Journal Description
Machines
Machines
is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI. The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Mechanical)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.6 (2022);
5-Year Impact Factor:
2.8 (2022)
Latest Articles
Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle
Machines 2024, 12(6), 375; https://doi.org/10.3390/machines12060375 - 29 May 2024
Abstract
Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change.
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Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system’s overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance.
Full article
(This article belongs to the Special Issue New Trends in Robotics and Automation)
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Open AccessArticle
Human-Centered Design and Manufacturing of a Pressure-Profile-Based Pad for Better Car Seat Comfort
by
Alessandro Naddeo, Alfonso Morra and Rosaria Califano
Machines 2024, 12(6), 374; https://doi.org/10.3390/machines12060374 - 28 May 2024
Abstract
A car seat’s function is to support, protect, and make passengers and drivers feel comfortable during a trip. A more uniform pressure distribution and a larger contact area usually provide less discomfort. Consequently, the seat pan’s material and geometry play an essential role
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A car seat’s function is to support, protect, and make passengers and drivers feel comfortable during a trip. A more uniform pressure distribution and a larger contact area usually provide less discomfort. Consequently, the seat pan’s material and geometry play an essential role in the design process. A shaped pad was opportunely designed and realized, starting from the pressure distributions between the buttocks and the seat pan; pressure data were acquired during an initial experiment involving 41 people, representing a wide range of percentiles. The shaped pad was compared with a standard one by building a special seat with an interchangeable internal pad and testing the standard and the new seat; the second experiment involved 52 people that tested both seats. The tests were conducted to assess comfort (33 subjects were asked to be seated for 1 min each) and discomfort (19 subjects were asked to be seated for 15 min each); during the tests, pressure distribution and contact area data were gathered. The results showed that, for both tests, about 80% of the participants, among which 100% of the female sample, preferred the shaped seat pan pad. Even if the material was exactly the same, the shaped pad seemed to be softer, more comfortable, and more suited to the body’s shape than the standard one. The design methodology was demonstrated to be very useful for granting a more uniform pressure distribution and a wider contact area, i.e., higher comfort and less discomfort.
Full article
(This article belongs to the Special Issue Digital Technologies to Support Human Factors Engineering in Manufacturing System Design: Theory and Applications)
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Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework
by
Vipul Dave, Himanshu Borade, Hitesh Agrawal, Anshuman Purohit, Nandan Padia and Vinay Vakharia
Machines 2024, 12(6), 373; https://doi.org/10.3390/machines12060373 - 27 May 2024
Abstract
Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes,
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Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM.
Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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Open AccessArticle
Utilizing Reinforcement Learning to Drive Redundant Constrained Cable-Driven Robots with Unknown Parameters
by
Dianjin Zhang and Bin Guo
Machines 2024, 12(6), 372; https://doi.org/10.3390/machines12060372 - 27 May 2024
Abstract
Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters,
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Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, complicating cable coordination and impeding mechanism parameter identification. This is especially notable in CDPRs with redundant constraints, leading to cable relaxation or breakage. To tackle this challenge, this paper introduces a novel approach using reinforcement learning to drive redundant constrained cable-driven robots with uncertain parameters. Kinematic and dynamic models are established and applied in simulations and practical experiments, creating a conducive training environment for reinforcement learning. With trained agents, the mechanism is driven across 100 randomly selected parameters, resulting in a distinct directional distribution of the trajectories. Notably, the rope tension corresponding to 98% of the trajectory points is within the specified tension range. Experiments are carried out on a physical cable-driven device utilizing trained intelligent agents. The results indicate that the rope tension remained within the specified range throughout the driving process, with the end platform successfully maneuvered in close proximity to the designated target point. The consistency between the simulation and experimental results validates the efficacy of reinforcement learning in driving unknown parameters in redundant constraint-driven robots. Furthermore, the method’s applicability extends to mechanisms with diverse configurations of redundant constraints, broadening its scope. Therefore, reinforcement learning emerges as a potent tool for acquiring motion data in cable-driven mechanisms with unknown parameters and redundant constraints, effectively aiding in the reconstruction process of such mechanisms.
