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Machines
Volume 12
Issue 8
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Machines, Volume 12, Issue 8 (August 2024) – 92 articles

Cover Story (view full-size image): A modular deployable robot, which adopts an origami structure instead of a flexible hinge, has been proposed and is examined within this work. The forward and inverse kinematic models of the robot were established by using the screw theory. A prototype of this robot was constructed, and its folding performance and bending performance were tested. In the folding test, the folding rate reached 60.71% in an upright state and 61.75% in an inverted state. In the bending test, the robot reached a 180° bending angle in both upright and inverted states. Finally, this study also tested the motion accuracy of the robot and performed an error analysis and optimization; the optimized robot position error was reduced by about 65%, effectively improving the motion accuracy and stability of the robot. View this paper
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23 pages, 7760 KiB  
Article
Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network
by Chengshun Zhu, Wei Lin, Hongji Zhang, Youren Cao, Qiming Fan and Hui Zhang
Machines 2024, 12(8), 587; https://doi.org/10.3390/machines12080587 - 22 Aug 2024
Viewed by 306
Abstract
In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo [...] Read more.
In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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22 pages, 9983 KiB  
Article
Vibration and Flow Characteristics of a 200 MW Kaplan Turbine Unit under Off-Cam Conditions
by Dandan Yan, Shuqiang Chen, Peng Ren, Weiqiang Zhao, Xiaobin Chen, Chengming Liu, Lingjiu Zhou and Zhengwei Wang
Machines 2024, 12(8), 586; https://doi.org/10.3390/machines12080586 - 22 Aug 2024
Viewed by 265
Abstract
Kaplan turbine units can adjust their blades to achieve wider outputs without a significant loss of efficiency. The combination of guide vane angle (GVA) and blade angle (BA) is selected based on efficiency curves obtained from cam tests. However, the vibration characteristics are [...] Read more.
Kaplan turbine units can adjust their blades to achieve wider outputs without a significant loss of efficiency. The combination of guide vane angle (GVA) and blade angle (BA) is selected based on efficiency curves obtained from cam tests. However, the vibration characteristics are not considered in the test. The vibration and flow characteristics are complex with different combinations of guide vane and blade angles. Different cam relation selection principles lead to varying machine vibration and flow characteristics. In this research, the flow and vibration characteristics were obtained by means of field test and numerical simulation. Vibration, pressure pulsation, and other stability indicators have been extracted and investigated under off-cam conditions. The flow and variation rules of different indicators have been thoroughly researched. The findings suggest that the magnitude of vibration in the X direction surpassed that in the Y direction for the head cover, upper frame, and lower frame under 22 experimental conditions. The disparity between the head cover and upper frame in both directions was not significant, whereas a substantial contrast existed between the lower frame in the X and Y directions. The calculation results indicate that when the guide vane angle was small, vortices appeared near the high-pressure edge of the runner in the vaneless region and caused disorganized flow lines in the runner, and this complex vortex behavior led to multiple frequency components in the pressure pulsation frequency domain. The conclusions provide references for the designers of Kaplan turbine units and improves the operating safety of Kaplan turbine power stations. Full article
(This article belongs to the Section Turbomachinery)
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16 pages, 6225 KiB  
Article
Optimization of Desired Multiple Resonant Modes of Compliant Parallel Mechanism Using Specific Frequency Range and Targeted Ratios
by Vin Low, Song Huat Yeo and Minh Tuan Pham
Machines 2024, 12(8), 585; https://doi.org/10.3390/machines12080585 - 22 Aug 2024
Viewed by 264
Abstract
In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and [...] Read more.
In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations. Full article
(This article belongs to the Special Issue Design Methodology for Soft Mechanisms, Machines, and Robots)
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25 pages, 3004 KiB  
Article
Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning
by Hengliang Tang and Jinda Dong
Machines 2024, 12(8), 584; https://doi.org/10.3390/machines12080584 - 22 Aug 2024
Viewed by 351
Abstract
Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural [...] Read more.
Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based on Relation (HGNNR) with Deep Reinforcement Learning (DRL). The proposed framework models the complex relationships in FJSP using heterogeneous graphs, where operations and machines are represented as nodes, with directed and undirected arcs indicating dependencies and compatibilities. The HGNNR framework comprises four key components: relation-specific subgraph decomposition, data preprocessing, feature extraction through graph convolution, and cross-relation feature fusion using a multi-head attention mechanism. For decision-making, we employ the Proximal Policy Optimization (PPO) algorithm, which iteratively updates policies to maximize cumulative rewards through continuous interaction with the environment. Experimental results on four public benchmark datasets demonstrate that our proposed method outperforms four state-of-the-art DRL-based techniques and three common rule-based heuristic algorithms, achieving superior scheduling efficiency and generalization capabilities. This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability. Full article
(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)
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24 pages, 11901 KiB  
Article
The Workspace Analysis of the Delta Robot Using a Cross-Section Diagram Based on Zero Platform
by Jun-Ho Hong, Ji-Ho Lim, Euntaek Lee and Dongwon Shin
Machines 2024, 12(8), 583; https://doi.org/10.3390/machines12080583 - 22 Aug 2024
Viewed by 353
Abstract
This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness [...] Read more.
