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
Research on Collaboration Motion Planning Method for a Dual-Arm Robot Based on Closed-Chain Kinematics
Machines 2024, 12(6), 387; https://doi.org/10.3390/machines12060387 (registering DOI) - 4 Jun 2024
Abstract
Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established
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Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established for the characteristic trajectories between the workpiece coordinate system and various other coordinate systems. Subsequently, the position and orientation curves of the working trajectory are discretized to facilitate the controller’s execution. Furthermore, an analysis is conducted of the closed-chain kinematics relationship between two arms of the robot and a pose-calibration method based on a reference coordinate system is introduced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning methodology. Simulations and experiments demonstrate that the proposed approach enables effective and collaborative task planning for dual-arm robots. Moreover, joint angle and angular velocity curves corresponding to the motion trajectory exhibit smoothness, reducing joint impacts.
Full article
(This article belongs to the Section Automation and Control Systems)
Open AccessArticle
Analysis of Vibration Characteristics and Influencing Factors of Complex Tread Pattern Tires Based on Finite Element Method
by
Mengdi Xu, Yunfei Ge, Xianbin Du and Zhaohong Meng
Machines 2024, 12(6), 386; https://doi.org/10.3390/machines12060386 - 4 Jun 2024
Abstract
The vibration of the tires significantly impacts a vehicle’s ride comfort and noise level; however, the current analysis of tire vibration characteristics often involves excessive simplification in their models, leading to a reduction in model accuracy. To analyze the tire vibrational properties and
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The vibration of the tires significantly impacts a vehicle’s ride comfort and noise level; however, the current analysis of tire vibration characteristics often involves excessive simplification in their models, leading to a reduction in model accuracy. To analyze the tire vibrational properties and the influence of its design and service conditions, a combined modeling technology was developed to construct a three-dimensional (3D) finite element model of a 205/55R16 specification radial tire with intricate tread patterns. The accuracy and reliability of the simulation model was verified through vibration modal tests. Based on the vibration mode theory, the Lanczos method provided by ABAQUS was adopted to analyze the modal characteristics of the tire under free inflation and grounded conditions, and the effects of different inflation pressures, loads, operating conditions, and belt cord angles on the tire vibration characteristics were analyzed. The results indicate that grounding constraints will suppress the low order radial modal frequency of the tire and enhance the lateral modal frequency. The higher the order of the tire vibration mode, the greater the impact of inflation pressure. As the operating conditions change, the modal frequencies of all directions have the same trend of change, and as the ground load increases, the tire is prone to misalignment at lower lateral frequencies. The radial and lateral grounding modes of the tire are slightly affected by the change of the cord angle in the belt layer, but the circumferential grounding frequency decreases as the belt layer angle increases. These research findings offer a crucial foundation for the structural design of complex tread pattern tires, and also serve as a reference for addressing vibration and comfort issues encountered in the tire matching process.