Full article
(This article belongs to the Special Issue Advances in Parallel Robots and Mechanisms)
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Simulating and Modelling the Safety Impact of Connected and Autonomous Vehicles in Mixed Traffic: Platoon Size, Sensor Error, and Path Choice
by
Alkis Papadoulis, Marianna Imprialou, Yuxiang Feng and Mohammed Quddus
Machines 2024, 12(6), 371; https://doi.org/10.3390/machines12060371 - 27 May 2024
Abstract
The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a
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The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety.
Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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Flexible Continuum Robot System for Minimally Invasive Endoluminal Gastrointestinal Endoscopy
by
Liping Sun and Xiong Chen
Machines 2024, 12(6), 370; https://doi.org/10.3390/machines12060370 - 26 May 2024
Abstract
This paper presents a minimally invasive surgical robot system for endoluminal gastrointestinal endoscopy through natural orifices. In minimally invasive gastrointestinal endoscopic surgery (MIGES), surgical instruments need to pass through narrow endoscopic channels to perform highly flexible tasks, imposing strict constraints on the size
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This paper presents a minimally invasive surgical robot system for endoluminal gastrointestinal endoscopy through natural orifices. In minimally invasive gastrointestinal endoscopic surgery (MIGES), surgical instruments need to pass through narrow endoscopic channels to perform highly flexible tasks, imposing strict constraints on the size of the surgical robot while requiring it to possess a certain gripping force and flexibility. Therefore, we propose a novel minimally invasive robot system with advantages such as compact size and high precision. The system consists of an endoscope, two compact flexible continuum mechanical arms with diameters of 3.4 mm and 2.4 mm, respectively, and their driving systems, totaling nine degrees of freedom. The robot’s driving system employs bidirectional ball-screw-driven motion of two ropes simultaneously, converting the choice of opening and closing of the instrument’s end into linear motion, facilitating easier and more precise control of displacement when in position closed-loop control. By means of coordinated operation of the terminal surgical tools, tasks such as grasping and peeling can be accomplished. This paper provides a detailed analysis and introduction of the system. Experimental results validate the robot’s ability to grasp objects of 3 N and test the system’s accuracy and payload by completing basic operations, such as grasping and peeling, thereby preliminarily verifying the flexibility and coordination of the robot’s operation in a master–slave configuration.
Full article
(This article belongs to the Special Issue Recent Advances in Medical Robotics)
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Efficient Simulation of the Laser-Based Powder Bed Fusion Process Demonstrated on Open Lattice Materials Fabrication
by
Harry Psihoyos and George Lampeas
Machines 2024, 12(6), 369; https://doi.org/10.3390/machines12060369 - 24 May 2024
Abstract
Strut-based or open lattice materials are a category of advanced materials used in medical and aerospace applications due to their properties, such as high strength-to-weight ratio and energy absorption capability. The most prominent method for the fabrication of lattice materials is the Laser-based
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Strut-based or open lattice materials are a category of advanced materials used in medical and aerospace applications due to their properties, such as high strength-to-weight ratio and energy absorption capability. The most prominent method for the fabrication of lattice materials is the Laser-based Powder Bed Fusion (L-PBF) additive manufacturing (AM) process, due to its ability to produce parts of complex geometries. The current work presents an efficient meso-scale finite element (FE) modeling methodology of the L-PBF process demonstrated in the fabrication of body-centered cubic (BCC) lattice materials. The modeling efficiency is gained through an adaptive mesh refinement technique, which results in accurate and efficient prediction of the temperature field during the process evolution. To examine the efficiency of the modeling method, the computational time is compared with that of a conventional FE simulation, based on the element and birth technique. The temperature history difference between the two approaches is minor but the adaptive mesh modeling requires only a small portion of the simulation time of the conventional model. In addition, the computational results present a good correlation with the available experimental measurements for various process parameters validating the presented efficient method.
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(This article belongs to the Special Issue Advancements in Emerging Additive Manufacturing Techniques for Multifunctional Sustainable Technologies)
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Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network
by
Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang and Shangteng Chang
Machines 2024, 12(6), 368; https://doi.org/10.3390/machines12060368 - 24 May 2024
Abstract
Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of
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Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.