This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness in configuring and generating the workspace conveniently. Analyzing the workspace of parallel robots, such as delta robots, requires extensive computation due to the constraints between the links, typically requiring complex equations or numerical methods. This paper proposes a new method for quickly estimating the shape and size of the workspace using a cross-section diagram based on a geometrical analysis of the zero-platform delta robot. The shape and size of the workspace can be rapidly estimated because the intersection of three cross-section diagrams needs only the torus’s 2D operation. Comparing the workspace between the cross-section diagram and the 3D CAD software, this paper shows that the cross-section diagram can analyze the shape and size of the workspace quickly and give a more geometrical understanding of the workspace. Full article
(This article belongs to the Section Machine Design and Theory)
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18 pages, 1592 KiB  
Article
Support Vector Machine-Based Fault Diagnosis under Data Imbalance with Application to High-Speed Train Electric Traction Systems
by Yunkai Wu, Tianxiang Ji, Yang Zhou and Yijin Zhou
Machines 2024, 12(8), 582; https://doi.org/10.3390/machines12080582 - 22 Aug 2024
Viewed by 283
Abstract
The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction [...] Read more.
The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction systems under data imbalance is not only theoretically important but also crucial for ensuring vehicle safety. Firstly, when addressing the data imbalance issue, the fault diagnosis mechanism based on support vector machines tends to prioritize the majority class when constructing the classification hyperplane. This frequently leads to a reduction in the recognition rate of minority-class samples. To tackle this problem, a self-tuning support vector machine is proposed in this paper by setting distinct penalty factors for each class based on sample information. This approach aims to ensure equal misclassification costs for both classes and achieve the objective of suppressing the deviation of the classification hyperplane. Finally, simulation experiments are conducted on the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform using three different imbalance ratios to address the data imbalance issue. The experimental results demonstrate consistent misclassification costs for both the minority- and majority-class samples. Additionally, the proposed self-tuning support vector machine effectively mitigates hyperplane deviation, further confirming the effectiveness of this fault diagnosis mechanism for high-speed train electric traction systems. Full article
(This article belongs to the Section Automation and Control Systems)
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16 pages, 2593 KiB  
Article
An Advanced Technique for the Detection of Pathological Gaits from Electromyography Signals: A Comprehensive Approach
by Karina Lenkevitciute, Jurgita Ziziene and Kristina Daunoraviciene
Machines 2024, 12(8), 581; https://doi.org/10.3390/machines12080581 - 22 Aug 2024
Viewed by 292
Abstract
The aim of this study was to determine the most appropriate advanced methods for distinguishing the gait of healthy children (CO) from the gait of children with cerebral palsy (CP) based on electromyography (EMG) parameters and coactivations. An EMG database of 22 children [...] Read more.
The aim of this study was to determine the most appropriate advanced methods for distinguishing the gait of healthy children (CO) from the gait of children with cerebral palsy (CP) based on electromyography (EMG) parameters and coactivations. An EMG database of 22 children (aged 4–11 years) was used in this study, which included 17 subjects in the CO group and 5 subjects in the CP group. EMG time parameters were calculated for the biceps femoris (BF) and semitendinosus (SE) muscles and coactivations for the rectus femoris (RF)/BF and RF/SE muscle pairs. To obtain a more accurate classification result, data augmentation was performed, and three classification algorithms were used: support vector machine (SVM), k-nearest neighbors (KNNs), and decision tree (DT). The accuracy of the root-mean-square (RMS) parameter and KNN algorithm was 95%, the precision was 94%, the sensitivity was 90%, the F1 score was 92%, and the area under the curve (AUC) score was 98%. The highest classification accuracy based on coactivations was achieved using the KNN algorithm (91–95%). It was determined that the KNN algorithm is the most effective, and muscle coactivation can be used as a reliable parameter in gait classification tasks. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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17 pages, 12755 KiB  
Article
Analysis of Resistance Influencing Factors of a Bench System Based on a Self-Developed Four-Wheel Drive Motor Vehicle Chassis Dynamometer
by Wanyou Huang, Dongying Liu, Ruixia Chu, Fangyuan Qiu, Zhenyu Li, Xiaoyue Jin, Hongtao Zhang, Yan Wang and Shaobo Ji
Machines 2024, 12(8), 580; https://doi.org/10.3390/machines12080580 - 22 Aug 2024
Viewed by 255
Abstract
In order to accurately simulate the actual road driving resistance of four-wheel drive motor vehicles based on the chassis dynamometer and efficiently test the vehicle performance, it is necessary to analyze the influencing factors of the additional loss resistance and the loading resistance [...] Read more.
In order to accurately simulate the actual road driving resistance of four-wheel drive motor vehicles based on the chassis dynamometer and efficiently test the vehicle performance, it is necessary to analyze the influencing factors of the additional loss resistance and the loading resistance of the chassis dynamometer bench system. In this paper, the effects of the drum speed, the sampling speed interval, and basic inertia on the test results of the additional loss resistance are tested and analyzed based on the self-developed chassis dynamometer of a four-wheel drive motor vehicle. The static and dynamic components of the additional loss resistance are defined by linear regression through ordinary least squares, and the additional loss resistance of the four-axis eight-drum chassis dynamometer and mainstream chassis dynamometer system for four-wheel drive motor vehicles are compared. In addition, the effects of the dynamometer type and the control strategy on the loading resistance are discussed, and the transient condition, steady-state condition, and overall operating condition deviation coefficient of loading force are defined, according to which the advantages and disadvantages of the control strategy of the chassis dynamometer system for four-wheel drive motor vehicles are evaluated. The analysis of the influencing factors and laws of the resistance of the four-wheel drive motor vehicle chassis dynamometer bench system can provide a reference basis for accurately simulating the resistance of vehicle road driving based on the bench testing. Full article
(This article belongs to the Section Vehicle Engineering)
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14 pages, 2935 KiB  
Article
Research on Scheduling Algorithm of Knitting Production Workshop Based on Deep Reinforcement Learning
by Lei Sun, Weimin Shi, Chang Xuan and Yongchao Zhang
Machines 2024, 12(8), 579; https://doi.org/10.3390/machines12080579 - 22 Aug 2024
Viewed by 300
Abstract
Intelligent scheduling of knitting workshops is the key to realizing knitting intelligent manufacturing. In view of the uncertainty of the workshop environment, it is difficult for existing scheduling algorithms to flexibly adjust scheduling strategies. This paper proposes a scheduling algorithm architecture based on [...] Read more.