Full article
(This article belongs to the Section Machine Design and Theory)
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Open AccessArticle
Development of a Multi-Robot System for Pier Construction
by
Hyo-Gon Kim, Ji-Hyun Park, Jong-Chan Kim, Jeong-Hwan Hwang, Jeong-Woo Park, In-Gyu Park, Hyo-Jun Lee, Kyoungseok Noh, Young-Ho Choi and Jin-Ho Suh
Machines 2024, 12(6), 385; https://doi.org/10.3390/machines12060385 - 4 Jun 2024
Abstract
The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support
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The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support the bridge deck. Pier construction involves a series of tasks including rebar connection, formwork installation, concrete pouring, formwork dismantling, and formwork reinstallation. These activities require working at heights, presenting a significant risk of falls. If bridge construction could be performed remotely using robots instead of relying on human labor, it would greatly contribute to the safety of bridge construction. This paper proposes a multi-robot system capable of remote operation and automation for rebar structure connection, concrete pouring, and concrete vibrating tasks in pier construction. The proposed multi-robot system for pier construction is composed of three robot systems. Each robot system consists of a robot arm mounted on a mobile robot that can move along rails. And to apply the proposed system to a construction site, it is essential to implement a compliance control algorithm that adapts to external forces. In this paper, we propose an admittance control that takes into account the weight of the tool for the compliance control of the proposed robot, which performs tasks by switching between various construction tools of different weights. Furthermore, we propose a synchronization control method for the multi-robot system to connect reinforcing structures. We validated the proposed algorithm through simulation. Furthermore, we developed a prototype of the proposed system to verify the feasibility of the suggested hardware design and control.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Open AccessArticle
A Thorough Procedure to Design Surface-Mounted Permanent Magnet Synchronous Generators
by
Gustavo Garbelini de Menezes, Narco Afonso Ravazzoli Maciejewski, Elissa Soares de Carvalho and Thiago de Paula Machado Bazzo
Machines 2024, 12(6), 384; https://doi.org/10.3390/machines12060384 - 4 Jun 2024
Abstract
This paper sets forth a thorough procedure to design surface-mounted permanent magnet synchronous generators. Since synchronous generators generate the majority of electrical energy, their relevance in society nowadays is substantial. As a consequence, the methodology to design these electrical machines also holds great
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This paper sets forth a thorough procedure to design surface-mounted permanent magnet synchronous generators. Since synchronous generators generate the majority of electrical energy, their relevance in society nowadays is substantial. As a consequence, the methodology to design these electrical machines also holds great importance. However, even though a considerable amount of works addresses the matter, it is difficult to find a complete and thoroughly explained design procedure. The proposed method is based on analytical equations to fully consider PM generator fundamentals with a few simplifications, which implies in a considerable number of design equations and parameters. Differently from most papers on the design of PM synchronous generators, a significant level of detail and explanation is presented, all design choices are discussed, and the suggested ranges for the design parameters are shown. This results in a straightforward procedure that allows non-experienced designers to easily replicate the results and effectively enhance the comprehension of permanent magnet synchronous machines, and provides a guideline for researchers from other fields who may need to understand and perform a synchronous generator design. To show the effectiveness of the proposed design procedure, a PM generator is designed, and the results are compared with a finite element simulation, showing good accuracy.
Full article
(This article belongs to the Special Issue Research in Design and Analysis of Permanent Magnet Synchronous Machines)
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Open AccessArticle
An Improved Fault Diagnosis Method for Rolling Bearings Based on 1D_CNN Considering Noise and Working Condition Interference
by
Kai Huang, Linbo Zhu, Zhijun Ren, Tantao Lin, Li Zeng, Jin Wan and Yongsheng Zhu
Machines 2024, 12(6), 383; https://doi.org/10.3390/machines12060383 - 3 Jun 2024
Abstract
Rolling bearings are prone to failure due to the complexity and serious operational environment of rotating equipment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has become an effective tool to ensure the reliable operation of rolling bearings. However, interference caused by
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Rolling bearings are prone to failure due to the complexity and serious operational environment of rotating equipment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has become an effective tool to ensure the reliable operation of rolling bearings. However, interference caused by environmental noise and variable working conditions can affect the data. To solve this problem, we propose an improved fault diagnosis method called deep convolutional neural network based on multi-scale features and mutual information (MMDCNN). In our approach, a multi-scale convolutional layer is placed at the front end of a 1D_CNN to maximize the retention of the multi-scale initial features. Meanwhile, the key fault features are further enhanced adaptively by introducing a self-attention mechanism. Then, the composite loss function is constructed by maximizing mutual information as an auxiliary loss based on cross-entropy loss; thus, the proposed method can extract robust fault features with high generalization performance. To demonstrate the superiority of MMDCNN, we compared the performance of our scheme with several existing deep learning models on two datasets. The results show that the proposed model successfully achieves bearing fault diagnosis with interference from noise and variable working conditions, possessing a powerful fault feature extraction capability.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
Open AccessArticle
Establishment and Analysis of Load Spectrum for Bogie Frame of High-Speed Train at 400 km/h Speed Level
by
Guidong Tao, Zhiming Liu, Chengxiang Ji and Guangxue Yang
Machines 2024, 12(6), 382; https://doi.org/10.3390/machines12060382 - 3 Jun 2024
Abstract
The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs,
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The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, especially when the design and operational speeds exceed 400 km/h, the applicability of current international standards is uncertain. The load spectrum serves as the foundation for structural reliability design and fatigue evaluation. In this paper, the measured loads of the bogie frame of a CR400AF high-speed train on the Beijing–Shanghai high-speed railway are obtained, and the time-domain characteristic of the measured loads is analyzed under different operating conditions. Then, through the Weibull distribution of three parameters, the Weibull parameters at the 450 km/h speed level are fitted, and the maximum load and cumulative frequency under the speed level are derived. Finally, the load spectrum of the bogie frame at the 450 km/h speed level is established, which provides a more realistic load condition for accurately evaluating the fatigue strength of bogie frames at higher speed levels.
