to roll [...] Read more.

This study proposes an indirect approach that estimates roll force from motor current signals in a rolling mill. Motor current is first converted to motor torque using an induction-motor equivalent-circuit model, then to roll torque via the gear ratio, and finally to roll force through a torque-arm relationship. A laboratory-scale rolling mill was designed and fabricated to experimentally validate the approach. Two torque-conversion schemes were examined: Method A, which determines the slip of the induction motor from measured rpm and recalculated motor parameters, and Method B, which estimates slip from measured motor current and applies a finite element (FE)–based response surface function to calibrate the converted torque. The converted roll torques were validated against FE analysis, and the resulting roll forces were compared with load cell measurements under various rolling conditions. Deviation, defined as the average difference between the FE-predicted torque and the converted torques, ranged from −11.9% to 28.8% for Method A and −7.2% to 13.8% for Method B. Roll force deviations from measurements ranged from −14.1% to 14.9% for Method A and −3.7% to 14.2% for Method B. Method A provided a straightforward and computationally light conversion route but was more sensitive to rpm-measurement noise, whereas Method B yielded smoother temporal behavior at the cost of FE-based calibration. The results demonstrate that both methods can reproduce the overall evolution of roll torque and roll force using only motor-side measurements, offering a practical foundation for real-time monitoring in rolling mills where stand-by-stand load cells are unavailable. Full article

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

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24 pages, 14376 KB

Open AccessArticle

LDFE-SLAM: Light-Aware Deep Front-End for Robust Visual SLAM Under Challenging Illumination

by Cong Liu, You Wang, Weichao Luo and Yanhong Peng

Machines 2026, 14(1), 44; https://doi.org/10.3390/machines14010044 - 29 Dec 2025

Abstract

Visual SLAM systems face significant performance degradation under dynamic lighting conditions, where traditional feature extraction methods suffer from reduced keypoint detection and unstable matching. This paper presents LDFE-SLAM, a novel visual SLAM framework that addresses illumination challenges through a Light-Aware Deep Front-End (LDFE) [...] Read more.

Visual SLAM systems face significant performance degradation under dynamic lighting conditions, where traditional feature extraction methods suffer from reduced keypoint detection and unstable matching. This paper presents LDFE-SLAM, a novel visual SLAM framework that addresses illumination challenges through a Light-Aware Deep Front-End (LDFE) architecture. Our key insight is that low-light degradation in SLAM is fundamentally a geometric feature distribution problem rather than merely a visibility issue. The proposed system integrates three synergistic components: (1) an illumination-adaptive enhancement module based on EnlightenGAN with geometric consistency loss that restores gradient structures for downstream feature extraction, (2) SuperPoint-based deep feature detection that provides illumination-invariant keypoints, and (3) LightGlue attention-based matching that filters enhancement-induced noise while maintaining geometric consistency. Through systematic evaluation of five method configurations (M1–M5), we demonstrate that enhancement, deep features, and learned matching must be co-designed rather than independently optimized. Experiments on EuRoC and TUM sequences under synthetic illumination degradation show that LDFE-SLAM maintains stable localization accuracy (∼1.2 m ATE) across all brightness levels, while baseline methods degrade significantly (up to 3.7 m). Our method operates normally down to severe lighting conditions (30% ambient brightness and 20–50 lux—equivalent to underground parking or night-time streetlight illumination), representing a 4–6× lower illumination threshold compared to ORB-SLAM3 (200–300 lux minimum). Under severe (25% brightness) conditions, our method achieves a 62% tracking success rate, compared to 12% for ORB-SLAM3, with keypoint detection remaining above the critical 100-point threshold, even under extreme degradation. Full article

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

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15 pages, 3197 KB

Open AccessArticle

Modelling of Aerostatic Bearings with Micro-Hole Restriction

by Dehong Huo, Amir Fard, Junliang Liu, Ning Gou and Kai Cheng

Machines 2026, 14(1), 43; https://doi.org/10.3390/machines14010043 - 29 Dec 2025

Abstract

Aerostatic bearings incorporating micro-hole restrictors with diameters on the order of tens of microns demonstrate superior static and dynamic stiffness characteristics, while significantly reducing air consumption, and are increasingly adopted in precision engineering applications. This paper investigates the modelling of aerostatic bearings with [...] Read more.

