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
Estimation of Tire Side-Slip Angles Based on the Frequency Domain Lateral Acceleration Characteristics Inside Tires
Machines 2024, 12(4), 229; https://doi.org/10.3390/machines12040229 (registering DOI) - 29 Mar 2024
Abstract
The identification and control of tire side slip angle is the key to vehicle stability control. Intelligent tire technology based on the sensing of side-slip acceleration inside the tire provides a novel method for estimating the tire side-slip angle. This study proposed a
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The identification and control of tire side slip angle is the key to vehicle stability control. Intelligent tire technology based on the sensing of side-slip acceleration inside the tire provides a novel method for estimating the tire side-slip angle. This study proposed a method to estimate the tire side-slip angle by using the frequency domain lateral acceleration of the tire. First, an intelligent tire testing system was constructed by independently developing a special rim assembly and data collector. A three-axis accelerometer was placed on the right side of the tire, and the acceleration value was acquired by using a wired method with a sampling frequency of 50 kHz. Second, based on the constructed test system, a tire side deflection test was carried out on the Flat Trac bench. Through data analysis, it was found that the lateral acceleration was in the frequency domain of 400 Hz. As the side-slip angle increased from −4° to 4°, the vibration amplitude gradually decreased. Moreover, the vibration amplitude within 0.5~2 kHz was highly correlated with the side-slip angle. Subsequently, the vibration amplitude of the lateral acceleration within 2 kHz was extracted at an interval of 20 Hz as the feature point, and a frequency domain data set FDAy3 was established together with the vertical load and tire pressure. Finally, the support vector machine (SVM) algorithm was employed to make predictions on the data set. The grid search method was utilized to find the optimal parameter values of the model penalty factor c and radial basis kernel function coefficient g, which were 1.4142 and 0.0884, respectively. The results suggested that the root mean square error of the model prediction was 0.0806°, and the maximum estimated angle deviation of the prediction was 0.4587°. Meanwhile, the optimal prediction accuracy and real-time performance were achieved when the number of feature points and the feature frequency band were 25 and within 500 Hz, respectively. The findings of this study confirm that it is feasible to estimate the tire side-slip angle based on the frequency domain lateral acceleration of the tire, which provides a new method for tire side-slip angle estimation.
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(This article belongs to the Topic Vehicle Dynamics and Control)
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Open AccessArticle
AC-Winding-Resistance Calculation of Toroidal Inductors with Solid-Round-Wire and Litz-Wire Winding Based on Complex Permeability Modeling
by
Dae-Yong Um, Seung-Ahn Chae and Gwan-Soo Park
Machines 2024, 12(4), 228; https://doi.org/10.3390/machines12040228 (registering DOI) - 28 Mar 2024
Abstract
This paper has investigated a method for calculating the frequency-dependent winding resistance of toroidal inductor windings with Litz-wire as well as solid-round wire. The modified Dowell’s model is employed to address the effectiveness for inductor windings with the low and high filling factors.
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This paper has investigated a method for calculating the frequency-dependent winding resistance of toroidal inductor windings with Litz-wire as well as solid-round wire. The modified Dowell’s model is employed to address the effectiveness for inductor windings with the low and high filling factors. To overcome the limitation of this model, especially for a winding densely wound around the core, an alternative approach based on the complex permeability and iterative calculations is proposed. For the calculated AC-resistance factor of five inductors with different numbers of turns, layers with the same wire diameters are compared with that of FEA, and the three air-core toroidal windings are manufactured and tested within the frequency where the self-resonance can be neglected. The proposed model demonstrates the versality of the AC-resistance calculation of both solid- and Litz-wire windings within an error of 15% across a wide range of frequencies up to 1 MHz, compared with FEA.
