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
In-Depth Exploration of Design and Analysis for PM-Assisted Synchronous Reluctance Machines: Implications for Light Electric Vehicles
Machines 2024, 12(6), 361; https://doi.org/10.3390/machines12060361 (registering DOI) - 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.
Full article
(This article belongs to the Topic Advanced Electrical Machine Design and Optimization Ⅱ)
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Open AccessArticle
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)
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.
Full article
(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.
Full article
(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.
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.).
Full article
(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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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.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Open AccessArticle
An Advanced IBVS-Flatness Approach for Real-Time Quadrotor Navigation: A Full Control Scheme in the Image Plane
by
Ahmed Alshahir, Khaled Kaaniche, Mohammed Albekairi, Shahr Alshahr, Hassen Mekki, Anis Sahbani and Meshari D. Alanazi
Machines 2024, 12(5), 350; https://doi.org/10.3390/machines12050350 - 19 May 2024
Abstract
This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image
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This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image space can lead to unpredictable movements of the robot in Cartesian space. While researchers have addressed this problem for mobile robots, quadrotors continue to face significant challenges. To tackle this issue, the adopted approach involves considering the separation of altitude control from the other variables, thus reducing the workspace. Furthermore, the movements of the quadrotor (pitch, roll, and yaw) are interdependent. Consequently, the connection between the inputs and outputs cannot be reversed. The task complexity becomes significant. To address this issue, we propose the following scenario: When the quadrotor is equipped with a downward-facing camera, flying at high altitude is sensible to spot a target. However, to minimize disturbances and conserve energy, the quadrotor needs to descend in altitude. This can result in the target being lost. The solution to this problem is a new methodology based on the principle of differential flatness, allowing the separation of altitude control from the other variables. The system first detects the target at high altitude, then plots a trajectory in the image coordinate system between the acquired image and the desired image. It is crucial to emphasize that this step is performed offline, ensuring that the image processing time does not affect the control frequency. Through the proposed trajectory planning, complying with the constraints of differential flatness, the quadrotor can follow the imposed dynamics. To ensure the tracking of the target while following the generated trajectory, the proposed control law takes the form of an Image Based Visual Servoing (IBVS) scheme. We validated this method using the RVCTOOLS environment in MATLAB. The DJI Phantom 1 quadrotor served as a testbed to evaluate, under real conditions, the effectiveness of the proposed control law. We specifically designed an electronic card to transfer calculated commands to the DJI Phantom 1 control joystick via Bluetooth. This card integrates a PIC18F2520 microcontroller, a DAC8564 digital-to-analogue converter, and an RN42 Bluetooth module. The experimental results demonstrate the effectiveness of this method, ensuring the precise tracking of the target as well as the accurate tracking of the path generated in the image coordinate system.
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(This article belongs to the Special Issue Advances in Path Planning and Autonomous Navigation)
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Open AccessArticle
Optimizing Machining Efficiency in High-Speed Milling of Super Duplex Stainless Steel with SiAlON Ceramic Inserts
by
Monica Guimarães, Victor Saciotto, Qianxi He, Jose M. DePaiva, Anselmo Diniz and Stephen Veldhuis
Machines 2024, 12(5), 349; https://doi.org/10.3390/machines12050349 - 17 May 2024
Abstract
Super duplex stainless steels (SDSSs) are widely utilized across industries owing to their remarkable mechanical properties and corrosion resistance. However, machining SDSS presents considerable challenges, particularly at high speeds. This study investigates the machinability of SDSS grade SAF 2507 (UNS S32750) under high-speed
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Super duplex stainless steels (SDSSs) are widely utilized across industries owing to their remarkable mechanical properties and corrosion resistance. However, machining SDSS presents considerable challenges, particularly at high speeds. This study investigates the machinability of SDSS grade SAF 2507 (UNS S32750) under high-speed milling conditions using SiAlON insert tools. Comprehensive analysis of key machinability indicators, including chip compression ratio, chip analysis, shear angle, tool wear, and friction conditions, reveals that lower cutting speeds optimize machining performance, reducing cutting forces and improving chip formation. Finite element analysis (FEA) corroborates the efficacy of lower speeds and moderate feed rates. Furthermore, insights into friction dynamics at the tool–chip interface are offered, alongside strategies for enhancing SDSS machining. This study revealed the critical impact of cutting speed on cutting forces, with a significant reduction in forces at cutting speeds of 950 and 1350 m/min, but a substantial increase at 1750 m/min, particularly when tool wear is severe. Furthermore, the combination of 950 and 1350 m/min cutting speeds with a 0.2 mm/tooth feed rate led to smoother chip surfaces and decreased friction coefficients, thus enhancing machining efficiency. The presence of stick–slip phenomena at 1750 m/min indicated thermoplastic instability. Optimizing machining parameters for super duplex stainless steel necessitates balancing material removal rate and surface integrity, as the latter plays an important role in ensuring long-term performance and reliability in critical applications.
