Machines

21 pages, 4908 KB

Open AccessArticle

Optimization Design of Blade Profile Parameters of Low-Speed and High-Torque Turbodrill Based on GA-LSSVM-MOPSO-TOPSIS Method

by Yulin Gao, Yu Wang, Guosong Chen, Jia Yan, Lingrong Kong and Yuzuo Lu

Machines 2025, 13(11), 1034; https://doi.org/10.3390/machines13111034 (registering DOI) - 7 Nov 2025

Abstract

The exploration and development of deep marine resources are faced with the problems of poor drill ability and serious wellbore instability in high temperature and high-pressure formations. The bottom hole dynamic drilling tool with low vibration characteristics is the best choice for deep [...] Read more.

The exploration and development of deep marine resources are faced with the problems of poor drill ability and serious wellbore instability in high temperature and high-pressure formations. The bottom hole dynamic drilling tool with low vibration characteristics is the best choice for deep well drilling. The output torque of the turbodrill is relatively small, which limits its application potential. In this study, intelligent optimization algorithms are used to improve the blade shape design to improve its output torque. Firstly, based on the moment of momentum theorem, the key blade profile parameters and range affecting the output characteristics of the turbodrill are analyzed and summarized. Subsequently, the five-order polynomial method and UG software (version 10.0) are used to complete the three-dimensional configuration of the bent-twisted blade. Then, based on the GA-LSSVM-MOPSO-TOPSIS intelligent optimization algorithm, the two-dimensional and three-dimensional modeling design parameters under the optimal hydraulic performance are optimized, and the accuracy of the intelligent optimization algorithm and parameters is verified by CFD simulation analysis. The results show that the hydraulic efficiency of only 4.9% is sacrificed, and the output torque is increased by 36.61%, which significantly improves the hydraulic performance of the turbodrill and provides guidance for the design of low-speed and high-torque turbodrills. Full article

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

15 pages, 1317 KB

Open AccessArticle

A Framework for Testing and Evaluation of Automated Valet Parking Using OnSite and Unity3D Platforms

by Ouchan Chen, Lei Chen, Junru Yang, Hao Shi, Lin Xu, Haoran Li, Weike Lu and Guojing Hu

Machines 2025, 13(11), 1033; https://doi.org/10.3390/machines13111033 (registering DOI) - 7 Nov 2025

Abstract

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an [...] Read more.

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an AVP testing and evaluation framework using OnSite (Open Naturalistic Simulation and Testing Environment) and Unity3D platforms. Through scenario construction based on field-collected data and model reconstruction, a testing scenario library is established, complying with industry standards. A simplified kinematic model, balancing simulation accuracy and operational efficiency, is applied to describe vehicle motion. A multidimensional evaluation system is developed with completion rate as a primary index and operation performance as a secondary index, which considers both parking efficiency and accuracy. Over 500 AVP algorithms are tested on the OnSite platform, and the testing results are evaluated through the Unity3D platform. The performance of the top 10 algorithms is analyzed. The evaluation platform is compared with CARLA simulation platform and field vehicle testing. This study finds that the framework provides an effective tool for AVP testing and evaluation; a variety of high-level AVP algorithms are developed, but their flexibility in complex dynamic scenarios has limitations. Future research should focus on exploring more sophisticated learning-based algorithms to enhance AVP adaptability and performance in complex dynamic environment. Full article

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

21 pages, 4523 KB

Open AccessArticle

Analysis of Driver Takeover Performance in Autonomous Vehicles Based on Generalized Estimating Equations

by Min Duan, Lian Xie, Jianrong Cai, Junru Yang and Haoran Li

Machines 2025, 13(11), 1032; https://doi.org/10.3390/machines13111032 (registering DOI) - 7 Nov 2025

Abstract

Current autonomous vehicles require human drivers to take over control during emergencies or in environments the system cannot handle. During other periods, drivers are permitted to engage in non-driving-related tasks. It is essential to investigate how the immersion in non-driving-related tasks affects drivers’ [...] Read more.