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(This article belongs to the Section Vehicle Engineering)
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Open AccessArticle
Flexspline Pitch Deviation Rapid Measurement Method Using Offset Point Laser Sensors
by
Xiaoyi Wang, Kunlei Zheng, Longyuan Xiao, Chengxiang Zhao, Mingkang Liu, Dongjie Zhu, Tianyang Yao and Zhaoyao Shi
Machines 2024, 12(6), 381; https://doi.org/10.3390/machines12060381 - 3 Jun 2024
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Flexsplines in harmonic gear reducers are usually characterized by a large number of teeth, small modulus, and poor stiffness, which makes them difficult to measure using conventional gear measuring centers. In order to efficiently evaluate the quality of flexsplines in harmonic gear reducers,
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Flexsplines in harmonic gear reducers are usually characterized by a large number of teeth, small modulus, and poor stiffness, which makes them difficult to measure using conventional gear measuring centers. In order to efficiently evaluate the quality of flexsplines in harmonic gear reducers, a rapid measurement method for flexspline pitch using offset point laser sensors (PLS) is proposed. This paper investigates the principle of measuring the tooth flank of the flexspline under the offset of the PLS, establishes a model for collecting and analyzing gear surface data, builds an experimental system, calibrates the six pose parameters of the sensor using the geometric features of the flexspline’s outer circular surface, and completes the reconstruction of the left and right gear surfaces of the flexspline based on the measured data. In the experiment, the gear surface obtained by the proposed method is largely consistent with that measured by the video imaging method, and the repeatability of both single pitch deviation and cumulative pitch deviation is within ±3 µm.
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Open AccessReview
A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems
by
Syeda Sitara Wishal Fatima and Afshin Rahimi
Machines 2024, 12(6), 380; https://doi.org/10.3390/machines12060380 - 1 Jun 2024
Abstract
Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries.
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Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.