Intelligent scheduling of knitting workshops is the key to realizing knitting intelligent manufacturing. In view of the uncertainty of the workshop environment, it is difficult for existing scheduling algorithms to flexibly adjust scheduling strategies. This paper proposes a scheduling algorithm architecture based on deep reinforcement learning (DRL). First, the scheduling problem of knitting intelligent workshops is represented by a disjunctive graph, and a mathematical model is established. Then, a multi-proximal strategy (multi-PPO) optimization training algorithm is designed to obtain the optimal strategy, and the job selection strategy and machine selection strategy are trained at the same time. Finally, a knitting intelligent workshop scheduling experimental platform is built, and the algorithm proposed in this paper is compared with common heuristic rules and metaheuristic algorithms for experimental testing. The results show that the algorithm proposed in this paper is superior to heuristic rules in solving the knitting workshop scheduling problem, and can achieve the accuracy of the metaheuristic algorithm. In addition, the response speed of the algorithm in this paper is excellent, which meets the production scheduling needs of knitting intelligent workshops and has a good guiding significance for promoting knitting intelligent manufacturing. Full article
(This article belongs to the Section Industrial Systems)
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21 pages, 11867 KiB  
Article
Thermo-Mechanical Coupling Analysis of Inserts Supporting Run-Flat Tires under Zero-Pressure Conditions
by Cheng Xue, Liguo Zang, Fengqi Wei, Yuxin Feng, Chong Zhou and Tian Lv
Machines 2024, 12(8), 578; https://doi.org/10.3390/machines12080578 - 21 Aug 2024
Viewed by 269
Abstract
The inserts supporting run-flat tire (ISRFT) is mainly used in military off-road vehicles, which need to maintain high mobility after a blowout. Regulations show that the ISRFT can be driven safely for at least 100 km at a speed of 30 km/h to [...] Read more.
The inserts supporting run-flat tire (ISRFT) is mainly used in military off-road vehicles, which need to maintain high mobility after a blowout. Regulations show that the ISRFT can be driven safely for at least 100 km at a speed of 30 km/h to 40 km/h under zero-pressure conditions. However, the ISRFT generates serious heat during zero-pressure driving, which accelerates the aging of the tire rubber and degrades its performance. In order to study the thermo-mechanical coupling characteristics of the ISRFT, a three-dimensional finite element model verified by bench tests was established. Then, the stress–strain, energy loss and heat generation of the ISRFT were analyzed by the sequential thermo-mechanical coupling method to obtain the steady-state temperature field (SSTF). Finally, four kinds of honeycomb inserts bodies were designed based on the tangent method, and the SSTF of the honeycomb and the original ISRFT were compared. The results indicated that the high-temperature region of the ISRFT is concentrated in the shoulder area. For every 1 km/h increase in velocity, the temperature at the shoulder of the tire increases by approximately 1.6 °C. The SSTF of the honeycomb ISRFT is more uniformly distributed, and the maximum temperature of the shoulder decreases by about 30 °C, but the maximum temperature of the tread increases by about 40 °C. This study provides methodological guidance for investigating the temperature and mechanical characteristics of the ISRFT under zero-pressure conditions. Full article
(This article belongs to the Section Vehicle Engineering)
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21 pages, 10327 KiB  
Article
Flow Field Noise Analysis and Noise Reduction Research of Twin-Screw Air Compressor Based on Multi-Field Coupling Technology
by Yayin He, Xuyang He, Lijun Chen, Junli Wang, Yongqiang Zhao and Zhigui Ren
Machines 2024, 12(8), 577; https://doi.org/10.3390/machines12080577 - 21 Aug 2024
Viewed by 284
Abstract
To address the flow field noise problem in twin-screw air compressors, multi-physical-field coupling technology is employed to perform flow field noise calculations for the compressor. Based on the structural characteristics and noise generation principles of the twin-screw compressor, a noise reduction design method [...] Read more.
To address the flow field noise problem in twin-screw air compressors, multi-physical-field coupling technology is employed to perform flow field noise calculations for the compressor. Based on the structural characteristics and noise generation principles of the twin-screw compressor, a noise reduction design method is proposed that employs a Helmholtz resonator and a three-chamber perforated muffler at the exhaust end. The muffler’s structural optimization is performed using a genetic algorithm, and the effectiveness of the noise reduction method is validated through calculations. The results indicate that the Helmholtz resonator effectively mitigates airflow pulsation at the exhaust port, stabilizing the flow and reducing low-frequency noise at the exhaust end. Additionally, the designed three-chamber perforated muffler achieves noise reduction across a broad frequency range. With this noise reduction method, the exhaust port noise of the twin-screw compressor is reduced from 100–114 dB to 37–68 dB. These findings provide valuable insights for vibration and noise reduction in twin-screw air compressors. Full article
(This article belongs to the Section Turbomachinery)
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20 pages, 2623 KiB  
Article
Analyzing Higher-Order Curvature of Four-Bar Linkages with Derivatives of Screws
by Liheng Wu, Jianguo Cai and Jian S. Dai
Machines 2024, 12(8), 576; https://doi.org/10.3390/machines12080576 - 21 Aug 2024
Viewed by 349
Abstract
Curvature theory, a fundamental subject in kinematics, is typically addressed through the concepts of instantaneous invariants and canonical coordinates, which are pivotal for the generation of mechanical paths. This paper delves into this subject with a higher-order analysis of screws, employing both canonical [...] Read more.