Current autonomous vehicles require human drivers to take over control during emergencies or in environments the system cannot handle. During other periods, drivers are permitted to engage in non-driving-related tasks. It is essential to investigate how the immersion in non-driving-related tasks affects drivers’ takeover performance under different scenarios. To address this, a mixed-design simulated driving experiment was conducted with 40 participants, incorporating three non-driving-related tasks (no task, watch video, play game), three takeover request lead times (3 s, 5 s, 7 s), and two obstacle types (dynamic, static). The takeover process was divided into three phases: preparation, obstacle avoidance, and recovery. Analysis of the areas of interest showed that engaging in non-driving-related tasks substantially reduced drivers’ visual attention tothe road ahead during the preparation phase. The Generalized Estimating Equations method was employed to investigate the effects of various factors on takeover performance. Model results showed that scenarios with static obstacles and longer takeover request times led to a significant reduction in mean lane deviation but a significant increase in the standard deviation of lane deviation, suggesting improved lateral control performance. A significant interaction was observed between the watch video task and static obstacles, which corresponded to a notable decrease in the mean vehicle speed during obstacle avoidance. Performance in the recovery phase was strongly predicted by that in the obstacle avoidance phase, indicating that the stability of the avoidance maneuver is a critical determinant of the subsequent recovery. These findings offer valuable insights for managing non-driving-related tasks and setting appropriate takeover request timings in automated driving systems. Full article

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

16 pages, 2896 KB

Open AccessArticle

Application of Various Artificial Neural Network Algorithms for Regression Analysis in the Dynamic Modeling of a Three-Link Planar RPR Robotic Arm

by Onur Denizhan

Machines 2025, 13(11), 1031; https://doi.org/10.3390/machines13111031 (registering DOI) - 7 Nov 2025

Abstract

The design, control, simulation and animation of robotic systems heavily depend on dynamic modeling. A variety of studies have explored different dynamic modeling methodologies applied to diverse robotic mechanisms. Artificial neural networks (ANNs) have proven their value in engineering design in recent years, [...] Read more.

The design, control, simulation and animation of robotic systems heavily depend on dynamic modeling. A variety of studies have explored different dynamic modeling methodologies applied to diverse robotic mechanisms. Artificial neural networks (ANNs) have proven their value in engineering design in recent years, enhancing the understanding of complex mechanisms as well as shortening experimental periods and decreasing related expenses. This study investigates the application of various neural network algorithms for the analysis of a custom-designed three-link planar revolute–prismatic–revolute (RPR) robotic arm mechanism. Initially, the Euler–Lagrange equations of motion for the RPR mechanism are derived. Joint accelerations are then computed under different mass configurations of the robotic links, resulting in a dataset comprising 204 joint acceleration samples. Six distinct neural network models are subsequently employed to perform regression analysis on the collected data. The primary objective of this study is to analyze the relationship between joint accelerations and varying link masses under constant joint torques and forces, while its secondary aim is to present a representative application of neural networks as regression learners for the dynamic modeling of robotic mechanisms. The approach outlined in this study allows users to select appropriate neural network algorithms for use in specific applications, considering the wide range of available algorithms. Link mass variations and their effects on joint accelerations are investigated, establishing a basis for the modeling of robotic dynamics using regression-based neural networks. The results indicate that the optimizable neural network algorithm produces the best regression accuracy results, although the other models maintain similar performance levels. Full article

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

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20 pages, 7190 KB

Open AccessArticle

Prediction of Component Erosion in a Francis Turbine Based on Sediment Particle Size

by Bingning Chen, Yan Jin, Ying Xue, Haojie Liang and Fangping Tang

Machines 2025, 13(11), 1030; https://doi.org/10.3390/machines13111030 (registering DOI) - 7 Nov 2025

Abstract

Erosion caused by sediment-laden flow significantly affects the efficiency and durability of Francis turbines. In this study, the Euler–Lagrange multi-phase flow model was employed to simulate solid-liquid two-phase flow with different sediment particle sizes to analyze erosion characteristics in turbine components. The results [...] Read more.

Erosion caused by sediment-laden flow significantly affects the efficiency and durability of Francis turbines. In this study, the Euler–Lagrange multi-phase flow model was employed to simulate solid-liquid two-phase flow with different sediment particle sizes to analyze erosion characteristics in turbine components. The results show that the maximum erosion rate of the runner blades is positively correlated with particle impact velocity, confirming that impact velocity is the dominant factor influencing local material removal. The total erosion rate of the runner blades, guide vanes, and draft tube corresponds closely with vorticity, indicating that vortex-induced flow separation accelerates particle–wall collisions and intensifies erosion. Both vorticity and erosion exhibit a nonlinear variation with particle size, reaching a minimum at 0.05 mm. These findings establish clear qualitative and quantitative relationships between erosion and key flow parameters, providing theoretical guidance for understanding and mitigating sediment-induced wear in Francis turbines. Full article