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(This article belongs to the Section Machines Testing and Maintenance)
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Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling
by
Federica Galli, Philippe Weber, Ghaleb Hoblos, Vincent Sircoulomb, Giuseppe Fiore and Charlotte Rostain
Machines 2024, 12(6), 367; https://doi.org/10.3390/machines12060367 - 24 May 2024
Abstract
Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to
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Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for ball bearing RUL (Remaining Useful Life) estimation. The proposed methodology is composed of three main steps: HI (Health Indicator) construction, bearing diagnosis and RUL estimation. The HI is obtained by processing non-stationary vibration data with the MODWPT (Maximum Overlap Discrete Wavelet Packet Transform). After that, a degradation profile is defined and coupled with crack initiation and crack propagation fatigue models. Lastly, a MB-HMM (Hidden Markov Model) is trained to capture the bearing degradation dynamics. This latter model is used to estimate the current degradation state as well as the RUL. The obtained results show good RUL prediction capabilities. In particular, the fatigue models allowed a reduction of the ML (Machine Learning) model size, improving the algorithms training phase.
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(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))
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Mechanical Modeling of Viscous Fluid Damper with Temperature and Pressure Coupling Effects
by
Yunlong Zhang, Weizhi Xu, Shuguang Wang, Dongsheng Du and Yan Geng
Machines 2024, 12(6), 366; https://doi.org/10.3390/machines12060366 - 24 May 2024
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During long-duration dynamic loads, such as wind loads or seismic effects, the internal temperature and pressure of a damping cylinder escalate rapidly, which induce shifts in the mechanical attributes of viscous fluid dampers (VFDs). This study investigated the mechanical performance of VFD considering
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During long-duration dynamic loads, such as wind loads or seismic effects, the internal temperature and pressure of a damping cylinder escalate rapidly, which induce shifts in the mechanical attributes of viscous fluid dampers (VFDs). This study investigated the mechanical performance of VFD considering the coupling effects of temperature and pressure under long-duration loads. First, we analyzed the mechanical and energy-dissipation performances of the dampers based on the dynamic mechanical tests considering different loading frequencies, displacement amplitude, and loading cycles. The experimental results indicated that both temperature and pressure influenced the output of the dampers, and in the sealed environment of the damper pip, temperature and pressure exerted mutual influence. Furthermore, the relationship between the damping coefficient and temperature–pressure coupling effects was obtained. Subsequently, an improved mathematical model for the mechanical performance of a gap-type VFD was proposed by considering the macroscopic energy balance of the entire fluid within the damper. Finally, the accuracy of the mathematical model for VFD under long-duration dynamic loads was validated by comparing the computational results with the experimental data.
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Application of a Multi-Criterion Decision-Making Method for Solving the Multi-Objective Optimization of a Two-Stage Helical Gearbox
by
Van-Thanh Dinh, Huu-Danh Tran, Duc-Binh Vu, Duong Vu, Ngoc-Pi Vu and Anh-Tung Luu
Machines 2024, 12(6), 365; https://doi.org/10.3390/machines12060365 - 24 May 2024
Abstract
This paper provides a novel application of a multi-criterion decision-making (MCDM) method to the multi-objective optimization problem of designing a two-stage helical gearbox. This study’s goal is to identify the ideal primary design elements that increase gearbox efficiency while reducing the gearbox cross-section
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This paper provides a novel application of a multi-criterion decision-making (MCDM) method to the multi-objective optimization problem of designing a two-stage helical gearbox. This study’s goal is to identify the ideal primary design elements that increase gearbox efficiency while reducing the gearbox cross-section area. In this work, three primary design parameters were selected for investigation: the gear ratio of the first stage and the coefficients of wheel face width (CWFW) of the first and second stages. The multi-objective optimization problem was further split into two phases: phase 1 solved the single-objective optimization problem of minimizing the gap between the variable levels, and phase 2 solved the multi-objective optimization issue of identifying the ideal key design factors. Moreover, the multi-objective optimization problem was handled by the SAW method as an MCDM approach, and the weight criteria were computed using the entropy approach. This study’s significant characteristics are as follows: First, a multi-objective optimization problem was successfully solved using the MCDM approach (SAW technique) for the first time. Second, the power losses in idle motion were investigated in this work in order to determine the efficiency of a two-stage helical gearbox. From this study’s findings, the ideal values for three major design parameters can be determined for the design of a two-stage helical gearbox.
