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
Influence of Tool Inclination and Effective Cutting Speed on Roughness Parameters of Machined Shaped Surfaces
Machines 2024, 12(5), 318; https://doi.org/10.3390/machines12050318 (registering DOI) - 05 May 2024
Abstract
Free-form surfaces in the automotive or aviation industry where the future shape of the product will contain complex surfaces raises the question of how to achieve the necessary shape of the required quality in the milling process. One of the methods of their
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Free-form surfaces in the automotive or aviation industry where the future shape of the product will contain complex surfaces raises the question of how to achieve the necessary shape of the required quality in the milling process. One of the methods of their production is the use of 5-axis milling, in which it is necessary to consider not only the input data of the process itself, but also the methodology for evaluating the desired results. Correctly answered questions can thus facilitate the choice of the inclination of the tool when machining parts of the surfaces defined in the experiment. The primary goal of the paper was to monitor the influence of tool inclination on the quality of the machined surface and effective cutting speed by evaluating surface roughness and surface topography. The experiment was designed to show the effect of different tool positions while the feed per tooth fz for the finishing operation remained constant. The best result in terms of surface quality was achieved with a tool inclination of 15° in the cutting process. The most unfavorable result was obtained with a tool axis inclination of zero degrees due to unfavorable cutting conditions.
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(This article belongs to the Special Issue Precision Manufacturing and Machine Tools)
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The Effect of a Ferromagnetic Steel Enclosure on Magnetic Shielding Systems: Analysis, Modeling, and Experimental Validation
by
Yuan Cheng, Jiang Huang, Yaozhi Luo and Feng Lu
Machines 2024, 12(5), 317; https://doi.org/10.3390/machines12050317 (registering DOI) - 05 May 2024
Abstract
The magnetic shielding device, made of high-permeability soft magnetic material, is sensitive to external influences and requires a protective steel enclosure. A steel enclosure, being strongly ferrimagnetic, can alter the surrounding magnetic field distribution, thus impacting the shielding effectiveness. This study proposes a
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The magnetic shielding device, made of high-permeability soft magnetic material, is sensitive to external influences and requires a protective steel enclosure. A steel enclosure, being strongly ferrimagnetic, can alter the surrounding magnetic field distribution, thus impacting the shielding effectiveness. This study proposes a novel analytical approach to quantify this effect, which has not been previously researched. The method develops a simplified finite element simulation model based on the structural symmetry of the steel enclosure. By using this model, this study analyzes the impact of steel structures with varying heights, widths, and remanent magnetization values. The validity of the method is confirmed through experimental tests on steel buildings. The findings offer insights into the optimal placement of magnetic shielding systems and provide theoretical guidance for designing large-scale magnetic shielding devices.
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(This article belongs to the Section Machine Design and Theory)
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Study on the Parameter Influences of Gear Tooth Profile Modification and Transmission Error Analysis
by
Di Zhou, Yonglin Guo, Jian Yang and Yimin Zhang
Machines 2024, 12(5), 316; https://doi.org/10.3390/machines12050316 (registering DOI) - 04 May 2024
Abstract
Gear transmission systems are widely used to transfer energy and motion and to guarantee the accuracy of the entire machine system. The modification technique is a common method that improves the gear profile and reduces the transmission error. Based on the parametric model,
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Gear transmission systems are widely used to transfer energy and motion and to guarantee the accuracy of the entire machine system. The modification technique is a common method that improves the gear profile and reduces the transmission error. Based on the parametric model, a modified gear can be established for the evaluation of static and dynamic characteristics. The influences of profile modification parameters and gear parameters are investigated while changing the rules of different kinds of factors. Based on sensitive parameters, a two-stage profile modification curve is proposed to improve the performance of gear pairs. Thus, considering the time-varying mesh stiffness and backlash, a novel, dynamic modified gear model is established to analyze the dynamic performance, such as the dynamic transmission error. Based on the proposed curve, the range and amplitude of the transmission error can be decreased. Additionally, the vibration displacement and noise can be reduced to improve the running characteristics.