(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)

25 pages, 2540 KB

Open AccessArticle

Anisotropic Plasticity in Sheet Metal Forming: Experimental and Numerical Analysis of Springback Using U-Bending Test

by Lotfi Ben Said, Abir Bouhamed, Mondher Wali, Taoufik Kamoun, Muapper Alhadri, Badreddine Ayadi, Sattam Alharbi and Wajdi Rajhi

Machines 2025, 13(11), 1029; https://doi.org/10.3390/machines13111029 - 7 Nov 2025

Abstract

Accurate forecasting of springback continues to pose a significant challenge in sheet metal forming processes. The present paper presents a numerical model designed for the precise prediction of springback, allowing for a deeper understanding of plasticity behavior during cold forming operations in sheet [...] Read more.

Accurate forecasting of springback continues to pose a significant challenge in sheet metal forming processes. The present paper presents a numerical model designed for the precise prediction of springback, allowing for a deeper understanding of plasticity behavior during cold forming operations in sheet metals. The key contribution of this model is the introduction of a non-associated anisotropic constitutive model featuring nonlinear mixed isotropic–kinematic hardening. This model is derived from Hill’48 quadratic function and it was implemented into ABAQUS 6.13 software environment through the user defined UMAT subroutine. For improved precision, kinematic hardening parameters specific to 5083 aluminum sheet metal were meticulously derived from cyclic shear experiments. Our results demonstrate the model’s strong capability in predicting springback during the U-bending operation, achieving remarkable accuracy. The design of experiments DOE is used as a statistical method to optimize the number of experiments and analyze the effects of key input factors. In this study, sheet thickness, punch speed, and sampling angle relative to the rolling direction (RD) are examined at different levels to assess their impact on folding force and springback. The strong agreement between experimental results and theoretical predictions confirms the accuracy and reliability of the proposed models in estimating folding force and springback. Full article

(This article belongs to the Special Issue Advanced Technologies for Sheet Metal Forming)

19 pages, 4979 KB

Open AccessArticle

Pediatric Lower Limb Rehabilitation Training System with Soft Exosuit and Quantitative Partial Body Weight Support

by Dezhi Liang, Shuk-Fan Tong, Hsuan-Yu Lu, Minghao Liu, Zhen Wang, Tian Xing, Hongliu Yu and Raymond Kai-Yu Tong

Machines 2025, 13(11), 1028; https://doi.org/10.3390/machines13111028 - 7 Nov 2025

Abstract

The pediatric period is a crucial window for motor function learning and growth. Individuals with central nervous system injuries like cerebral palsy commonly display severe crouch gait in the lower limbs. Hyperflexion of the knee joints promotes the forward trunk and increases reliance [...] Read more.

The pediatric period is a crucial window for motor function learning and growth. Individuals with central nervous system injuries like cerebral palsy commonly display severe crouch gait in the lower limbs. Hyperflexion of the knee joints promotes the forward trunk and increases reliance on the handle frame of a walker for support. In this study, we developed a quantitative partial body weight training system integrated with a soft pneumatic exosuit to assist the knee extension during the stance phase of the gait cycle. In the preliminary results for five pediatric cerebral palsy subjects, compared to the baseline condition, excessive knee flexion ameliorated with the assistance of the soft pneumatic exosuit. The peak knee extension and range of motion increased by 19.72° (±3.47°) and 15.46° (±5.06°), respectively. With exosuit assistance, the subjects demonstrated improved gait retraining compared to baseline. They were able to bear significantly more body weight on their affected limb, as evidenced by a 33.3% increase in the fraction of body weight measured by the force plate. Additionally, they relied less on the handrail for support during walking. With more extended knee joints to bear the load over gravity, the pediatric subjects transferred the reliance from external support and upper limbs back to the lower limbs as a more independent status during the loading response to terminal stance. Full article

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

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23 pages, 3997 KB

Open AccessArticle

Cutting Tool Remaining Useful Life Prediction Using Multi-Sensor Data Fusion Through Graph Neural Networks and Transformers

by Xin Chen and Kai Cheng

Machines 2025, 13(11), 1027; https://doi.org/10.3390/machines13111027 - 6 Nov 2025

Abstract

In the context of Industry 4.0 and smart manufacturing, predicting cutting tool remaining useful life (RUL) is crucial for enabling and enhancing the reliability and efficiency of CNC machining. This paper presents an innovative predictive model based on the data fusion architecture of [...] Read more.