Full article
(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation)
Open AccessArticle
Developmental and Experimental Study on a Double-Excitation Ultrasonic Elliptical Vibration-Assisted Cutting Device
by
Gaofeng Hu, Wendong Xin, Min Zhang, Junti Lu, Yanjie Lu, Shengming Zhou and Kai Zheng
Machines 2024, 12(6), 379; https://doi.org/10.3390/machines12060379 - 1 Jun 2024
Abstract
Ultrasonic elliptical vibration-assisted cutting (UEVC) has been successfully applied in the precision and ultra-precision machining of hard and brittle materials due to its advantages of a low cutting force and minimal tool wear. This study developed a novel double-excitation ultrasonic elliptic vibration-assisted cutting
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Ultrasonic elliptical vibration-assisted cutting (UEVC) has been successfully applied in the precision and ultra-precision machining of hard and brittle materials due to its advantages of a low cutting force and minimal tool wear. This study developed a novel double-excitation ultrasonic elliptic vibration-assisted cutting (D-UEVC) device by coupling ultrasonic vibrations in orthogonal dual paths. A two-degree-of-freedom vibration system of the D-UEVC was modeled, form which the elliptical trajectory of the end under different phase angle φ values was derived. The initial dimensions of the D-UEVC device were obtained through theoretical calculations. Subsequently, with the aid of finite element analysis methods, structural dynamic analysis of the device was conducted to obtain the elliptical vibration trajectory under different phase differences of the excitation source. In order to verify the cutting trajectory and cutting performance of the D-UEVC device, a prototype of the device was developed, and a series of vibration performance tests as well as the Inconel 718 cutting experiment were conducted. The experimental results illustrated that the D-UEVC device can achieve the elliptical vibration trajectory at the tool tip with a resonant frequency of 36.5 KHz. The adjustable elliptical vibration trajectories covered a range of ±4 μm in the axial and radial directions. Compared with the surface roughness Ra = 0.36 μm under the conventional cutting, the surface roughness of Inconel 718 under D-UEVC was Ra = 0.215 μm. Thus, the surface quality can be significant improved by utilizing the D-UEVC device.
Full article
(This article belongs to the Section Advanced Manufacturing)
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Open AccessArticle
Kinematic Modeling and Performance Analysis of a 5-DoF Robot for Welding Applications
by
Selvaraj Karupusamy, Sundaram Maruthachalam and Balaji Veerasamy
Machines 2024, 12(6), 378; https://doi.org/10.3390/machines12060378 - 1 Jun 2024
Abstract
Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated
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Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated robots are popular due to their diverse applications, this research proposes a novel 5-DoF robot design for industrial automation, featuring a combination of three prismatic and two revolute (2R) joints, and analyzes its workspace. The proposed techno-economically efficient design offers control over the robot manipulator to achieve any reachable position and orientation within its workspace, replacing traditional 6-DoF robots. The kinematic model integrates both parallel and serial manipulator principles, combining a Cartesian mechanism with rotational mechanisms. Simulations demonstrate the end effector’s flexibility for tasks like welding, additive manufacturing, and material inspections, achieving the desired position and orientation. The research encompasses the design of linear and rotational actuators, kinematic modeling, Human–Machine Interface (HMI) development, and welding application integration. The developed robot demonstrates a superior performance and user-friendliness in welding. The experimental work validates the design’s optimized joint trajectories, efficient power usage, singularity avoidance, easy access in application areas, and reduced costs due to fewer actuators.
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(This article belongs to the Section Automation and Control Systems)
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Open AccessArticle
Analytical Modeling of Eddy Current Losses and Thermal Analysis of Non-Uniform-Air-Gap Combined-Pole Permanent Magnet Motors for Electric Vehicles
by
Shilun Ma, Jianwei Ma, Keqi Chen and Changwei Li
Machines 2024, 12(6), 377; https://doi.org/10.3390/machines12060377 - 31 May 2024
Abstract
In order to solve the problem of large eddy current losses and high temperature rises caused by a large number of permanent magnets, a new type of combined-magnetic-pole permanent magnet motor is proposed in this paper. The sinusoidally distributed subdomain model of a
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In order to solve the problem of large eddy current losses and high temperature rises caused by a large number of permanent magnets, a new type of combined-magnetic-pole permanent magnet motor is proposed in this paper. The sinusoidally distributed subdomain model of a non-uniform-air-gap rotor was established using the Laplace equation, and the analytical expression of eddy current losses in the rotor in a uniform air gap and non-uniform air gap was derived. The effect of the rotor’s eccentricity on eddy current losses was obtained. According to the characteristics of the distributed winding of the non-uniform-air-gap combined-pole permanent magnet motor, an equivalent treatment was performed to obtain the equivalent thermal conductivity value; to establish an equivalent thermal network model of the motor; determine the temperature of each component of the motor; and verify the correctness of the thermal network model through magnetothermal bidirectional coupling. Finally, an experimental platform was set up to carry out temperature rise experiments on the two prototypes. The experimental results show that a non-uniform-air-gap rotor structure can effectively reduce a rotor’s eddy current losses and motor temperature rise, as well as verify the accuracy of the analytical model’s calculation results.