Full article
(This article belongs to the Section Electrical Machines and Drives)
Open AccessArticle
Effect of Turbulent Wind Conditions on the Dynamic Characteristics of a Herringbone Planetary Gear System of a Wind Turbine
by
Wei-qiang Zhao, Wenhui Zhao, Jie Liu and Na Yang
Machines 2024, 12(4), 227; https://doi.org/10.3390/machines12040227 - 28 Mar 2024
Abstract
Due to complex environmental factors, the gear transmission systems of wind turbines are continuously affected by large torque load excitation with periodic and random properties. This paper shares the load-sharing and dynamic characteristics of a herringbone planetary gear system applied in a wind
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Due to complex environmental factors, the gear transmission systems of wind turbines are continuously affected by large torque load excitation with periodic and random properties. This paper shares the load-sharing and dynamic characteristics of a herringbone planetary gear system applied in a wind turbine. The gear dynamic model is established using a typical lumped parameter method, in which the nonlinear transmission errors of the gear pairs and left and right-side coupling stiffness of the herringbone gears are included. With the help of the blade element momentum theory, the precise calculation of the hub load of the wind turbine, which is the external excitation of the gear system, is implemented, in which the wind shear, tower shadow, turbulent effect, and tip loss correction are taken into consideration. The nonlinear dynamic characteristics of the system are obtained using the Runge-Kutta method and then discussed. The results show that the turbulent effect plays a major role in the impact on the load-sharing characteristics, and a reasonable set of the support stiffness of rotational components can improve the load-sharing characteristics of the system. The purpose of this research is to provide some useful references in numerical modelling and methods for designers and researchers of wind turbine transmission systems.
Full article
(This article belongs to the Section Turbomachinery)
Open AccessArticle
Towards Zero-Defect Manufacturing Based on Artificial Intelligence through the Correlation of Forces in 5-Axis Milling Process
by
Itxaso Cascón-Morán, Meritxell Gómez, David Fernández, Alain Gil Del Val, Nerea Alberdi and Haizea González
Machines 2024, 12(4), 226; https://doi.org/10.3390/machines12040226 - 28 Mar 2024
Abstract
Zero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at
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Zero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces.
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(This article belongs to the Special Issue Sensors and Signal Processing in Manufacturing Processes)
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Open AccessArticle
Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation
by
Sandi Baressi Šegota, Nikola Anđelić, Jelena Štifanić and Zlatan Car
Machines 2024, 12(4), 225; https://doi.org/10.3390/machines12040225 - 28 Mar 2024
Abstract
Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the
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Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson’s correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving and , the model trained on the original, non-pruned, data achieves and . These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation.
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(This article belongs to the Special Issue Design and Control of Electrical Machines II)
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Open AccessEssay
Research on the Remaining Life Prediction Method of Rolling Bearings Based on Optimized TPA-LSTM
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Na Lei, Youfu Tang, Ao Li and Peichen Jiang
Machines 2024, 12(4), 224; https://doi.org/10.3390/machines12040224 - 27 Mar 2024
Abstract
The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss of important time series
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The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss of important time series information in the process of the life prediction of rolling bearings, which leads to a decline in prediction accuracy. Therefore, a method for predicting the remaining useful life (RUL) of rolling bearings based on the combination of temporal pattern attention mechanism (TPA) and LSTM is proposed. The method firstly combines hierarchical clustering and principal component analysis (PCA) to construct a multi-faceted and multi-scale preferred feature set reflecting the degradation information of rolling bearings, then strengthens the information correlation between hidden layers of the LSTM model through TPA and optimates the parameters of the fusion model of TPA and LSTM by using the gazelle optimization algorithm (GOA). Finally, the model is applied to the experimental data set of rolling bearing degradation. The results show that, compared with the traditional model, this method is more suitable for the remaining life prediction of rolling bearings.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