In the context of Industry 4.0 and smart manufacturing, predicting cutting tool remaining useful life (RUL) is crucial for enabling and enhancing the reliability and efficiency of CNC machining. This paper presents an innovative predictive model based on the data fusion architecture of Graph Neural Networks (GNNs) and Transformers to address the complexity of shallow multimodal data fusion, insufficient relational modeling, and single-task limitations simultaneously. The model harnesses time-series data, geometric information, operational parameters, and phase contexts through dedicated encoders, employs graph attention networks (GATs) to infer complex structural dependencies, and utilizes a cross-modal Transformer decoder to generate fused features. A dual-head output enables collaborative RUL regression and health state classification of cutting tools. Experiments are conducted on a multimodal dataset of 824 entries derived from multi-sensor data, constructing a systematic framework centered on tool flank wear width (VB), which includes correlation analysis, trend modeling, and risk assessment. Results demonstrate that the proposed model outperforms baseline models, with MSE reduced by 26–41%, MAE by 33–43%, R² improved by 6–12%, accuracy by 6–12%, and F1-Score by 7–14%. Full article

(This article belongs to the Special Issue Artificial Intelligence in Mechanical Engineering Applications)

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23 pages, 2795 KB

Open AccessArticle

Research on Position-Tracking Control Method for Fatigue Test Bed of Absorber Based on SCHO and Fuzzy Adaptive LADRC

by Muzhi Zhu, Zhilei Chen, Xingrong Huang, Xujie Zhang and Chao Xun

Machines 2025, 13(11), 1026; https://doi.org/10.3390/machines13111026 - 6 Nov 2025

Abstract

A collaborative control strategy combining the hyperbolic sine-cosine optimization (SCHO) algorithm with fuzzy adaptive linear active disturbance rejection control is proposed to address the nonlinearity and uncertainties in the hydraulic position servo system of shock absorber test benches. First, based on the dynamic [...] Read more.

A collaborative control strategy combining the hyperbolic sine-cosine optimization (SCHO) algorithm with fuzzy adaptive linear active disturbance rejection control is proposed to address the nonlinearity and uncertainties in the hydraulic position servo system of shock absorber test benches. First, based on the dynamic characteristics of the shock absorber fatigue test bench and the tested shock absorber, a linearized model of the valve-controlled hydraulic cylinder and its load was established. The coupling mechanism of system parameter perturbation and disturbance was also analyzed. A third-order LADRC (Linear Active Disturbance Rejection Control) was designed considering the linear model characteristics of the test bench hydraulic servo system model to quickly estimate internal system disturbances and perform real-time compensation. Secondly, a multi-objective optimization function was constructed by integrating system performance indicators and incorporating controller and observer bandwidths into the optimization objectives. The SCHO algorithm was used for the global search and optimization of key LADRC parameters. To enhance the controller’s adaptive capability of modeling uncertainties and external disturbances, a fuzzy adaptive module was introduced to adjust control gains online according to errors and their rates of change, further improving system robustness and dynamic performance. The results show that compared with traditional PID, under different working conditions, the proposed method reduced the maximum tracking error, overshoot, and system response time by an average of 45%, from 15% to 5%, and by approximately 30%, respectively. Meanwhile, the parameter combination obtained via SCHO effectively avoids the limitations of manual parameter tuning, significantly improving control accuracy and energy utilization. The simulation results indicate that this method can significantly enhance position-tracking accuracy compared with traditional LADRC, providing an effective solution for position-tracking control in hydraulic servo testing systems. Full article

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

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30 pages, 6683 KB

Open AccessArticle

A Hybrid Flow Energy Harvester to Power an IoT-Based Wireless Sensor System for the Digitization and Monitoring of Pipeline Networks

by Wahad Ur Rahman and Farid Ullah Khan

Machines 2025, 13(11), 1025; https://doi.org/10.3390/machines13111025 - 6 Nov 2025

Abstract

This study presents a novel energy harvesting device that combines piezoelectric and electromagnetic transduction to extract energy from fluid flow within pipelines to supply power to wireless sensor nodes for the digital transformation of pipeline networks. The proposed harvester consisted of a permanent [...] Read more.