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(This article belongs to the Section Machine Design and Theory)
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Open AccessReview
A Review of Wrist Rehabilitation Robots and Highlights Needed for New Devices
by
Gabriella Faina Garcia, Rogério Sales Gonçalves and Giuseppe Carbone
Machines 2024, 12(5), 315; https://doi.org/10.3390/machines12050315 - 03 May 2024
Abstract
Various conditions, including traffic accidents, sports injuries, and neurological disorders, can impair human wrist movements, underscoring the importance of effective rehabilitation methods. Robotic devices play a crucial role in this regard, particularly in wrist rehabilitation, given the complexity of the human wrist joint,
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Various conditions, including traffic accidents, sports injuries, and neurological disorders, can impair human wrist movements, underscoring the importance of effective rehabilitation methods. Robotic devices play a crucial role in this regard, particularly in wrist rehabilitation, given the complexity of the human wrist joint, which encompasses three degrees of freedom: flexion/extension, pronation/supination, and radial/ulnar deviation. This paper provides a comprehensive review of wrist rehabilitation devices, employing a methodological approach based on primary articles sourced from PubMed, ScienceDirect, Scopus, and IEEE, using the keywords “wrist rehabilitation robot” from 2007 onwards. The findings highlight a diverse array of wrist rehabilitation devices, systematically organized in a tabular format for enhanced comprehension. Serving as a valuable resource for researchers, this paper enables comparative analyses of robotic wrist rehabilitation devices across various attributes, offering insights into future advancements. Particularly noteworthy is the integration of serious games with simplified wrist rehabilitation devices, signaling a promising avenue for enhancing rehabilitation outcomes. These insights lay the groundwork for the development of new robotic wrist rehabilitation devices or to make improvements to existing prototypes incorporating a forward-looking approach to improve rehabilitation outcomes.
Full article
(This article belongs to the Special Issue Design and Application of Medical and Rehabilitation Robots)
Open AccessArticle
Resonant Fatigue Tests on Polished Drill Pipe Specimens
by
Ciro Santus, Lorenzo Romanelli, Leonardo Bertini, Alessandro Burchianti and Tomoya Inoue
Machines 2024, 12(5), 314; https://doi.org/10.3390/machines12050314 - 03 May 2024
Abstract
In this study, the fatigue strength of polished drill pipe specimens was investigated and compared with previous test results of corroded and not-corroded pipes. The resonant fatigue test rig, which was designed and implemented by the University of Pisa, is initially presented by
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In this study, the fatigue strength of polished drill pipe specimens was investigated and compared with previous test results of corroded and not-corroded pipes. The resonant fatigue test rig, which was designed and implemented by the University of Pisa, is initially presented by providing a detailed description of the set-up of the machine, the calibration of the strain gauges, the control system, and the correct identification of the vibrational node locations. A polishing rig was also designed and put into operation to remove the corrosion pits from the outer surface of almost the entire length of the drill pipe specimens. After the fatigue tests with the resonant rig, and the observation of the fatigue fracture of the specimens, a few samples were extracted from different zones (corroded and not corroded) of the failed drill pipe specimens. This allowed for investigations to be carried out using a scanning electronic microscope. The obtained results were analyzed using the Murakami model, and a discussion is presented about the effect of the corrosion pits on the fatigue strength.
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(This article belongs to the Special Issue Design and Experimental Activity of Testing Machines and Mechanical Test Rigs)
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Open AccessArticle
Analytical Model of Tapered Thread Made by Turning from Different Machinability Workpieces
by
Oleh Onysko, Volodymyr Kopei, Cristian Barz, Yaroslav Kusyi, Saulius Baskutis, Miсhal Bembenek, Predrag Dašić and Vitalii Panchuk
Machines 2024, 12(5), 313; https://doi.org/10.3390/machines12050313 - 03 May 2024
Abstract
High-precision tapered threads are widely used in hard-loaded mechanical joints, especially in the aggressive environment of the drilling of oil and gas wells. Therefore, they must be made of workable materials often difficult to machine. This requires the use of high-performance cutting tools,
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High-precision tapered threads are widely used in hard-loaded mechanical joints, especially in the aggressive environment of the drilling of oil and gas wells. Therefore, they must be made of workable materials often difficult to machine. This requires the use of high-performance cutting tools, which means the application of non-zero geometric parameters: rake and edge inclination angles. This study is based on analytical geometry methodology and describes the theoretical function of the thread profile as convoluted surfaces dependent on the tool’s geometric angles. The experiments were conducted using a visual algorithm grounded on the obtained function and prove the practical use of the scientific result. They predict the required accuracy of thread made using a lathe tool with a rake angle of up to 12°.
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(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)
Open AccessReview
Effects of Cryogenic- and Cool-Assisted Burnishing on the Surface Integrity and Operating Behavior of Metal Components: A Review and Perspectives
by
Jordan Maximov and Galya Duncheva
Machines 2024, 12(5), 312; https://doi.org/10.3390/machines12050312 - 02 May 2024
Abstract
When placed under cryogenic temperatures (below −180 °C), metallic materials undergo structural changes that can improve their service life. This process, known as cryogenic treatment (CrT), has received extensive research attention over the past five decades. CrT can be applied as either an
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When placed under cryogenic temperatures (below −180 °C), metallic materials undergo structural changes that can improve their service life. This process, known as cryogenic treatment (CrT), has received extensive research attention over the past five decades. CrT can be applied as either an autonomous process (for steels and non-ferrous alloys, tool materials, and finished products) or as an assisting process for conventional metalworking. Cryogenic impacts and conventional machining or static surface cold working (SCW) can also be performed simultaneously in hybrid processes. The static SCW, known as burnishing, is a widely used environmentally friendly finishing process that achieves high-quality surfaces of metal components. The present review is dedicated to the portion of the hybrid processes in which burnishing under cryogenic conditions is carried out from the viewpoint of surface engineering, namely, finishing–surface integrity (SI)–operational behavior. Analyzes and summaries of the effects of cryogenic-assisted (CrA) burnishing on SI and the operational behavior of the investigated materials are made, and perspectives for future research are proposed.