Open AccessArticle
Supercritical Operation of Bearingless Cross-Flow Fan
by
Ivana Bagaric, Daniel Steinert, Thomas Nussbaumer and Johann W. Kolar
Machines 2024, 12(4), 223; https://doi.org/10.3390/machines12040223 - 27 Mar 2024
Abstract
This paper presents a decoupled bearingless cross-flow fan (CFF) that operates at a supercritical speed, thereby increasing the maximum achievable rotational speed and fluid dynamic power. In magnetically levitated CFF rotors, the rotational speed and fan performance are limited by the bending resonance
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This paper presents a decoupled bearingless cross-flow fan (CFF) that operates at a supercritical speed, thereby increasing the maximum achievable rotational speed and fluid dynamic power. In magnetically levitated CFF rotors, the rotational speed and fan performance are limited by the bending resonance frequency. This is primarily defined by the low mechanical bending stiffness of the CFF blades, which are optimised for fluid dynamic performance, and the heavy rotor magnets on both rotor sides, which add significant mass but a minimal contribution to the overall rotor stiffness. This results in detrimental deformations of the CFF blades in the vicinity of the rotor bending resonance frequency; hence, the CFF is speed-limited to subcritical rotational speeds. The novel CFF rotor presented in this study features additional mechanical decoupling elements with low bending stiffness between the fan blades and the rotor magnets. Thus, the unbalance forces primarily deform the soft decoupling elements, which enables them to pass resonances without CFF blade damage and allows rotor operation in the supercritical speed region due to the self-centring effect of the rotor. The effects of the novel rotor design on the rotor dynamic behaviour are investigated by means of a mass-spring-damper model. The influence of different decoupling elements on the magnetic bearing is experimentally tested and evaluated, from which an optimised decoupled CFF rotor is derived. The final prototype enables a stable operation at 7000 rpm in the supercritical speed region. This corresponds to a rotational speed increase of 40%, resulting in a higher, validated fluid flow and a higher static pressure compared to the previously presented bearingless CFF without decoupling elements.
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(This article belongs to the Section Machine Design and Theory)
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Open AccessArticle
Active Torque Control for Speed Ripple Elimination: A Mechanical Perspective
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Julien Croonen, Adrien Leopold J Deraes, Jarl Beckers, Wim Devesse, Omar Hegazy and Björn Verrelst
Machines 2024, 12(4), 222; https://doi.org/10.3390/machines12040222 - 26 Mar 2024
Abstract
Torque fluctuations in drivetrains are the result of dynamic excitations and can be unfavorable for the lifetime of the system. Passive ripple suppression methods exist, such as torsional dampers and flywheels, which are often bulky and not always desired. Alternatively, performant active control
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Torque fluctuations in drivetrains are the result of dynamic excitations and can be unfavorable for the lifetime of the system. Passive ripple suppression methods exist, such as torsional dampers and flywheels, which are often bulky and not always desired. Alternatively, performant active control methods exist; however, their applicability to certain drivetrains is not covered. Therefore, this paper focuses on active control from a mechanical perspective, more specifically, drivetrain dynamics impacting active control effectiveness. A quasi-resonant controller is implemented as an active control method, and its performance and robustness are proven both in simulation on a 3-DOF mechanical model and experimentally at different excitation frequencies. The tests show that active control effectiveness is highly drivetrain-dependent. In particular, the propagation of the torque oscillation is influenced by the elastic filtering properties of the drivetrain, and the speed ripple depends on the inertial attenuation of the drivetrain. High-stiffness, low-inertia drivetrains benefit best from active control for ripple suppression because the inertial attenuation is limited, while high-stiffness elements increase the mechanical bandwidth before dynamic decoupling happens between the inertias of interest. Active control serves as a viable alternative for speed ripple reduction when drivetrain compactness is key, instead of the current passive solutions.