This study presents a novel energy harvesting device that combines piezoelectric and electromagnetic transduction to extract energy from fluid flow within pipelines to supply power to wireless sensor nodes for the digital transformation of pipeline networks. The proposed harvester consisted of a permanent magnet, an unimorph circular piezoelectric plate, an adjustable housing, two wound coils, and a coil holder. In laboratory tests, the harvester demonstrated an ability to produce 831.7 µW of AC power and 680 µW of DC power at a flow pressure of 2.90 kPa and a flow rate of 11.083 L/s. The energy harvester charged a power backup from 1.01 V to 4.49 V in a time duration of 120 min. Additionally, a low-power wireless system for monitoring pipeline pressure was developed and integrated with this energy harvesting system. By incorporating this technology into the digitization of pipeline systems, continuous power generation is possible, ensuring the reliable and autonomous operation of sensors for real-time data collection and monitoring of the pipeline network. The hybrid flow energy harvester surpasses both earlier standalone electromagnetic and piezoelectric flow energy harvesters. Full article

(This article belongs to the Special Issue Recent Progress on Vibration-Based Energy Harvesting and Its Related Applications)

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15 pages, 15245 KB

Open AccessArticle

Impact of Static Rotor Eccentricity on the NVH Behavior of Electric Permanent Magnet Synchronous Machines

by Julius Müller, Georg Jacobs, Rasim Dalkiz and Stefan Wischmann

Machines 2025, 13(11), 1024; https://doi.org/10.3390/machines13111024 - 6 Nov 2025

Abstract

In comparison to internal combustion engines, which usually have low frequency, broadband excitations, in electric vehicles, tonal excitations from the electric drivetrain are noticeable and disturbing. As the acoustic and structural dynamic behavior, often referred to as noise, vibration, and harshness (NVH), strongly [...] Read more.

In comparison to internal combustion engines, which usually have low frequency, broadband excitations, in electric vehicles, tonal excitations from the electric drivetrain are noticeable and disturbing. As the acoustic and structural dynamic behavior, often referred to as noise, vibration, and harshness (NVH), strongly influences customers’ quality perceptions, optimizing it is a key challenge in development. This study investigates the influence of static rotor–stator eccentricity on the NVH behavior of an electric drivetrain using a transient elastic multibody simulation (eMBS) model incorporating non-linear gear meshing, bearing contact, and electromagnetic forces. The analysis identifies the 36th order excitation of the electric machine as the dominant source, leading to a maximum total acceleration level of 152 dB. Two specific excitation directions were found to reduce this amplitude most effectively. However, varying the amount of static eccentricity in these directions resulted in only minor vibration reductions (<1.5 dB). The findings indicate that the symmetric mode shapes of the cylindrical housing govern the response, indicating that addressing the excitability of housing modes by developing asymmetric housing designs could offer a more effective approach for NVH optimizations of electric drivetrains. Full article

(This article belongs to the Special Issue Active Vibration Control System)

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16 pages, 2221 KB

Open AccessArticle

A Comparative Study of Natural and Exact Elastic Balancing Methods for the RR-4R-R Manipulator

by Luca Bruzzone, Matteo Verotti and Pietro Fanghella

Machines 2025, 13(11), 1023; https://doi.org/10.3390/machines13111023 - 6 Nov 2025

Abstract

If elastic elements are introduced into the mechanical architecture of a robotic manipulator, a free vibration response (Natural Motion) arises that can be exploited to reduce energy consumption in cyclic motions, such as pick-and-place tasks. In this work, this approach is applied to [...] Read more.

If elastic elements are introduced into the mechanical architecture of a robotic manipulator, a free vibration response (Natural Motion) arises that can be exploited to reduce energy consumption in cyclic motions, such as pick-and-place tasks. In this work, this approach is applied to the RR-4R-R manipulator, which is derived from the SCARA robot by replacing the prismatic joint that drives the vertical motion of the end-effector with a four-bar mechanism. This mechanical modification lowers friction and facilitates the introduction of a balancing elastic element. If the elastic element is designed to provide indifferent equilibrium at any position (exact elastic balancing), the actuators need only to overcome the inertial forces; this approach is convenient for slow motions. Conversely, if the elastic element balances gravity exactly only in the median vertical position of the end-effector, Natural Motion around this position arises, and it can be exploited to reduce energy consumption in fast cyclic motions, where inertial forces become prevalent. The threshold of convenience between exact balancing and natural balancing has been evaluated for the RR-4R-R robot by means of a multibody model, assessing different performance indices: the maximum torque of the four-bar actuator, the integral control effort, and the mechanical energy. The simulation campaign was carried out considering different trajectory shapes and the influence of finite stop phases, highlighting the potential benefits of exploiting Natural Motion in robotized manufacturing lines. Full article

