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
Flexible Continuum Robot System for Minimally Invasive Endoluminal Gastrointestinal Endoscopy
Machines 2024, 12(6), 370; https://doi.org/10.3390/machines12060370 (registering DOI) - 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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Open AccessArticle
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.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
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Open AccessArticle
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.
Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition))
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Open AccessArticle
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
Abstract
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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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Open AccessArticle
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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Open AccessArticle
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)
Open AccessArticle
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
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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.
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(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
Open AccessArticle
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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Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study
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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.
Full article
(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.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
Open AccessArticle
Mild Hybrid Powertrain for Mitigating Loss of Volumetric Efficiency and Improving Fuel Economy of Gasoline Vehicles Converted to Hydrogen Fueling
by
Sebastian Bibiloni, Adrian Irimescu, Santiago Martinez-Boggio, Simona Merola and Pedro Curto-Risso
Machines 2024, 12(6), 355; https://doi.org/10.3390/machines12060355 - 21 May 2024
Abstract
The pursuit of sustainable and environmentally friendly transportation has led to the exploration of alternative fuel sources, among which hydrogen stands out prominently. This work delves into the potential of hydrogen fuel for internal combustion engines (ICEs), emphasizing its capacity to ensure the
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The pursuit of sustainable and environmentally friendly transportation has led to the exploration of alternative fuel sources, among which hydrogen stands out prominently. This work delves into the potential of hydrogen fuel for internal combustion engines (ICEs), emphasizing its capacity to ensure the required performance levels while concurrently enhancing overall efficiency. The integration of a mild hybrid powertrain in a small size passenger car was considered for obtaining a twofold advantage: mitigating power loss due to low volumetric efficiency and increasing fuel economy. A comprehensive approach combining 0D/1D modeling simulations and experimental validations was employed on a gasoline-powered small size ICE, considering its conversion to hydrogen, and mild hybridization. Vehicle simulations were performed in AVL Cruise M and validated against experimental data. Various electric motors were scrutinized for a small size battery pack typical of mild hybrid vehicles. Furthermore, the paper assesses the potential range achievable with the hydrogen-powered hybrid vehicle and compares it with the range reported by the manufacturer for the original gasoline and pure electric version. In terms of global results, these modifications were found to successfully improve efficiency compared to baseline gasoline and hydrogen fueling. Additionally, performance gains were achieved, surpassing the capabilities of the original gasoline vehicle despite its intrinsic volumetric efficiency limitations when using hydrogen. Along with the conversion to hydrogen and thus zero-carbon tail-pipe emissions, incorporating a Start/Stop system, and the integration of mild hybrid technology with energy recuperation during braking, overall efficiency was enhanced by up to 30% during urban use. Furthermore, the hybridization implemented in the H2 version allows an autonomy comparable to that of the electric vehicle but with evident shorter refilling times. Specific aspects of the 48 V battery management are also scrutinized.
Full article
(This article belongs to the Special Issue Advanced Engine Energy Saving Technology)
Open AccessArticle
Magnetic Field Analysis and Thrust Verification of Solenoid Actuator Based on Subdomain Method
by
Mengkun Lu, Zhifang Yuan and Xianglie Yi
Machines 2024, 12(6), 354; https://doi.org/10.3390/machines12060354 - 21 May 2024
Abstract
In view of the problem that the output thrust of the solenoid actuator is affected by various factors and is difficult to calculate in actual working conditions, this paper proposes a semi-analytical model constructed by magnetic field subdomain method with internal and external
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In view of the problem that the output thrust of the solenoid actuator is affected by various factors and is difficult to calculate in actual working conditions, this paper proposes a semi-analytical model constructed by magnetic field subdomain method with internal and external boundary conditions in a cylindrical coordinate system for calculation, and the general solution equations of magnetic vector potential for each subdomain are derived and solved by MATLAB. Taking a push–pull electromagnet as an example, the finite element simulation and experimental comparative analysis are carried out. The correctness and applicable conditions of the subdomain method are illustrated by comparing the gradient plot of magnetic vector potential, inductance curve and electromagnetic force. It is shown that the results calculated by the subdomain method are very close to the finite element method when the magnetic saturation problem is neglected. However, when the nonlinearity of core permeability is considered, the magnetic saturation gradually deepens with the increase in current, and the error of the subdomain method calculation results gradually increases. Through simulation and experimental verification at slight magnetic saturation, the output thrust after considering the core gravity, spring force and electromagnetic force, it is shown that this method has the advantage of computational flexibility compared with the finite element method, and it is easier to write special algorithms according to various working conditions to calculate the important parameters in engineering applications.