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(This article belongs to the Special Issue Design and Control of Electrical Drives and Electrical Machines)
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Open AccessArticle
Multi-Objective Optimization of Yokeless and Segmented Armature Machine for In-Wheel Traction Applications Based on the Taguchi Method
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Liang Su, Guangchen Wang, Yuan Gao, Pericle Zanchetta and Hengliang Zhang
Machines 2024, 12(4), 221; https://doi.org/10.3390/machines12040221 - 26 Mar 2024
Abstract
For electrical machines with complex structures, the design space of parameters can be large with high dimensions during optimization. Considering the calculation cost and time consumption, it is hard to optimize all the design parameters at the same time. Therefore, in that situation,
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For electrical machines with complex structures, the design space of parameters can be large with high dimensions during optimization. Considering the calculation cost and time consumption, it is hard to optimize all the design parameters at the same time. Therefore, in that situation, sensitivity analysis of these design parameters is usually used to sort out crucial parameters. In this paper, the sensitivity analysis-based Taguchi method is applied to optimize the axial-flux permanent magnet (AFPM) machine with yokeless and segmented armature (YASA) topology for an in-wheel traction system. According to the key parameters and their sensitivity analysis, the optimal machine scheme to meet the performance requirements can be formed. In this case study, the machine performance is improved significantly after optimization. Lastly, the experimental results verify the accuracy of the model used in this study.
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(This article belongs to the Special Issue Design, Analytical Modeling, Optimization, and Application of Motor Drives)
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Open AccessArticle
Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current
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Laszlo Szamel and Jackson Oloo
Machines 2024, 12(4), 220; https://doi.org/10.3390/machines12040220 - 26 Mar 2024
Abstract
Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator
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Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator is composed of windings that are intricately insulated from each other using materials such as enamel wire, polymer films, mica tapes, epoxy resin, varnishes, or insulating tapes. The dielectric strength of the insulation may fail over time due to several environmental factors and processes. Dielectric breakdown of the winding insulation can be caused by rapid switching of the winding current, the presence of contaminants, and thermal aging. For reliable and efficient operation of the SRMs and other electrical machines, it is necessary to take into account the physics of the winding insulation and perform appropriate diagnostics and estimations that can monitor the integrity of the insulation. This article presents the estimation problem using a Genetic Algorithm (GA)-optimized Random Forest Regressor. Empirical properties and measurable quantities in the historical data are utilized to derive temperature and leakage current estimation. The developed model is then combined with a moving average function to increase the accuracy of prediction of the stator winding temperature and leakage current. The performance of the model is compared with that of the Feedforward Neural Network and Long Short-Term Memory over the same winding temperature and leakage current historical data. The performance metrics are based on computation of the Mean Square Error and Mean Absolute Error.
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(This article belongs to the Special Issue Advancements in Control and Diagnostics for Electric Motor Drive Systems)
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Open AccessArticle
Path-Following Sliding Mode Controller for an Electric Vehicle Considering Actuator Dynamics
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Luis Arturo Torres-Romero, Riemann Ruiz-Cruz and Luis Enrique González-Jiménez
Machines 2024, 12(4), 219; https://doi.org/10.3390/machines12040219 - 26 Mar 2024
Abstract
This study introduced a novel path-following controller tailored to electric vehicles equipped with a steer-by-wire system, i.e., the steering angle of the vehicle was defined by an electrical actuator. The control objective was to force the proper steering angle of the vehicle, which
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This study introduced a novel path-following controller tailored to electric vehicles equipped with a steer-by-wire system, i.e., the steering angle of the vehicle was defined by an electrical actuator. The control objective was to force the proper steering angle of the vehicle, which permits following a desired path. The system presupposed that an external algorithm that utilized sensor data provided the lateral movement references while maintaining a steady longitudinal velocity for the vehicle. The proposed control scheme was based on a robust sliding mode steering controller to manage the vehicle’s lateral movement. Furthermore, a brushless DC (BLDC) motor was considered as the steering actuator, which was controlled by a field-oriented controller (FOC), which was based on four internal proportional–integral (PI) control loops for precise steering actuation. To assess the performance of the proposed control scheme, numerical simulations were obtained, which demonstrated its effectiveness in achieving the control objective.