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(This article belongs to the Section Machine Design and Theory)
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Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS
by
Mohammad Sheikhsamad, Raúl Suárez and Jan Rosell
Machines 2024, 12(6), 364; https://doi.org/10.3390/machines12060364 - 23 May 2024
Abstract
Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human
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Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper.
Full article
(This article belongs to the Special Issue Advances in Robotic Manipulation through Artificial Intelligence and Innovative Gripping Concepts)
Open AccessArticle
Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles
by
Xiaohu Geng, Weidong Liu, Xiangyu Liu, Guanzheng Wen, Maohan Xue and Jie Wang
Machines 2024, 12(6), 363; https://doi.org/10.3390/machines12060363 - 23 May 2024
Abstract
When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical
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When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical transmission (AMT) clutches and proposes an optimal control of the clutch torque for launching heavy-duty vehicles. Firstly, a fuzzy neural network (FNN)-based vehicle launching states recognition (LSR) system is designed for distinguishing the driver’s launching intention and the vehicle’s launching equivalent moment of resistance. Secondly, jerk, friction work, and launching reserve power are taken as the performance indexes for clutch torque optimization, the weight coefficients of each performance index are adjusted according to the LSR results, and the optimal clutch torque is solved by using the minimum value principle based on the shooting method. Finally, simulations and tests are conducted to validate the strategy of optimizing clutch torque, and the impact of torque optimization on the position change during the engagement process is analyzed. The results indicate that under different driver’s intentions, vehicle masses, and road gradient conditions, the jerk, friction work, and slipping time of heavy vehicles during the launching process are improved by applying the optimization strategy.
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(This article belongs to the Section Vehicle Engineering)
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New Health Indicator Construction and Fault Detection Network for Rolling Bearings via Convolutional Auto-Encoder and Contrast Learning
by
Dongdong Wu, Da Chen and Gang Yu
Machines 2024, 12(6), 362; https://doi.org/10.3390/machines12060362 - 23 May 2024
Abstract
As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fault detection play a crucial role in
[...] Read more.
As one of the most important components in rotating machinery, if bearings fail, serious disasters may occur. Therefore, the remaining useful life (RUL) prediction of bearings is of great significance. Health indicator (HI) construction and early fault detection play a crucial role in data-driven RUL prediction. Unfortunately, most existing HI construction methods require prior knowledge and preset trends, making it difficult to reflect the actual degradation trend of bearings. And the existing early fault detection methods rely on massive historical data, yet manual annotation is time-consuming and laborious. To address the above issues, a novel deep convolutional auto-encoder (CAE) based on envelope spectral feature extraction is developed in this work. A sliding value window is defined in the envelope spectrum to obtain initial health indicators, which are used as preliminary labels for model training. Subsequently, CAE is trained by minimizing the composite loss function. The proposed construction method can reflect the actual degradation trend of bearings. Afterwards, the autoencoder is pre-trained through contrast learning (CL) to improve its discriminative ability. The model that has undergone offline pre-training is more sensitive to early faults. Finally, the HI construction method is combined with the early fault detection method to obtain a comprehensive network for online health assessment and fault detection, thus laying a solid foundation for subsequent RUL prediction. The superiority of the proposed method has been verified through experiments.