(This article belongs to the Special Issue Mechanism and Machine Science for Sustainable Development Goals: Contributions from the I4SDG 2025 Conference)

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20 pages, 941 KB

Open AccessArticle

Parameter Estimation of Weibull Distribution Using Constrained Search Space: An Application to Elevator Maintenance

by Khubab Ahmed, Huaqing Liu, Li Ke, Ray Tahir Mushtaq, Muhammad Zaman and Adnan Akhunzada

Machines 2025, 13(11), 1022; https://doi.org/10.3390/machines13111022 - 6 Nov 2025

Abstract

The Weibull distribution is widely used in reliability estimation across industries, but accurately identifying its parameters remains a challenging task. This research proposes an efficient method for estimating Weibull distribution parameters by combining the maximum likelihood method with optimization theory. First, the parameter [...] Read more.

The Weibull distribution is widely used in reliability estimation across industries, but accurately identifying its parameters remains a challenging task. This research proposes an efficient method for estimating Weibull distribution parameters by combining the maximum likelihood method with optimization theory. First, the parameter estimation problem is formulated as an optimization problem. A constrained search space partitioning framework is introduced, leveraging parameter-specific minimum and maximum bounds for the shape, location, and scale parameters. By dividing the search space into smaller subspaces for each parameter, the method constrains the search direction, significantly reducing estimation time. To address the local optima problem common in heuristic algorithms, a randomness operator is integrated into the optimization process. The proposed constrained search space partitioning framework is implemented using a conventional g-best version of the particle swarm optimization algorithm with historical fault data. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods and conventional optimization-based approaches in terms of estimation accuracy and computational efficiency. Full article

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

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93 pages, 3567 KB

Open AccessReview

Research Review on Workshop Scheduling for Intelligent Manufacturing: Digital Twin Modeling, Optimization Algorithm, and System Architecture

by Adilanmu Sitahong, Yulong Chen, Yiping Yuan, Areziguli Wubuli, Junyan Ma and Peiyin Mo

Machines 2025, 13(11), 1021; https://doi.org/10.3390/machines13111021 - 5 Nov 2025

Abstract

Digital twin, as a new generation of industrial intelligent technology, has become a key technology for achieving virtual-physical interaction and real-time optimization in intelligent manufacturing systems due to its capability for high-fidelity virtual mapping of physical systems. Production scheduling, as the core link [...] Read more.

Digital twin, as a new generation of industrial intelligent technology, has become a key technology for achieving virtual-physical interaction and real-time optimization in intelligent manufacturing systems due to its capability for high-fidelity virtual mapping of physical systems. Production scheduling, as the core link in the operation of intelligent workshops, faces challenges such as frequent dynamic disturbances, rendering traditional static scheduling approaches inadequate to meet the real-time and flexibility requirements of backdrop operations. In this context, the significant potential of the deep integration of digital twin technology and workshop scheduling in enhancing scheduling real-time performance, agility, and robustness has increasingly been highlighted. This paper reviews the research progress in workshop digital twin scheduling technology over the past five years, focusing on the development paths and technical characteristics of typical workshop digital twin modeling techniques, intelligent scheduling algorithms, and system frameworks. Based on this, the paper proposes a conceptual framework for digital twin scheduling in complex manufacturing scenarios, providing theoretical references for developing highly real-time and robust intelligent manufacturing scheduling systems, and highlights future research directions and developmental trends. Full article

(This article belongs to the Section Advanced Manufacturing)

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31 pages, 3565 KB

Open AccessReview

Overview: A Comprehensive Review of Soft Wearable Rehabilitation and Assistive Devices, with a Focus on the Function, Design and Control of Lower-Limb Exoskeletons

by Weilin Guo, Shiv Ashutosh Katiyar, Steve Davis and Samia Nefti-Meziani

Machines 2025, 13(11), 1020; https://doi.org/10.3390/machines13111020 - 5 Nov 2025

Abstract

With the global ageing population and the increasing prevalence of mobility impairments, the demand for effective and comfortable rehabilitation and assistive solutions has grown rapidly. Soft exoskeletons have emerged as a key direction in the development of wearable rehabilitation devices. This review examines [...] Read more.