Curvature theory, a fundamental subject in kinematics, is typically addressed through the concepts of instantaneous invariants and canonical coordinates, which are pivotal for the generation of mechanical paths. This paper delves into this subject with a higher-order analysis of screws, employing both canonical and natural coordinates. Through this exploration, a new Euler–Savary equation is derived, one that does not rely on canonical coordinates. Additionally, the paper provides a comprehensive classification of the degenerate conditions of the cubic of stationary curves of four-bar linkages at rotational positions. A thorough examination of four-bar linkages in translational positions—the couple links translate instantaneously—is also presented, with analyses extending up to the sixth order. The findings reveal that the Burmester’s points at translational positions can be extended to Burmester’s points with excess one, provided that all pivot points are symmetrically distributed about the pole norm with the two cranks in corotating senses. However, the extension to Burmester’s points with excess two is not possible. Similarly, the Ball’s point with excess one does not progress to Ball’s point with excess two. The paper also highlights that the traditional method, which is based on canonical coordinates, is ineffective when the four-bar linkage forms a parallelogram. Fortunately, this scenario can be effectively analyzed using the screw-based approach. Ultimately, the results presented can also serve as analytical solutions for 3-RR platforms with higher-order shakiness. Full article
(This article belongs to the Section Machine Design and Theory)
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25 pages, 2550 KiB  
Article
Performance Analysis of New One-Piece Iron Roughneck and Its Spinning Mechanism
by Yongbai Sha, Donghe Han, Donghu Chen and Congzhi Liu
Machines 2024, 12(8), 575; https://doi.org/10.3390/machines12080575 - 21 Aug 2024
Viewed by 296
Abstract
The iron roughneck is an automated piece of equipment utilized for the connection and removal of drilling tools. This paper presents the design of an integrated iron roughneck, providing a detailed introduction to its clamp body structure, along with an analysis of its [...] Read more.
The iron roughneck is an automated piece of equipment utilized for the connection and removal of drilling tools. This paper presents the design of an integrated iron roughneck, providing a detailed introduction to its clamp body structure, along with an analysis of its structural characteristics and performance requirements. The study delves into the integration mode and working characteristics of the clamping mechanism and spin buckle mechanism for the integrated upper clamp body structure of the iron roughneck. Additionally, this paper conducts an in-depth theoretical study on the spin buckle mechanism. Firstly, it analyzes the actual working condition of the spin buckle roller from two perspectives, namely contact theory and rolling friction theory, determining the structural form of the spin buckle roller. Secondly, it investigates the relative displacement between the spin buckle mechanism and the drilling tool, proposing a design method for the floating device mounted on the spin buckle roller and establishing the kinematic equation of the spin buckle roller under the influence of the floating device. Furthermore, the kinematic equations of the spin buckle roller under the influence of the floating device are established. Finally, a dynamics simulation experiment is performed to simulate the working process of the spin buckle mechanism under actual working conditions, analyzing the dynamics and kinematics of the spin buckle mechanism and obtaining the relevant parameter curves of the spin buckle mechanism and drilling tools. Through data comparison and analysis, the correctness of the theoretical analysis results and the rationality of the performance and structure of the spin buckle mechanism are verified. Full article
(This article belongs to the Section Advanced Manufacturing)
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29 pages, 2692 KiB  
Review
Novel Directions for Neuromorphic Machine Intelligence Guided by Functional Connectivity: A Review
by Mindula Illeperuma, Rafael Pina, Varuna De Silva and Xiaolan Liu
Machines 2024, 12(8), 574; https://doi.org/10.3390/machines12080574 - 20 Aug 2024
Viewed by 376
Abstract
As we move into the next stages of the technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains. Leveraging the concept of functional connectivity (FC) in the human brain, this paper provides [...] Read more.
As we move into the next stages of the technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains. Leveraging the concept of functional connectivity (FC) in the human brain, this paper provides novel research directions for neuromorphic machine intelligence (NMI) systems that are energy-efficient and human-compatible. This review serves as an accessible review for multidisciplinary researchers introducing a range of concepts inspired by neuroscience and analogous machine learning research. These include possibilities to facilitate network integration and segregation in artificial architectures, a novel learning representation framework inspired by two FC networks utilised in human learning, and we explore the functional connectivity underlying task prioritisation in humans and propose a framework for neuromorphic machines to improve their task-prioritisation and decision-making capabilities. Finally, we provide directions for key application domains such as autonomous driverless vehicles, swarm intelligence, and human augmentation, to name a few. Guided by how regional brain networks interact to facilitate cognition and behaviour such as the ones discussed in this review, we move toward a blueprint for creating NMI that mirrors these processes. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Machines)
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17 pages, 7219 KiB  
Article
Fault Detection of Rotating Machines Using poly-Coherent Composite Spectrum of Measured Vibration Responses with Machine Learning
by Khalid Almutairi, Jyoti K. Sinha and Haobin Wen
Machines 2024, 12(8), 573; https://doi.org/10.3390/machines12080573 - 19 Aug 2024
Viewed by 368
Abstract
This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations into a single spectrum, retaining amplitude and phase information while [...] Read more.