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(This article belongs to the Section Vehicle Engineering)
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Open AccessReview
Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review
by
Jiwei Qu, Zhe Zhang, Zheyu Qin, Kangquan Guo and Dan Li
Machines 2024, 12(4), 218; https://doi.org/10.3390/machines12040218 - 25 Mar 2024
Abstract
The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the
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The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.
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(This article belongs to the Special Issue Current and Future Applications of Agricultural Machines Based on Intelligent Methods)
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Open AccessArticle
Novel Gesture-Based Robot Programming Approach with the Ability of Code Reuse
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Vladyslav Andrusyshyn, Kamil Židek, Vitalii Ivanov and Ján Piteľ
Machines 2024, 12(4), 217; https://doi.org/10.3390/machines12040217 - 25 Mar 2024
Abstract
Nowadays, there is a worldwide demand to create new, simpler, and more intuitive methods for the manual programming of industrial robots. Gestures can allow the operator to interact with the robot more simply and naturally, as gestures are used in everyday life. The
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Nowadays, there is a worldwide demand to create new, simpler, and more intuitive methods for the manual programming of industrial robots. Gestures can allow the operator to interact with the robot more simply and naturally, as gestures are used in everyday life. The authors have developed and tested a gesture-based robot programming approach for part-handling applications. Compared to classic manual programming methods using jogging and lead-through, the gesture control method reduced wasted time by up to 70% and reduced the probability of operator error. In addition, the proposed method compares favorably with similar works in that the proposed approach allows one to write programs in the native programming language of the robot’s controller and allows the operator to control the gripper of an industrial robot.
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(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)
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Open AccessArticle
Simulation and Experimental Study on the Effect of Edge Radius on the Cutting Condition of Carbide Inserts
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Shitao Chen, Zhiyuan Bao, Yuhong Yan, Binghai Lyu, Hongyu Chen, Wei Hang, Jinhu Wang, Wenhong Zhao, Julong Yuan and Xu Wang
Machines 2024, 12(4), 216; https://doi.org/10.3390/machines12040216 - 23 Mar 2024
Abstract
Carbide tools are extensively used in the automotive, aerospace, and marine industries. However, an unsuitable tool-edge treatment can affect the cutting performance of carbide tools. In the tool-cutting process, the cutting edge radius is one of the major factors that affect the cutting
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Carbide tools are extensively used in the automotive, aerospace, and marine industries. However, an unsuitable tool-edge treatment can affect the cutting performance of carbide tools. In the tool-cutting process, the cutting edge radius is one of the major factors that affect the cutting force, temperature, and quality. In this study, a cutting simulation model of carbide inserts was used to analyze the effect of the cutting edge radius on the cutting performance. The cutting edge radii of the inserts were prepared using shear-thickening polishing methods, followed by cutting experiments. The accuracy of the cutting simulation model was verified through cutting experiments. The simulation results showed that under low-speed cutting conditions, the cutting force and temperature tended to increase with an increase in the cutting edge radius, and the cutting temperature was less affected by the cutting edge radius. The results of the cutting force and cutting temperature obtained from the experiment and simulation were consistent; therefore, the cutting simulation model was verified to be reliable. The results indicate that modeling cutting simulation is a promising research method for predicting the cutting performance of tools.
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(This article belongs to the Section Advanced Manufacturing)
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Open AccessArticle
Study on Temperature Field Uniformity of Dynamic Induction Heating for Camshaft of Marine Diesel Engine
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Xiaona Shi, Kelong Wang, Guochao Li, Chenghao Lyu, Lei Zhao, Jianzhi Chen, Li Sun and Hengheng Wu
Machines 2024, 12(4), 215; https://doi.org/10.3390/machines12040215 - 23 Mar 2024
Abstract
This paper focuses on the study of the induction heating process of a camshaft in a marine diesel engine. A three-dimensional finite element model for dynamic induction heating is established using the finite element method of multi-physical field coupling, aiming to investigate the
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This paper focuses on the study of the induction heating process of a camshaft in a marine diesel engine. A three-dimensional finite element model for dynamic induction heating is established using the finite element method of multi-physical field coupling, aiming to investigate the temperature uniformity of the cam during this process. Three elements are analyzed in this study: the moving speed, the gap between the induction coil and the workpiece, and the width of the induction coil. These factors allow for an analysis of the temperature distribution in the thickness direction and contour line direction of the cam under various conditions. On this basis, an equivalent parameter about the temperature uniformity in the thickness direction of the cam is proposed to guide the selection of the camshaft induction heating process parameters.