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(This article belongs to the Section Vehicle Engineering)
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Open AccessReview
Nonlinear Passive Observer for Motion Estimation in Multi-Axis Precision Motion Control
by
Hector Gutierrez and Dengfeng Li
Machines 2024, 12(6), 376; https://doi.org/10.3390/machines12060376 - 30 May 2024
Abstract
A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-axis high-precision motion control stage is presented. The proposed nonlinear estimation strategy is developed based on a Lyapunov stability analysis, which proves that the NPO is stable. Three test cases
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A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-axis high-precision motion control stage is presented. The proposed nonlinear estimation strategy is developed based on a Lyapunov stability analysis, which proves that the NPO is stable. Three test cases are used to investigate the performance of the proposed observer. Experimental results are given to demonstrate the performance of the proposed NPO in accurately estimating time-varying velocity during alignment, reciprocating motion, and multi-axis motion in high-precision motion control applications.
Full article
(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)
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Open AccessArticle
Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle
by
Yelin He, Jian Wu, Fuxing Xu, Xin Liu, Shuai Wang and Guanjie Cui
Machines 2024, 12(6), 375; https://doi.org/10.3390/machines12060375 - 29 May 2024
Abstract
Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change.
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Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system’s overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance.
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(This article belongs to the Special Issue New Trends in Robotics and Automation)
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Open AccessArticle
Human-Centered Design and Manufacturing of a Pressure-Profile-Based Pad for Better Car Seat Comfort
by
Alessandro Naddeo, Alfonso Morra and Rosaria Califano
Machines 2024, 12(6), 374; https://doi.org/10.3390/machines12060374 - 28 May 2024
Abstract
A car seat’s function is to support, protect, and make passengers and drivers feel comfortable during a trip. A more uniform pressure distribution and a larger contact area usually provide less discomfort. Consequently, the seat pan’s material and geometry play an essential role
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A car seat’s function is to support, protect, and make passengers and drivers feel comfortable during a trip. A more uniform pressure distribution and a larger contact area usually provide less discomfort. Consequently, the seat pan’s material and geometry play an essential role in the design process. A shaped pad was opportunely designed and realized, starting from the pressure distributions between the buttocks and the seat pan; pressure data were acquired during an initial experiment involving 41 people, representing a wide range of percentiles. The shaped pad was compared with a standard one by building a special seat with an interchangeable internal pad and testing the standard and the new seat; the second experiment involved 52 people that tested both seats. The tests were conducted to assess comfort (33 subjects were asked to be seated for 1 min each) and discomfort (19 subjects were asked to be seated for 15 min each); during the tests, pressure distribution and contact area data were gathered. The results showed that, for both tests, about 80% of the participants, among which 100% of the female sample, preferred the shaped seat pan pad. Even if the material was exactly the same, the shaped pad seemed to be softer, more comfortable, and more suited to the body’s shape than the standard one. The design methodology was demonstrated to be very useful for granting a more uniform pressure distribution and a wider contact area, i.e., higher comfort and less discomfort.
Full article
(This article belongs to the Special Issue Digital Technologies to Support Human Factors Engineering in Manufacturing System Design: Theory and Applications)
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Open AccessArticle
Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework
by
Vipul Dave, Himanshu Borade, Hitesh Agrawal, Anshuman Purohit, Nandan Padia and Vinay Vakharia
Machines 2024, 12(6), 373; https://doi.org/10.3390/machines12060373 - 27 May 2024
Abstract
Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes,
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Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM.
Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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Open AccessArticle
Utilizing Reinforcement Learning to Drive Redundant Constrained Cable-Driven Robots with Unknown Parameters
by
Dianjin Zhang and Bin Guo
Machines 2024, 12(6), 372; https://doi.org/10.3390/machines12060372 - 27 May 2024
Abstract
Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters,
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Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, complicating cable coordination and impeding mechanism parameter identification. This is especially notable in CDPRs with redundant constraints, leading to cable relaxation or breakage. To tackle this challenge, this paper introduces a novel approach using reinforcement learning to drive redundant constrained cable-driven robots with uncertain parameters. Kinematic and dynamic models are established and applied in simulations and practical experiments, creating a conducive training environment for reinforcement learning. With trained agents, the mechanism is driven across 100 randomly selected parameters, resulting in a distinct directional distribution of the trajectories. Notably, the rope tension corresponding to 98% of the trajectory points is within the specified tension range. Experiments are carried out on a physical cable-driven device utilizing trained intelligent agents. The results indicate that the rope tension remained within the specified range throughout the driving process, with the end platform successfully maneuvered in close proximity to the designated target point. The consistency between the simulation and experimental results validates the efficacy of reinforcement learning in driving unknown parameters in redundant constraint-driven robots. Furthermore, the method’s applicability extends to mechanisms with diverse configurations of redundant constraints, broadening its scope. Therefore, reinforcement learning emerges as a potent tool for acquiring motion data in cable-driven mechanisms with unknown parameters and redundant constraints, effectively aiding in the reconstruction process of such mechanisms.
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(This article belongs to the Special Issue Advances in Parallel Robots and Mechanisms)
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Open AccessArticle
Simulating and Modelling the Safety Impact of Connected and Autonomous Vehicles in Mixed Traffic: Platoon Size, Sensor Error, and Path Choice
by
Alkis Papadoulis, Marianna Imprialou, Yuxiang Feng and Mohammed Quddus
Machines 2024, 12(6), 371; https://doi.org/10.3390/machines12060371 - 27 May 2024
Abstract
The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a
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The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety.
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(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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Open AccessArticle
Flexible Continuum Robot System for Minimally Invasive Endoluminal Gastrointestinal Endoscopy
by
Liping Sun and Xiong Chen
Machines 2024, 12(6), 370; https://doi.org/10.3390/machines12060370 - 26 May 2024
Abstract
This paper presents a minimally invasive surgical robot system for endoluminal gastrointestinal endoscopy through natural orifices. In minimally invasive gastrointestinal endoscopic surgery (MIGES), surgical instruments need to pass through narrow endoscopic channels to perform highly flexible tasks, imposing strict constraints on the size
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This paper presents a minimally invasive surgical robot system for endoluminal gastrointestinal endoscopy through natural orifices. In minimally invasive gastrointestinal endoscopic surgery (MIGES), surgical instruments need to pass through narrow endoscopic channels to perform highly flexible tasks, imposing strict constraints on the size of the surgical robot while requiring it to possess a certain gripping force and flexibility. Therefore, we propose a novel minimally invasive robot system with advantages such as compact size and high precision. The system consists of an endoscope, two compact flexible continuum mechanical arms with diameters of 3.4 mm and 2.4 mm, respectively, and their driving systems, totaling nine degrees of freedom. The robot’s driving system employs bidirectional ball-screw-driven motion of two ropes simultaneously, converting the choice of opening and closing of the instrument’s end into linear motion, facilitating easier and more precise control of displacement when in position closed-loop control. By means of coordinated operation of the terminal surgical tools, tasks such as grasping and peeling can be accomplished. This paper provides a detailed analysis and introduction of the system. Experimental results validate the robot’s ability to grasp objects of 3 N and test the system’s accuracy and payload by completing basic operations, such as grasping and peeling, thereby preliminarily verifying the flexibility and coordination of the robot’s operation in a master–slave configuration.
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(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.
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(This article belongs to the Section Machines Testing and Maintenance)
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