This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations into a single spectrum, retaining amplitude and phase information while reducing background noise. The use of pCCS significantly reduces the number of extracted parameters in the frequency domain compared to using individual spectra at each measurement location. This reduction in parameters is crucial, especially for large industrial rotating machines, as processing and analysing extensive datasets demand significant computational resources, increasing the time and cost of fault detection. An artificial neural network (ANN)-based machine learning model is then employed for fault detection using these reduced extracted parameters. The methodology is developed and validated on an experimental rotating machine at three different speeds: below the first critical speed, between the first and second critical speeds, and above the second critical speed. This range of speeds represents the diverse dynamic conditions commonly encountered in industrial settings. This study examines both healthy machine conditions and various simulated fault conditions, including misalignment, rotor-to-stator rub, shaft cracks, and bearing faults. By combining the pCCS technique with machine learning, this study enhances the reliability, efficiency, and practical applicability of fault detection in rotating machines under varying dynamic conditions and different machine conditions. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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18 pages, 8130 KiB  
Article
Design and Prototyping of a Collaborative Station for Machine Parts Assembly
by Federico Emiliani, Albin Bajrami, Daniele Costa, Giacomo Palmieri, Daniele Polucci, Chiara Leoni and Massimo Callegari
Machines 2024, 12(8), 572; https://doi.org/10.3390/machines12080572 - 19 Aug 2024
Viewed by 359
Abstract
Collaboration between humans and machines is the core of the Industry 5.0 paradigm, and collaborative robotics is one the most impactful enabling technologies for small and medium Enterprises (SMEs). In fact, small batch production and high levels of product customization make parts assembly [...] Read more.
Collaboration between humans and machines is the core of the Industry 5.0 paradigm, and collaborative robotics is one the most impactful enabling technologies for small and medium Enterprises (SMEs). In fact, small batch production and high levels of product customization make parts assembly one of the most challenging operations to be automated, and it often still depends on the versatility of human labor. Collaborative robots, for their part, can be easily integrated in this productive paradigm, as they have been specifically developed for coexistence with human beings. This work investigates the performance of collaborative robots in machine parts assembly. Design and research activities were carried out as a case study of industrial relevance at the i-Labs industry laboratory, a pole of innovation that is briefly introduced at the beginning of the paper. A fully-functional prototype of the cobotized station was realized at the end of the project, and several experimental tests were performed to validate the robustness of the assembly process as well as the collaborative nature of the application. Full article
(This article belongs to the Special Issue Advancing Human-Robot Collaboration in Industry 4.0)
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25 pages, 19178 KiB  
Article
A High-Speed Train Axle Box Bearing Fault Diagnosis Method Based on Dimension Reduction Fusion and the Optimal Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals
by Zhongyao Wang, Zejun Zheng, Dongli Song and Xiao Xu
Machines 2024, 12(8), 571; https://doi.org/10.3390/machines12080571 - 19 Aug 2024
Viewed by 241
Abstract
The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box [...] Read more.
The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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22 pages, 23119 KiB  
Article
Assessment of the Uniform Wear Bending Strength of Large Modulus Rack and Pinion Pair: Theoretical vs. Experimental Results
by Zongxing Gong, Baojia Chen and Xuan Cheng
Machines 2024, 12(8), 570; https://doi.org/10.3390/machines12080570 - 19 Aug 2024
Viewed by 296
Abstract
Due to long-term operation under low-speed and heavy-load conditions, large module gears and racks are inevitably subject to tooth surface wear. To investigate the changes in gear tooth bending strength, the Three Gorges ship lift was taken as the research object and a [...] Read more.
Due to long-term operation under low-speed and heavy-load conditions, large module gears and racks are inevitably subject to tooth surface wear. To investigate the changes in gear tooth bending strength, the Three Gorges ship lift was taken as the research object and a simulation test bench was established. An analytical method, a finite element method, and an experimental method were utilized to analyze the bending stress of gears under normal and various uniform wear conditions. The obtained results revealed that with the increase in wear degree and load, the bending stress of single-tooth meshing was significantly higher than that of double-tooth meshing, and the single-tooth meshing time also increased, which indicates that gear wear accelerated the process of performance degradation. Furthermore, the relative errors obtained by the three calculation methods were all at a low level. This investigation aims to provide a solid theoretical and experimental basis for the dynamic analysis of large module gear and rack transmission. Full article
(This article belongs to the Section Machine Design and Theory)
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50 pages, 1870 KiB  
Review
Optimization Techniques in the Localization Problem: A Survey on Recent Advances
by Massimo Stefanoni, Peter Sarcevic, József Sárosi and Akos Odry
Machines 2024, 12(8), 569; https://doi.org/10.3390/machines12080569 - 19 Aug 2024
Viewed by 254
Abstract
Optimization is a mathematical discipline or tool suitable for minimizing or maximizing a function. It has been largely used in every scientific field to solve problems where it is necessary to find a local or global optimum. In the engineering field of localization, [...] Read more.