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(This article belongs to the Section Machines Testing and Maintenance)
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In-Depth Exploration of Design and Analysis for PM-Assisted Synchronous Reluctance Machines: Implications for Light Electric Vehicles
by
Cristina Adăscăliței, Radu Andrei Marțiș, Petros Karaisas and Claudia Steluța Marțiș
Machines 2024, 12(6), 361; https://doi.org/10.3390/machines12060361 - 23 May 2024
Abstract
In electric or hybrid vehicles’ propulsion systems, Permanent Magnet-Assisted Synchronous Reluctance Machines represent a viable alternative to Permanent Magnet Synchronous Machines. Based on previous research work, the present paper proposes, designs, and optimizes two ferrite PMaSynRM topologies, analyzed against a reference machine (also
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In electric or hybrid vehicles’ propulsion systems, Permanent Magnet-Assisted Synchronous Reluctance Machines represent a viable alternative to Permanent Magnet Synchronous Machines. Based on previous research work, the present paper proposes, designs, and optimizes two ferrite PMaSynRM topologies, analyzed against a reference machine (also PMaSynRM) with improved torque ripple content, based on similar specifications and dimensional constraints. Considering the trend of increasing the DC voltage level in electric and hybrid vehicles, the optimal topology is included in an analysis of the DC voltage level impact on the design and performances of PMSynRM.
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(This article belongs to the Topic Advanced Electrical Machine Design and Optimization Ⅱ)
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Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks
by
Xiangyang Wu, Renyong Tian, Yuncong Lei, Hongli Gao and Yanjiang Fang
Machines 2024, 12(6), 360; https://doi.org/10.3390/machines12060360 - 22 May 2024
Abstract
In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each axis during each operation may vary. There may even be out-of-control situations where the robot does not run according to the set welding
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In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each axis during each operation may vary. There may even be out-of-control situations where the robot does not run according to the set welding trajectory, which may cause the robot and equipment to collide and be damaged. Therefore, a real-time judgment method for the welding robot trajectory is proposed. Firstly, multiple sets of axis data are obtained by running the welding robot, and the phase of the data is aligned by using a proposed algorithm, and then the Kendall correlation coefficient is used to identify and remove weak axis data. Secondly, the mean of multiple sets of axis data with strong correlation is calculated as the standard trajectory, and the trajectory threshold of the robot is set using the μ ± nσ method based on the trajectory deviation judgment sensitivity. Finally, the absolute difference between the real-time axis trajectory and the standard trajectory is used to determine the deviation of the running trajectory. When the deviation reaches the threshold, a forewarning starts. When the deviation exceeds the threshold + σ, the robot is stopped. Take the six-axis welding robot as an example, by collecting the axis data of the robot running multiple times under the same conditions, it is proved that the proposed method can accurately warn the deviation of the running trajectory. The research results have important practical value for the prevention of welding robot accidents in industrial production.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Development of a New Lightweight Multi-Channel Micro-Pipette Device
by
Xifa Zhao, Zhengxiong Yuan, Lin Lin, Chaowen Zheng and Hui You
Machines 2024, 12(6), 359; https://doi.org/10.3390/machines12060359 - 22 May 2024
Abstract
In this study, to improve the efficiency of the pipetting workstation and reduce the impact of the pipetting device on the stability performance of the workstation, a novel fully automatic pipetting method is proposed. Based on this method, a lightweight, multifunctional, and quantitative
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In this study, to improve the efficiency of the pipetting workstation and reduce the impact of the pipetting device on the stability performance of the workstation, a novel fully automatic pipetting method is proposed. Based on this method, a lightweight, multifunctional, and quantitative twelve-channel pipetting device was designed. This device can achieve simultaneous quantitative liquid absorption for twelve channels and sequential interval liquid discharge for each channel. Initially, the overall functional requirements were determined, and with the aim of a lightweight design, the total weight of the device was controlled to be within 580 g through a reasonable structural design, material selection, and choice of driving source. The device’s overall dimensions are 170 mm × 70 mm × 180 mm (length × width × height), with a micropipetting volume ranging between 1.3 L and 1.4 L. Subsequently, factors affecting liquid suction stability were experimentally analyzed, and appropriate pipetting parameters were selected. The stability performance of this pipetting method during prolonged operation was investigated. Finally, the twelve-channel pipetting device was validated through experiments, demonstrating results that meet the national standards for the stability of a pipetting device. In summary, the device designed in this study exhibits novel design features, low cost, and modularity, thus demonstrating promising potential for applications in high-speed micro-volume pipetting.