Full article
(This article belongs to the Section Electromechanical Energy Conversion Systems)
Open AccessArticle
Validation of Ecology and Energy Parameters of Diesel Exhausts Using Different Fuel Mixtures, Consisting of Hydrogenated Vegetable Oil and Diesel Fuels, Presented at Real Market: Approaches Using Artificial Neural Network for Large-Scale Predictions
by
Jonas Matijošius, Alfredas Rimkus and Alytis Gruodis
Machines 2024, 12(6), 353; https://doi.org/10.3390/machines12060353 - 21 May 2024
Abstract
Machine learning models have been used to precisely forecast emissions from diesel engines, specifically examining the impact of various fuel types (HVO10, HVO 30, HVO40, HVO50) on the accuracy of emission forecasts. The research has revealed that models with different numbers of perceptrons
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Machine learning models have been used to precisely forecast emissions from diesel engines, specifically examining the impact of various fuel types (HVO10, HVO 30, HVO40, HVO50) on the accuracy of emission forecasts. The research has revealed that models with different numbers of perceptrons had greater initial error rates, which subsequently reached a stable state after further training. Additionally, the research has revealed that augmenting the proportion of Hydrogenated Vegetable Oil (HVO) resulted in the enhanced precision of emission predictions. The use of visual data representations, such as histograms and scatter plots, yielded significant insights into the model’s versatility across different fuel types. The discovery of these results is vital for enhancing engine performance and fulfilling environmental regulations. This study highlights the capacity of machine learning in monitoring the environment and controlling engines and proposes further investigation into enhancing models and making real-time predictive adjustments. The novelty of the research is based on the determination of the input interface (a sufficient amount of input parameters, including chemical as well as technical), which characterizes the different regimes of the diesel engine. The novelty of the methodology is based on the selection of a suitable ANN type and architecture, which allows us to predict the required parameters for a wide range of input intervals (different types of mixtures consisting of HVO and pure diesel, different loads, different RPMs, etc.).
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(This article belongs to the Special Issue Cutting-Edge Technologies and Applications in Automatic Control Systems)
Open AccessArticle
Influence of Machine Tool Operating Conditions on the Resulting Circularity and Positioning Accuracy
by
Matej Sarvas, Michal Holub, Tomas Marek, Jan Prochazka, Frantisek Bradac and Petr Blecha
Machines 2024, 12(5), 352; https://doi.org/10.3390/machines12050352 - 20 May 2024
Abstract
The operating conditions of the production process significantly influence the resulting dimensional and form accuracy of the workpiece. The operating conditions include the position of the workpiece location, with internal and external heat sources influenced not only by the machine location but also
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The operating conditions of the production process significantly influence the resulting dimensional and form accuracy of the workpiece. The operating conditions include the position of the workpiece location, with internal and external heat sources influenced not only by the machine location but also by its operation. In addition, there are the cutting conditions and the feed rate requirements of CNC machine tools. These changes, such as workpiece position, feed rates, and machine heat load, are further reflected in the ability of the machine to run at the position required and interpolate within the given tolerances of circularity. For the accuracy and repeatability of positioning, the machine was set up according to ISO 230-2 and for the circular interpolation tests according to ISO 230-4. The obtained results show the importance of attention to the appropriate setting of the operating conditions of the production process, where the knowledge of the geometric accuracy of the CNC machine tool in its working space can systematically increase the manufacturing accuracy itself or be another tool suitable for predicting the dimensional and form accuracy of workpieces.
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(This article belongs to the Special Issue Precision Manufacturing and Machine Tools)
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Open AccessArticle
Mechanism Analysis and Optimization Design of Exoskeleton Robot with Non-Circular Gear–Pentabar Mechanism
by
Guibin Wang, Maile Zhou, Hao Sun, Zhaoxiang Wei, Herui Dong, Tingbo Xu and Daqing Yin
Machines 2024, 12(5), 351; https://doi.org/10.3390/machines12050351 - 19 May 2024
Abstract
To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to
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To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to constrain the degrees of freedom of the R-para-rod mechanism. In this study, the kinematic model of a non-circular gear–five−rod mechanism is established based on motion mapping theory by obtaining the normal motion positions of the human lower limb. An optimization design software for the non-circular gear–five−rod mechanism is developed using the MATLAB 2018b visualization platform, with the non−circular active gear as the sole input variable. A set of ideal parameters is obtained through parameter adjustment and optimal parameter selection, and the corresponding trajectories are compared with human trajectories. The three−dimensional model of the mechanism is established according to the obtained parameters, and the motion simulation of the non−circular gear–five−bar mechanism demonstrates that the mechanism can better reproduce the expected human knee joint motion posture, meeting the working requirements of an exoskeleton knee robot.
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(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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