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(This article belongs to the Section Advanced Manufacturing)
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Open AccessArticle
Brake Disc Deformation Detection Using Intuitive Feature Extraction and Machine Learning
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Tamás Dózsa, Péter Őri, Mátyás Szabari, Ernő Simonyi, Alexandros Soumelidis and István Lakatos
Machines 2024, 12(4), 214; https://doi.org/10.3390/machines12040214 - 22 Mar 2024
Abstract
In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the
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In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation.
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(This article belongs to the Special Issue Artificial Intelligence for Automatic Control of Vehicles)
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Open AccessArticle
Discretionary Lane-Change Decision and Control via Parameterized Soft Actor–Critic for Hybrid Action Space
by
Yuan Lin, Xiao Liu and Zishun Zheng
Machines 2024, 12(4), 213; https://doi.org/10.3390/machines12040213 - 22 Mar 2024
Abstract
This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and
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This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and uncertainty of lane-change scenarios, the functionality of autonomous lane change still faces challenges. In this research, we conducted autonomous lane-change simulations using both deep reinforcement learning (DRL) and model predictive control (MPC). Specifically, we used the parameterized soft actor–critic (PASAC) algorithm to train a DRL-based lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. We also used MPC for lane selection based on the smallest predictive car-following costs for the different lanes. For the first time, we compared the performance of DRL and MPC in the context of lane-change decisions. The simulation results indicated that, under the same reward/cost function and traffic flow, both MPC and PASAC achieved a collision rate of 0%. PASAC demonstrated a comparable performance to MPC in terms of average rewards/costs and vehicle speeds.
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(This article belongs to the Special Issue Data-Driven and Learning-Based Control for Vehicle Applications)
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Open AccessArticle
Perfect Tracking Control of Linear Sliders Using Sliding Mode Control with Uncertainty Estimation Mechanism
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Tomoya Hoshina, Takato Yamada and Mingcong Deng
Machines 2024, 12(4), 212; https://doi.org/10.3390/machines12040212 - 22 Mar 2024
Abstract
This paper aims to achieve precise position control of a stage used in semiconductor exposure apparatus. The demand for smart devices, such as smartphones, is rapidly expanding, and their performance is expected to continue to improve. To manufacture these devices, it is necessary
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This paper aims to achieve precise position control of a stage used in semiconductor exposure apparatus. The demand for smart devices, such as smartphones, is rapidly expanding, and their performance is expected to continue to improve. To manufacture these devices, it is necessary to miniaturize semiconductor devices and improve productivity. The precise control of semiconductor exposure apparatus is important for the manufacture of ultra-small semiconductor devices. The stage of semiconductor exposure apparatus uses a linear motor, and this paper performs high-precision perfect tracking control of this stage. Perfect tracking control is a control method that always follows the command value while the command value changes moment by moment, and requires high accuracy. In high-precision positioning, uncertainty in the stage model has a significant impact. Therefore, this paper proposes a method to reduce tracking errors due to the influence of uncertainty by performing uncertainty compensation using sliding mode control with the estimated value of uncertainty. The estimation of uncertainty uses a method that combines Kernel LMS with an observer. Instead of the widely used Gaussian kernel, this paper uses a generalized Gaussian kernel that allows for finer parameter settings. Furthermore, this paper proposes a method to adaptively optimize the shape parameter of the generalized Gaussian kernel. Our simulations and experiments confirm that the proposed method improves tracking performance compared to conventional sliding mode control.