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(This article belongs to the Section Machine Design and Theory)
Open AccessArticle
Study on the Load-Bearing Characteristics Analysis Model of Non-Pneumatic Tire with Composite Spokes
by
Muyang Sun, Weidong Liu, Qiushi Zhang, Yuxi Chen, Jianshan Jiang and Xiaotong Liu
Machines 2024, 12(6), 358; https://doi.org/10.3390/machines12060358 - 22 May 2024
Abstract
This study aims to analyze the load-bearing characteristics of non-pneumatic tires with composite spokes using experimental and finite element simulation methods and to establish a mechanical analysis model based on the Timoshenko beam theory. Subsequently, experiments were conducted on carbon fiber-reinforced plastics and
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This study aims to analyze the load-bearing characteristics of non-pneumatic tires with composite spokes using experimental and finite element simulation methods and to establish a mechanical analysis model based on the Timoshenko beam theory. Subsequently, experiments were conducted on carbon fiber-reinforced plastics and rubbers to establish the corresponding constitutive model. A finite element model of the non-pneumatic tires with composite spokes was also developed. The main structural and material parameters were selected, and their correlation with the vertical stiffness of the non-pneumatic tires with composite spokes was studied using response surface methodology. The stiffness characteristics of the composite spokes were simplified, and a load-bearing characteristic analysis model was established. The results indicated that among the parameters of the reinforcement plate structure and rubber, the constitutive parameter C10 of the rubber in the spokes had the greatest impact, with a comprehensive influence value of 319.83 N/mm. Under a load of 5000 N, the load-bearing characteristic analysis model results were consistent with those of the finite element simulation, with a maximum relative error of 7.49%. The proposed load-bearing characteristic analysis model can assist in the rapid design and performance prediction of non-pneumatic tires with composite spokes.
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(This article belongs to the Section Vehicle Engineering)
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Open AccessArticle
Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study
by
Nicolò Oreste Pinciroli Vago, Francesca Forbicini and Piero Fraternali
Machines 2024, 12(6), 357; https://doi.org/10.3390/machines12060357 - 22 May 2024
Abstract
Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the
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Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three datasets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared on multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure.
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(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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Open AccessArticle
Performance Prediction of the Elastic Support Structure of a Wind Turbine Based on Multi-Task Learning
by
Chengshun Zhu, Jie Qi, Zhizhou Lu, Shuguang Chen, Xiaoyan Li and Zejian Li
Machines 2024, 12(6), 356; https://doi.org/10.3390/machines12060356 - 21 May 2024
Abstract
The effectiveness of a wind turbine elastic support in reducing vibrations significantly impacts the unit’s lifespan. During the structural design process, it is necessary to consider the influence of structural design parameters on multiple performance indicators. While neural networks can fit the relationships
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The effectiveness of a wind turbine elastic support in reducing vibrations significantly impacts the unit’s lifespan. During the structural design process, it is necessary to consider the influence of structural design parameters on multiple performance indicators. While neural networks can fit the relationships between design parameters on multiple performance indicators, traditional modeling methods often isolate multiple tasks, hindering the learning on correlations between tasks and reducing efficiency. Moreover, acquiring training data through physical experiments is expensive and yields limited data, insufficient for effective model training. To address these challenges, this research introduces a data generation method using a digital twin model, simulating physical conditions to generate data at a lower cost. Building on this, a Multi-gate Mixture-of-Experts multi-task prediction model with Long Short-Term Memory (MMoE-LSTM) module is developed. LSTM enhances the model’s ability to extract nonlinear features from data, improving learning. Additionally, a dynamic weighting strategy, based on coefficient of variation weighting and ridge regression, is employed to automate loss weight adjustments and address imbalances in multi-task learning. The proposed model, validated on datasets created using the digital twin model, achieved over 95% predictive accuracy for multiple tasks, demonstrating that this method is effective.
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(This article belongs to the Section Machines Testing and Maintenance)
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