Optimization is a mathematical discipline or tool suitable for minimizing or maximizing a function. It has been largely used in every scientific field to solve problems where it is necessary to find a local or global optimum. In the engineering field of localization, optimization has been adopted too, and in the literature, there are several proposals and applications that have been presented. In the first part of this article, the optimization problem is presented by considering the subject from a purely theoretical point of view and both single objective (SO) optimization and multi-objective (MO) optimization problems are defined. Additionally, it is reported how local and global optimization problems can be tackled differently, and the main characteristics of the related algorithms are outlined. In the second part of the article, extensive research about local and global localization algorithms is reported and some optimization methods for local and global optimum algorithms, such as the Gauss–Newton method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and so on, are presented; for each of them, the main concept on which the algorithm is based, the mathematical model, and an example of the application proposed in the literature for localization purposes are reported. Among all investigated methods, the metaheuristic algorithms, which do not exploit gradient information, are the most suitable to solve localization problems due to their flexibility and capability in solving non-convex and non-linear optimization functions. Full article
(This article belongs to the Special Issue New Localization Methods and Motion Tracking Algorithms for Mechatronic Systems, Robots and Unmanned Vehicles)
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19 pages, 7004 KiB  
Article
A Study on the Running of a Joystick-Type Six-Wheeled Electric Wheelchair When Curb Climbing
by Tetsuaki Kawata, Fumihisa Sato, Shiori Tsuji, Toya Suzuki, Takato Suzuki and Takuto Kokuryu
Machines 2024, 12(8), 568; https://doi.org/10.3390/machines12080568 - 19 Aug 2024
Viewed by 307
Abstract
In Japan, the number of power wheelchair users is increasing as the country becomes an aging society. This trend is expected to continue in the future. Electric wheelchairs currently on the market include (1) bar-handle-type power wheelchairs for older users and (2) joystick-type [...] Read more.
In Japan, the number of power wheelchair users is increasing as the country becomes an aging society. This trend is expected to continue in the future. Electric wheelchairs currently on the market include (1) bar-handle-type power wheelchairs for older users and (2) joystick-type power wheelchairs that change direction by operating a joystick. When such electric wheelchairs are used outdoors, the problem is curb-climbing at the boundary between the roadway and the sidewalk. It would be difficult for a wheelchair with a small front wheel diameter of 200 mm to overcome a curb height of 50 mm. Therefore, users are forced to take a detour or drive on the street to avoid the curb step. One of the most effective ways to solve this problem is to increase the wheel diameter. However, larger wheels make it more difficult for users to get in and out of the wheelchair. In addition, there are problems such as an increased footprint when turning, which makes the wheelchairs difficult to use on narrow streets. In this paper, using a joystick-type six-wheel electric wheelchair as an example, we examined the mechanism by which an electric wheelchair can overcome curb climbing and consider improvements to the chassis with a method that does not rely on increasing the wheel diameter. As a result, it became possible to overcome a curb of 96 mm in height with a front-wheel diameter of 200 mm. Full article
(This article belongs to the Section Vehicle Engineering)
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30 pages, 17677 KiB  
Article
Theoretical and Experimental Investigation of a Novel Wedge-Loading Planetary Traction Drive
by Yujiang Jiang and Guangjian Wang
Machines 2024, 12(8), 567; https://doi.org/10.3390/machines12080567 - 19 Aug 2024
Viewed by 476
Abstract
The development of high-speed motors has stimulated the demand for high-speed reducers. In response to the lack of research on high-speed reducers and the challenge of developing high-speed transmission systems, this study proposes a novel wedge-loading planetary traction drive (WPTD). First, a more [...] Read more.
The development of high-speed motors has stimulated the demand for high-speed reducers. In response to the lack of research on high-speed reducers and the challenge of developing high-speed transmission systems, this study proposes a novel wedge-loading planetary traction drive (WPTD). First, a more accurate theoretical analysis model is established by considering the combined effects of elastic deformation, loading state, and a elastohydrodynamic lubrication (EHL) traction mechanism. Second, the mixed thermal EHL model is introduced into the performance analysis of traction drive for the first time. The fitting formulas for predicting traction contact behavior are derived, and a performance analysis method for all line-contact traction drives is presented. Third, the loading performance, transmission characteristics, and the influence of different parameters on the transmission characteristics of WPTD are analyzed. Finally, the theoretical model is validated by prototype performance tests. The results show that the loading mechanism demonstrates a good self-adaptive loading effect, and WPTD achieves a peak efficiency of 96%. Additionally, WPTD delivers superior efficiency and vibration and noise performance because of its smooth power-transfer characteristics, thereby providing a possible solution for high-speed and low-vibration transmissions. Full article
(This article belongs to the Section Machine Design and Theory)
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20 pages, 13413 KiB  
Article
Uncertainty Optimization of Vibration Characteristics of Automotive Micro-Motors Based on Pareto Elliptic Algorithm
by Hao Hu, Deping Wang, Yudong Wu, Jianjiao Deng, Xi Chen and Weiping Ding
Machines 2024, 12(8), 566; https://doi.org/10.3390/machines12080566 - 18 Aug 2024
Viewed by 333
Abstract
The NVH (Noise, Vibration, and Harshness) characteristics of micro-motors used in vehicles directly affect the comfort of drivers and passengers. However, various factors influence the motor’s structural parameters, leading to uncertainties in its NVH performance. To improve the motor’s NVH characteristics, we propose [...] Read more.