Full article
(This article belongs to the Special Issue Recent Advances in Surface Integrity with Machining and Milling)
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Open AccessArticle
An Image-Based Interactive Training Method of an Upper Limb Rehabilitation Robot
by
Changlong Ye, Zun Wang, Suyang Yu and Chunying Jiang
Machines 2024, 12(5), 348; https://doi.org/10.3390/machines12050348 - 16 May 2024
Abstract
Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera
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Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Open AccessArticle
Ensuring the Abrasive Jet Machining Efficiency Using a Nozzle with a Perforated Insert
by
Vadym Baha, Ivan Pavlenko, Kamil Židek and Olaf Ciszak
Machines 2024, 12(5), 347; https://doi.org/10.3390/machines12050347 (registering DOI) - 16 May 2024
Abstract
Ejector-cleaning devices for abrasive jet machining have various practical applications. The working nozzle is one of the device’s key elements affecting the treated surface quality. There arises the necessity for new approaches to achieving an efficiency increase in abrasive jet equipment nozzles, namely
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Ejector-cleaning devices for abrasive jet machining have various practical applications. The working nozzle is one of the device’s key elements affecting the treated surface quality. There arises the necessity for new approaches to achieving an efficiency increase in abrasive jet equipment nozzles, namely their design improvement and further development of a new, relatively cheap but effective technology for their manufacturing and maintenance. This technology should allow for the high durability of nozzles without being essential for the hardness or wear resistance parameters of the material used for manufacturing. The nozzle should be designed as a long-length perforated insert to allow for radial airflow, forcing the abrasive material (river sand) from the inner walls of the nozzle’s working surface to reduce its friction with the abrasive material. This will result in new wear-out conditions, providing an essential decrease in the wear-out of a nozzle’s working surface. The article aims to develop a more effective design for the working nozzle based on the perforated insert application. The task was set to provide a more detailed experimental and theoretical study of the processes in perforated nozzles to improve their effectiveness. The research resulted in a new design for nozzles with higher efficiency.
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(This article belongs to the Section Advanced Manufacturing)
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Open AccessReview
Mathematical Complexities in Modelling Damage in Spur Gears
by
Aselimhe Oreavbiere and Muhammad Khan
Machines 2024, 12(5), 346; https://doi.org/10.3390/machines12050346 - 16 May 2024
Abstract
Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a
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Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction.