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(This article belongs to the Special Issue Design and Control of Advanced Mechatronics Systems, Volume II)
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Open AccessArticle
Using the AIDA Method in the Design of New Elements for the Photovoltaic Mounting Structures
by
Vlad Andrei Ciubotariu, Cosmin Constantin Grigoras, Valentin Zichil and Bogdan Alexandru Chirita
Machines 2024, 12(3), 211; https://doi.org/10.3390/machines12030211 - 21 Mar 2024
Abstract
To address diverse challenges and accelerate the adoption of PV technology, innovative and cost-effective PV assemblies are essential. The Analysis of Interconnected Decision Areas—the AIDA method—offers a promising approach to achieving this goal by providing a structured framework for identifying, assessing, and optimizing
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To address diverse challenges and accelerate the adoption of PV technology, innovative and cost-effective PV assemblies are essential. The Analysis of Interconnected Decision Areas—the AIDA method—offers a promising approach to achieving this goal by providing a structured framework for identifying, assessing, and optimizing the design of PV assemblies. The aim is to demonstrate how AIDA can be effectively used to identify and assess potential improvements in PV assembly design, leading to the development of more efficient, cost-effective, and environmentally friendly PV systems. For this, out of 54 combinations, 10 of them were retained, so that in the end only two possible solutions obtained by applying AIDA remained. Both structures were assessed by applying FEM, analysing data regarding equivalent von Mises stresses and displacements but also the existence of stress hotspots. A design insight study was also carried out. Also, the models were first built by additive manufacturing (3D printing). These models were evaluated by a manufacturer so that the evaluation matrix and criteria satisfaction matrix could be successfully completed. Therefore, AIDA can be successfully used in solving problems in product design in the field of mounting structures for PV panels. Depending on the manufacturer’s capabilities, the intended functions can be adapted quickly, because AIDA is quite simple to apply if the data of the problem are known very well. Following the application of the FEM it was concluded that the surfaces as simple as possible are to be followed in the design of components. Also, an assessment of environmental impact was successfully undertaken by means of software assistance. The decision to use one option or another is a subjective one. If the technical data are followed, then one type of structure is the one that the manufacturer should adopt as a solution to the problem. However, if the manufacturer considers that the impact on the environment is important and dedicates resources in this direction, then a different type of structure should be adopted.
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(This article belongs to the Special Issue Selected Papers from OPROTEH 2023: 18th International Conference of Constructive Design and Technological Optimization in Machine Building)
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Open AccessArticle
A Structural Reliability Analysis Method Considering Multiple Correlation Features
by
Xiaoning Bai, Yonghua Li, Dongxu Zhang and Zhiyang Zhang
Machines 2024, 12(3), 210; https://doi.org/10.3390/machines12030210 - 21 Mar 2024
Abstract
The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability analysis that considers the joint effects of multiple correlation features. To
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The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability analysis that considers the joint effects of multiple correlation features. To portray the stress–strength correlation structure, the Copula function is utilized and the influence of the correlation degree parameter on reliability is clarified. The text describes the introduction of time-varying characteristics of structural strength and correlation parameters. A time-varying Copula is then constructed to calculate the structural reliability under the stress–strength correlation characteristics. Additionally, a time-varying hybrid Copula is constructed to characterize the intricate and correlation features of multiple failure mechanisms and components. The article proposes the variational adaptive sparrow search algorithm (VASSA) to obtain optimal parameters for the time-varying hybrid Copula. The effectiveness and accuracy of the proposed method are verified through actual cases. The results indicate that multiple correlation features significantly influence structural reliability. Incorporating multiple correlation features into the solution of structural reliability yields safer results that align with engineering practice.
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(This article belongs to the Section Machines Testing and Maintenance)
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