The NVH (Noise, Vibration, and Harshness) characteristics of micro-motors used in vehicles directly affect the comfort of drivers and passengers. However, various factors influence the motor’s structural parameters, leading to uncertainties in its NVH performance. To improve the motor’s NVH characteristics, we propose a method for optimizing the structural parameters of automotive micro-motors under uncertain conditions. This method uses the motor’s maximum magnetic flux as a constraint and aims to reduce vibration at the commutation frequency. Firstly, we introduce the Pareto ellipsoid parameter method, which converts the uncertainty problem into a deterministic one, enabling the use of traditional optimization methods. To increase efficiency and reduce computational cost, we employed a data-driven method that uses the one-dimensional Inception module as the foundational model, replacing both numerical models and physical experiments. Simultaneously, the module’s underlying architecture was improved, increasing the surrogate model’s accuracy. Additionally, we propose an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) method that utilizes adaptive reference point updating, dividing the optimization process into exploration and refinement phases based on population matching error. Comparative experiments with traditional models demonstrate that this method enhances the overall quality of the solution set, effectively addresses parameter uncertainties in practical engineering scenarios, and significantly improves the vibration characteristics of the motor. Full article
(This article belongs to the Section Electrical Machines and Drives)
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24 pages, 6900 KiB  
Article
Advanced State Estimation for Multi-Articulated Virtual Track Trains: A Fusion Approach
by Zhenggang Lu, Zehan Wang and Xianguang Luo
Machines 2024, 12(8), 565; https://doi.org/10.3390/machines12080565 - 17 Aug 2024
Viewed by 420
Abstract
The Virtual Track Train (VTT) represents an innovative urban public transportation system that combines tire-based running gears with rail transit management. Effective control of such a system necessitates precise state estimation, a task rendered complex by the multi-articulated nature of the vehicles. This [...] Read more.
The Virtual Track Train (VTT) represents an innovative urban public transportation system that combines tire-based running gears with rail transit management. Effective control of such a system necessitates precise state estimation, a task rendered complex by the multi-articulated nature of the vehicles. This study addresses the challenge by focusing on state estimation for the first unit under significant interference, introducing a fusion state estimation strategy utilizing Gaussian Process Regression (GPR) and Interacting Multiple Model (IMM) techniques. First, a joint model for the first unit is established, comprising the dynamics model as the main model and a residual model constructed based on GPR to accommodate the main model’s error. The proposed fusion strategy comprises two components: a kinematic model-based method for handling transient and high-acceleration phases, and a joint-model-based method suitable for near-steady-state and low-acceleration conditions. The IMM method is employed to integrate these two approaches. Subsequent units’ states are computed from the first unit’s state, articulation angles, and yaw rates’ filtered data. Validation through hardware-in-the-loop (HIL) simulation demonstrates the strategy’s efficacy, achieving high accuracy with an average lateral speed estimation error below 0.02 m/s and a maximum error not exceeding 0.22 m/s. Additionally, the impact on VTT control performance after incorporating state estimation is minimal, with a reduction of only 3–6%. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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14 pages, 2244 KiB  
Article
Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation
by Changmook Kang, Taehyung Lee and Jongho Shin
Machines 2024, 12(8), 564; https://doi.org/10.3390/machines12080564 - 17 Aug 2024
Viewed by 283
Abstract
The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to [...] Read more.
The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate an abnormal driving area detection system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to changes in the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but an index can be used to infer the current status of the AGV and the driving environment. The index can help us detect both unusual behavior of the AGV such as skidding or sliding, and areas with low-friction road conditions that are not confirmed by images from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to the image-only method. The overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on rough unpaved proving ground. Nine abnormal driving areas were discovered on the ground. In five of these areas, vehicles consistently exhibited abnormal driving behavior. The remaining four areas were confirmed to be affected by variables such as weather conditions and vehicle tire wear. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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19 pages, 6649 KiB  
Article
Pipeline Landmark Classification of Miniature Pipeline Robot π-II Based on Residual Network ResNet18
by Jian Wang, Chuangeng Chen, Bingsheng Liu, Juezhe Wang and Songtao Wang
Machines 2024, 12(8), 563; https://doi.org/10.3390/machines12080563 - 16 Aug 2024
Viewed by 383
Abstract
A pipeline robot suitable for miniature pipeline detection, namely π-II, was proposed in this paper. It features six wheel-leg mobile mechanisms arranged in a staggered manner, with a monocular fisheye camera located at the center of the front end. The proposed robot can [...] Read more.
A pipeline robot suitable for miniature pipeline detection, namely π-II, was proposed in this paper. It features six wheel-leg mobile mechanisms arranged in a staggered manner, with a monocular fisheye camera located at the center of the front end. The proposed robot can be used to capture images during detection in miniature pipes with an inner diameter of 120 mm. To efficiently identify the robot’s status within the pipeline, such as navigating in straight pipes, curved pipes, or T-shaped pipes, it is necessary to recognize and classify these specific pipeline landmarks accurately. For this purpose, the residual network model ResNet18 was employed to learn from the images of various pipeline landmarks captured by the fisheye camera. A detailed analysis of image characteristics of some common pipeline landmarks was provided, and a dataset of approximately 908 images was created in this paper. After modifying the outputs of the network model, the ResNet18 was trained according to the proposed datasets, and the final test results indicate that this modified network has a high accuracy rate in classifying various pipeline landmarks, demonstrating a promising application prospect of image detection technology based on deep learning in miniature pipelines. Full article
(This article belongs to the Special Issue New Localization Methods and Motion Tracking Algorithms for Mechatronic Systems, Robots and Unmanned Vehicles)
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10 pages, 7927 KiB  
Article
Double-Sided Surface Structures with Undercuts on Cold-Rolled Steel Sheets for Interlocking in Hybrid Components
by Aron Ringel, Sindokht Shayan and David Bailly
Machines 2024, 12(8), 562; https://doi.org/10.3390/machines12080562 - 16 Aug 2024
Viewed by 282
Abstract
Weight reduction strategies are essential for the transportation sector to reduce greenhouse gas emissions or extend the range of electric vehicles. In the field of lightweight assembly strategies, multi-material design offers great potential. Joining materials typically used in the automotive sector, such as [...] Read more.