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(This article belongs to the Special Issue Intelligent Machinery Fault Diagnosis and Maintenance)
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Open AccessArticle
Determination of Mechanical Power Loss of the Output Mechanisms with Serially Arranged Rollers in Cycloidal Gears While Taking into Account Manufacturing Tolerances
by
Piotr Antoniak and Sławomir Bednarczyk
Machines 2024, 12(5), 345; https://doi.org/10.3390/machines12050345 - 16 May 2024
Abstract
Despite their complex design, cycloidal gearboxes are characterized by high efficiency. Nevertheless, due to friction, some power is lost during gearbox operation. Basically, these losses occur in two structural nodes: the cycloid gearing and the output mechanism. Since the first of these nodes
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Despite their complex design, cycloidal gearboxes are characterized by high efficiency. Nevertheless, due to friction, some power is lost during gearbox operation. Basically, these losses occur in two structural nodes: the cycloid gearing and the output mechanism. Since the first of these nodes has been well discussed in the literature, the output mechanism will be discussed in this article. The design of the output mechanism has a significant impact on mechanical power losses. There are several mechanism design solutions. One of them is a mechanism with serially arranged rollers. Three solutions that are different in design but work identically will be discussed. Due to this affinity, a single, common mathematical model will be used to determine the value of losses. As will be shown, the value of losses is directly affected by the backlash, number, and diameter of the rollers used in the output mechanism and indirectly by the ratio and eccentricity of the cycloidal gearbox. Sample calculations were carried out using the developed model of mechanical power losses in the output mechanism. This made it possible to analyze the distribution of backlash created by manufacturing tolerances. It was also shown that the backlash has a significant effect on the number of rollers involved in torque transmission, as well as on the distribution of loads, contact pressures, and mechanical power losses.
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(This article belongs to the Section Electrical Machines and Drives)
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Open AccessArticle
Objective Evaluation of Motion Cueing Algorithms for Vehicle Driving Simulator Based on Criteria Importance through Intercriteria Correlation (CRITIC) Weight Method Combined with Gray Correlation Analysis
by
Xue Jiang, Xiafei Chen, Yiyang Jiao and Lijie Zhang
Machines 2024, 12(5), 344; https://doi.org/10.3390/machines12050344 - 16 May 2024
Abstract
Perception-based fidelity evaluation metrics are crucial in driving simulators, as they play a key role in the automatic tuning, assessment, and comparison of motion cueing algorithms. Nevertheless, there is presently no unified and effective evaluation framework for these algorithms. To tackle this challenge,
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Perception-based fidelity evaluation metrics are crucial in driving simulators, as they play a key role in the automatic tuning, assessment, and comparison of motion cueing algorithms. Nevertheless, there is presently no unified and effective evaluation framework for these algorithms. To tackle this challenge, our study initially establishes a model rooted in visual–vestibular interaction and head tilt angle perception systems. We then employ metrics like the Normalized Average Absolute Difference (NAAD), Normalized Pearson Correlation (NPC), and Estimated Delay (ED) to devise an evaluation index system. Furthermore, we use a combined approach incorporating CRITIC and gray relational analysis to ascertain the weights of these indicators. This allows us to consolidate them into a comprehensive evaluation metric that reflects the overall fidelity of motion cueing algorithms. Subjective evaluation experiments validate the reasonableness and efficacy of our proposed Perception Fidelity Evaluation (PFE) method.
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(This article belongs to the Section Automation and Control Systems)
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Open AccessArticle
Electromagnetic Characterization of Permanent Magnet Eddy Current Structures Based on Backplane Distance Adjustment
by
Yipeng Wu, Teng Wang, Tao Song and Wenxiao Guo
Machines 2024, 12(5), 343; https://doi.org/10.3390/machines12050343 - 15 May 2024
Abstract
To address the problem of problematic spray design inside mining anchor-digging equipment, a switching seal using a permanent magnet eddy current drive is initially presented here. The layer model of the permanent magnet eddy current structure is established, the subdomain analysis model is
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To address the problem of problematic spray design inside mining anchor-digging equipment, a switching seal using a permanent magnet eddy current drive is initially presented here. The layer model of the permanent magnet eddy current structure is established, the subdomain analysis model is introduced, the permanent magnet eddy current structure is divided into six regions along the axial direction, and the boundary equations are established at the interfaces of each region. The vector magnetic potential equations in each region are deduced, along with the electromagnetic torque and axial force equations. The computational results are compared and analyzed with the results of finite element simulation, verifying the accuracy of the theoretical model. The design of experiments is used to verify the feasibility of the switching seal using the permanent magnet eddy current structure.
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(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)
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Open AccessArticle
A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory
by
Yong Liu, Jiaqi Liu, Han Wang, Mingshun Yang, Xinqin Gao and Shujuan Li
Machines 2024, 12(5), 342; https://doi.org/10.3390/machines12050342 - 15 May 2024
Abstract
In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components
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In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.
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(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)
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