With the global ageing population and the increasing prevalence of mobility impairments, the demand for effective and comfortable rehabilitation and assistive solutions has grown rapidly. Soft exoskeletons have emerged as a key direction in the development of wearable rehabilitation devices. This review examines how these systems are designed and controlled, as well as how they differ from the rigid exoskeletons that preceded them. Made from flexible fabrics and lightweight components, soft exoskeletons use pneumatic or cable mechanisms to support movement while keeping close contact with the body. Their compliant structure helps to reduce joint stress and makes them more comfortable for long periods of use. The discussion in this paper covers recent work on lower-limb designs, focusing on actuation, power transmission, and human–robot coordination. It also considers the main technical barriers that remain, such as power supply limits, the wear and fatigue of soft materials, and the challenge of achieving accurate tracking performance, low latency, and resilience to external disturbances. Studies reviewed here show that these systems help users regain functionality and improve rehabilitation, while also easing caregivers’ workload. The paper ends by outlining several priorities for future development: lighter mechanical layouts, better energy systems, and adaptive control methods that make soft exoskeletons more practical for everyday use as well as clinical therapy. Full article

(This article belongs to the Special Issue Innovations in Soft Robotics: Enhancing Safety, Performance, and Dexterity)

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23 pages, 4947 KB

Open AccessArticle

Graded Evaluation and Optimal Scheme Selection of Mine Rock Diggability Based on the Multidimensional Cloud Model

by Shibin Yao, Xiaoyuan Li, Jian Zhou and Manoj Khandelwal

Machines 2025, 13(11), 1019; https://doi.org/10.3390/machines13111019 - 3 Nov 2025

Abstract

With the advancement of mining technologies, the evaluation of rock diggability has become a critical research topic for ensuring both safety and efficiency in mining operations. This study establishes a comprehensive evaluation system for mine rock diggability and proposes corresponding grading criteria. For [...] Read more.

With the advancement of mining technologies, the evaluation of rock diggability has become a critical research topic for ensuring both safety and efficiency in mining operations. This study establishes a comprehensive evaluation system for mine rock diggability and proposes corresponding grading criteria. For the determination of indicator weights, a combination of subjective and objective methods is employed, integrating expert knowledge and data characteristics to identify optimal weights, thereby providing a reliable basis for comprehensive evaluation. The single-indicator cloud model effectively mitigates the difficulties associated with defining transitional values between adjacent intervals. The multidimensional cloud model, by considering the interactions among indicators, enables the optimization of indicator interactions and enhances the interpretability of diggability grades. Comparison with the Diggability Index (DI) method shows a high consistency between the two approaches (R² = 0.991). The absolute accuracy of diggability levels reaches 74%, while the accuracy based on cloud model fuzzy evaluation reaches 100%, demonstrating the effectiveness of the cloud model in handling transitional intervals and capturing uncertainty. This study provides a novel methodology and theoretical foundation for the scientific evaluation of mine rock diggability, offering practical guidance for reasonable grading, optimization of mining parameters, and interpretation of diggability levels in engineering practice. Full article

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

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14 pages, 5265 KB

Open AccessArticle

Non-Line-of-Sight Error Compensation Method for Ultra-Wideband Positioning System

by Bin Liang, Xuechuang Zhu, Tonggang Liu and Guangpeng Shan

Machines 2025, 13(11), 1018; https://doi.org/10.3390/machines13111018 - 3 Nov 2025

Abstract

Existing Ultra-Wideband (UWB) positioning methods are poorly suited for underground mobile devices and have limited positioning effectiveness in complex scenarios such as narrow tunnels, high dust levels, metallic structures, moving personnel, and machinery. To address this, we propose a UWB positioning method for [...] Read more.