Weight reduction strategies are essential for the transportation sector to reduce greenhouse gas emissions or extend the range of electric vehicles. In the field of lightweight assembly strategies, multi-material design offers great potential. Joining materials typically used in the automotive sector, such as aluminum and steel, brings challenges as conventional processes such as fusion welding are unsuitable. Therefore, new technologies can extend the design options. In previous studies, a mechanical interlocking between cold-rolled surface structures with undercuts on a steel sheet and die-cast aluminum was presented. This method has now been extended to double-sided structures for more complex applications with a joint on both sheet surfaces. Numerical simulations and validation experiments were performed to investigate the manufacturing of the double-sided structures. Furthermore, the influence of the alignment of the upper and lower structures in relation to each other on the resulting structural geometry and the rolling forces were analyzed. More advantageous geometric parameters, e.g., 24% larger undercuts, and approx. 24.1% lower forming forces at 20% height reduction were observed for a shifted alignment. However, significantly higher wear of the structured rollers occurred in the corresponding experiments. Full article
(This article belongs to the Special Issue Advances in Design and Manufacturing in Die Casting and Metal Forming)
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32 pages, 4745 KiB  
Review
In-Depth Analysis of the Processing of Nomex Honeycomb Composites: Problems, Techniques and Perspectives
by Tarik Zarrouk, Mohammed Nouari, Jamal-Eddine Salhi, Hilal Essaouini, Mohammed Abbadi, Ahmed Abbadi and Mohammed Lhassane Lahlaouti
Machines 2024, 12(8), 561; https://doi.org/10.3390/machines12080561 - 15 Aug 2024
Viewed by 442
Abstract
Nomex honeycomb composites are widely recognized for their advanced structural applications in the aerospace, automotive and defense industries. These materials are distinguished by exceptional characteristics such as thin cell walls and a hexagonal structure, as well as layers made of phenolic resins and [...] Read more.
Nomex honeycomb composites are widely recognized for their advanced structural applications in the aerospace, automotive and defense industries. These materials are distinguished by exceptional characteristics such as thin cell walls and a hexagonal structure, as well as layers made of phenolic resins and aramid fibers. However, complex machining and the maintenance of high quality at a large scale presents considerable challenges. This study provides a comprehensive review of the literature on the processing of Nomex composites, highlighting the design challenges related to processing technologies, the impact of conventional and ultrasonic processing methods, and the associated mechanical properties and microstructural topographies. Moreover, it reviews research advances in machining techniques, current challenges, and future perspectives, thereby providing valuable guidance to ensure the optimal cutting of Nomex honeycomb composite structures (NHCs). Full article
(This article belongs to the Section Material Processing Technology)
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34 pages, 16532 KiB  
Article
Design, Analysis and Application of Control Techniques for Driving a Permanent Magnet Synchronous Motor in an Elevator System
by Vasileios I. Vlachou, Dimitrios E. Efstathiou and Theoklitos S. Karakatsanis
Machines 2024, 12(8), 560; https://doi.org/10.3390/machines12080560 - 15 Aug 2024
Viewed by 391
Abstract
An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, [...] Read more.
An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, Unmanned Aerial Vehicles (UAVs), elevator systems, etc., as the main source of drive transmission. Nowadays, the elevator industry, with the evolution of magnetic materials, has turned to gearless PMSMs over geared induction motors (IMs). One of the most important elements that is given special emphasis in these applications is proper motor design in consideration of the weight and speed of the chamber to be served during operation. This paper presents a design of a high-efficiency PMSM, in which finite elements analysis (FEA) and the study of the lift operating cycle provided useful conclusions on the magnetic field of the machine in different operating states. In addition, a simulated model was compared with experimental results of test operations. Furthermore, the drive system also required the use of appropriate electrical power and controls to drive the PMSM. Especially in elevator applications, the control of the motor speed by the variable voltage variable frequency technique (VVVF) is the most common technology used to avoid endangering the safety of the passengers. Thus, suitable speed and current controllers were used for this purpose. In our research, we focused on studying different control techniques using a suitable inverter to compare the system operation in each case studied. Full article
(This article belongs to the Special Issue Optimal Design and Drive of Permanent Magnet Synchronous Motors)
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18 pages, 6676 KiB  
Article
Chatter Detection in Thin-Wall Milling Based on Multi-Sensor Fusion and Dual-Stream Residual Attention CNN
by Danian Zhan, Dawei Lu, Wenxiang Gao, Haojie Wei and Yuwen Sun
Machines 2024, 12(8), 559; https://doi.org/10.3390/machines12080559 - 15 Aug 2024
Viewed by 326
Abstract
Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion [...] Read more.
Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%. Full article
(This article belongs to the Section Advanced Manufacturing)
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21 pages, 6716 KiB  
Article
A Velocity-Adaptive MPC-Based Path Tracking Method for Heavy-Duty Forklift AGVs
by Yajun Wang, Kezheng Sun, Wei Zhang and Xiaojun Jin
Machines 2024, 12(8), 558; https://doi.org/10.3390/machines12080558 - 15 Aug 2024
Viewed by 366
Abstract
In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid [...] Read more.
In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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