Aerostatic bearings incorporating micro-hole restrictors with diameters on the order of tens of microns demonstrate superior static and dynamic stiffness characteristics, while significantly reducing air consumption, and are increasingly adopted in precision engineering applications. This paper investigates the modelling of aerostatic bearings with micro-hole restrictors. First, a refined discharge coefficient formula is developed, incorporating the orifice length-to-diameter ratio effect using the computational fluid dynamics (CFD) simulation results on a centrally fed circular aerostatic bearing. A numerical solution scheme is proposed using the developed discharge coefficients to enable more accurate and efficient prediction of the bearing performance and flow characteristics. Finally, the proposed numerical approach is implemented using the finite difference method (FDM) and demonstrated through a circular thrust air bearing case study. The results are validated against both CFD simulations and experimental measurements, showing excellent agreement and confirming the reliability of the FDM-based numerical model. Numerical and experimental investigations consistently demonstrate that micro-hole-restricted air bearings can achieve both high load capacity and high stiffness, having the potential for application in more complex air bearing designs and systems. Full article

(This article belongs to the Section Advanced Manufacturing)

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21 pages, 20516 KB

Open AccessArticle

Sensorless Sector Determination of Brushless DC Motors Using Maximum Likelihood Estimation

by Abdulkerim Ahmet Kaplan, Mehmet Onur Gulbahce and Derya Ahmet Kocabas

Machines 2026, 14(1), 42; https://doi.org/10.3390/machines14010042 - 29 Dec 2025

Abstract

Brushless DC motors are widely used for their high power density and efficiency. However, sensorless control remains challenging due to the difficulty of accurate rotor position detection, especially at low speeds. This paper proposes a novel sensorless trapezoidal control method based on Maximum [...] Read more.

Brushless DC motors are widely used for their high power density and efficiency. However, sensorless control remains challenging due to the difficulty of accurate rotor position detection, especially at low speeds. This paper proposes a novel sensorless trapezoidal control method based on Maximum Likelihood Estimation (MLE) for rotor sector detection. Unlike conventional back-EMF zero-crossing techniques, the proposed method uses a statistical algorithm to generate a probability map from prior motor state data, enabling accurate rotor position estimation without sensors. The MLE method operates with a typical computation time of 50–100

μ

s, offering a balanced tradeoff between speed and accuracy. It is significantly faster than Kalman filter-based approaches (200–1000

μ

s) and comparable to observer-based methods (20–80

μ

s), while being more robust than zero-crossing techniques (<5

μ

s). This makes it a practical and cost-effective solution for applications demanding high efficiency and reliability, such as electric mobility systems. Full article

(This article belongs to the Special Issue Advanced Sensorless Control of Electrical Machines)

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11 pages, 1724 KB

Open AccessArticle

Coupling Dynamic Behavior Analysis of Multiple Vibration Excitation Sources in Heavy-Duty Mining Screen

by Xiaohao Li, Yang Zhou, Mingzheng Bao and Yahui Wang

Machines 2026, 14(1), 41; https://doi.org/10.3390/machines14010041 - 29 Dec 2025

Abstract

A heavy-duty vibrating screen with excitation sources is a mining vibrating machine synchronized by two eccentric rotors, exhibiting typical coupled dynamic behavior. Aiming at the coupling dynamic behavior of dual excitation sources based on the nonlinear vibration of a heavy-duty mining screen, theoretical [...] Read more.

A heavy-duty vibrating screen with excitation sources is a mining vibrating machine synchronized by two eccentric rotors, exhibiting typical coupled dynamic behavior. Aiming at the coupling dynamic behavior of dual excitation sources based on the nonlinear vibration of a heavy-duty mining screen, theoretical research and experimental analysis of coupling synchronization are carried out, and the dynamic reasons for the dual excitation sources to achieve vibration synchronization are discussed. Based on nonlinear vibration theory, electromechanical coupling nonlinear dynamics equations for a dual excitation source vibrating screen are established in this paper, and the coupled dynamics factors of the two eccentric rotors are analyzed. The impact of coupling strength on the equilibrium state of the nonlinear vibration system is discussed, and the evolution process of the synchronous motion of the two eccentric rotors is further investigated, revealing the causal relationship by which the dual excitation sources achieve synchronization due to coupled dynamics behavior. The results show that the coupling effect of the multi-exciter is based on the nonlinear vibration of the vibration system, and the motion characteristics and motion mode of the exciter will change, and, finally, a coupled synchronous motion state will be reached. The research results can provide ideas for the mechanical structure design of heavy-duty mining screens excited by multiple excitation sources and can provide a theoretical basis and application reference for the selection of structural parameters of this kind of mining machinery. Full article

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

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