Existing Ultra-Wideband (UWB) positioning methods are poorly suited for underground mobile devices and have limited positioning effectiveness in complex scenarios such as narrow tunnels, high dust levels, metallic structures, moving personnel, and machinery. To address this, we propose a UWB positioning method for non-line-of-sight (NLOS) error compensation, significantly improving the positioning accuracy of mobile equipment in coal mine tunnels. First, the characteristics of the impulse response waveform channel of the dataset are extracted, and the AdaBoost-based ensemble learning method is used to identify the mixture propagation channel. Then, combined with the UWB range noise model, the extended Kalman filter (EKF) algorithm is used to compensate for UWB NLOS errors. Finally, a mobile tag is used in conjunction with four positioning base stations to obtain positioning data, and the positioning effect in coal mine tunnels is simulated using a ranging noise model. The experimental results show that the EKF error compensation algorithm has good positioning accuracy and algorithm stability in different motion states in a noisy environment. Full article

(This article belongs to the Section Vehicle Engineering)

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30 pages, 11325 KB

Open AccessArticle

An Enhanced NSGA-II Algorithm Combining Lévy Flight and Simulated Annealing and Its Application in Electric Winch Trajectory Planning: A Complex Multi-Objective Optimization Study

by Enzhi Quan, Yanjun Liu, Han Gao, Huaqiang You and Gang Xue

Machines 2025, 13(11), 1017; https://doi.org/10.3390/machines13111017 - 3 Nov 2025

Abstract

To overcome the limitations of traditional multi-objective evolutionary algorithms—which often become trapped in local optima when addressing complex optimization problems and face challenges in balancing convergence efficiency with population diversity—this study proposes an enhanced NSGA-II algorithm that incorporates Lévy flight and simulated annealing [...] Read more.

To overcome the limitations of traditional multi-objective evolutionary algorithms—which often become trapped in local optima when addressing complex optimization problems and face challenges in balancing convergence efficiency with population diversity—this study proposes an enhanced NSGA-II algorithm that incorporates Lévy flight and simulated annealing strategies. The proposed algorithm enhances global exploration via Lévy flight mutation, improves local search precision through simulated annealing, and dynamically coordinates the search process using adaptive parameter strategies. Experiments conducted on the ZDT and DTLZ test function series demonstrated that the proposed algorithm achieves performance comparable to or better than that of NSGA-II and other benchmark algorithms, as measured by inverted generational distance and hypervolume metrics. It also exhibited superior convergence, distribution uniformity, and robustness. Furthermore, the algorithm was applied to the multi-objective optimization of electric winch trajectories for oil drilling rigs, which employed trajectory planning based on quintic polynomials. The simulation results demonstrated, compared to the pre-optimization baseline data, reductions of 6% in total operation time, 17.99% in energy consumption, and 27.4% in impact severity, thereby validating the method’s effectiveness and applicability in practical engineering scenarios. The comprehensive results demonstrate that the improved algorithm exhibits robust performance and excellent adaptability when addressing complex multi-objective optimization problems. Full article

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

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15 pages, 4111 KB

Open AccessArticle

Enabling Manual Guidance in High-Payload Industrial Robots for Flexible Manufacturing Applications in Large Workspaces

by Paolo Avanzi La Grotta, Martina Salami, Andrea Trentadue, Pietro Bilancia and Marcello Pellicciari

Machines 2025, 13(11), 1016; https://doi.org/10.3390/machines13111016 - 3 Nov 2025

Abstract

Industrial Robots (IRs) are typically employed as flexible machines to perform many types of repetitive and intensive tasks within fenced safe areas, ensuring high productivity and cost efficiency. However, their rigid programming approaches often pose challenges during cell commissioning and reset, hindering the [...] Read more.

Industrial Robots (IRs) are typically employed as flexible machines to perform many types of repetitive and intensive tasks within fenced safe areas, ensuring high productivity and cost efficiency. However, their rigid programming approaches often pose challenges during cell commissioning and reset, hindering the implementation of self-reconfigurable systems. In addition, several production lines still need the presence of skilled operators to conduct assisted assembly operations and inspections. This motivates the growing interest in the development of innovative solutions for supporting safe and efficient human–robot collaborative applications. The manual guidance of the IR end-effector is a representative functionality of such collaboration, as it simplifies heavy-part manipulation and allows intuitive robot teaching and programming. The present study reports a sensor-based approach for enabling manual guidance operations with high-payload IRs and discusses its practical implementation on a production cell with an extended workspace. The setup features a KUKA robot mounted on a custom linear track actuated via Beckhoff technology to enable flexible assembly and machining operations. The developed logic and its software configuration, split into multiple control units to allow the manual guiding of both the 6-axis IR and the linear track unit, are described in detail. Finally, an experimental demonstration involving two users with different levels of expertise was conducted to evaluate the approach during target teaching on a physical cell. The results showed that the proposed manual guidance method significantly reduced task completion time by more than 55% compared with the conventional teach pendant, demonstrating the effectiveness and practical advantages of the developed framework. Full article

(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)

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