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(This article belongs to the Topic Advanced Manufacturing and Surface Technology)
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Open AccessArticle
Influence of Laser Texturing and Coating on the Tribological Properties of the Tool Steels Properties
by
Jana Moravčíková, Roman Moravčík, Martin Sahul and Martin Necpal
Machines 2024, 12(5), 311; https://doi.org/10.3390/machines12050311 - 02 May 2024
Abstract
The article is aimed at identifying the influence of laser texturing and subsequent coating with a hard, wear-resistant coating AlCrSiN (nACRo®) on selected tribological properties of the analyzed tool steels for cold work, produced by conventional and powder metallurgy. The substrate
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The article is aimed at identifying the influence of laser texturing and subsequent coating with a hard, wear-resistant coating AlCrSiN (nACRo®) on selected tribological properties of the analyzed tool steels for cold work, produced by conventional and powder metallurgy. The substrate from each steel was heat treated to achieve optimal properties regarding the chemical composition and the method of production of the material. Böhler K100 and K390 Microclean® steels were used. These are highly alloyed tool steels used for various types of tools intended for cold work. The obtained results show that the coefficient of friction is increased by coating, but the wear rate is lower compared to the samples which were only textured.
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(This article belongs to the Special Issue Precision Manufacturing and Machine Tools)
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An Internet of Things-Based Production Scheduling for Distributed Two-Stage Assembly Manufacturing with Mold Sharing
by
Yin Liu, Cunxian Ma and Yun Huang
Machines 2024, 12(5), 310; https://doi.org/10.3390/machines12050310 - 02 May 2024
Abstract
In digital product and ion scheduling centers, order–factory allocation, factory–mold allocation, and mold routing can be performed centrally and efficiently to maximize the utilization of manufacturing resources (molds). Therefore, in this paper, a manufacturing resource (molds)-sharing mechanism based on the Internet of Things
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In digital product and ion scheduling centers, order–factory allocation, factory–mold allocation, and mold routing can be performed centrally and efficiently to maximize the utilization of manufacturing resources (molds). Therefore, in this paper, a manufacturing resource (molds)-sharing mechanism based on the Internet of Things (IoT) and a cyber-physical production system (CPPS) is designed to realize the coordinated allocation of molds and production scheduling. A mixed-integer mathematical model is developed to optimize the cost structure and obtain a reasonable profit solution. A heuristic algorithm based on evolutionary reversal is used to solve the problem. The numerical results show that based on the digital coordinated production scheduling method, distributed two-stage assembly manufacturing with shared molds can effectively reduce the order delay time and increase potential benefits for distributed production enterprises.
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(This article belongs to the Special Issue Technology Integration for Smart Manufacturing/Re-manufacturing Systems Development)
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Open AccessArticle
Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder
by
Haoran Wang, Zhongze Han, Xiaoshuang Xiong, Xuewei Song and Chen Shen
Machines 2024, 12(5), 309; https://doi.org/10.3390/machines12050309 - 02 May 2024
Abstract
Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails
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Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model.
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(This article belongs to the Section Machines Testing and Maintenance)
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Open AccessArticle
Improving Material Flows in an Industrial Enterprise: A Comprehensive Case Study Analysis
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Luboslav Dulina, Jan Zuzik, Beata Furmannova and Sławomir Kukla
Machines 2024, 12(5), 308; https://doi.org/10.3390/machines12050308 - 01 May 2024
Abstract
The primary objective of this research endeavor was to devise an improved workplace design tailored to the demands of a digital factory environment. With the overarching aim of enhancing efficiency and productivity, a comprehensive proposal was formulated to improve layout configurations within the
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The primary objective of this research endeavor was to devise an improved workplace design tailored to the demands of a digital factory environment. With the overarching aim of enhancing efficiency and productivity, a comprehensive proposal was formulated to improve layout configurations within the designated enterprise. The key focus lies in minimizing material transit across individual workstations, thereby mitigating potential bottlenecks and streamlining operations. Employing a structured workplace research framework, this study delved into material flow analysis techniques, augmented by the utilization of visTABLE software. While visTABLE served solely to visualize the work environment effectively, it played a crucial role in validating proposed solutions. Notably, the investigation yielded a discernible reduction in beam production time, marking a significant improvement of 10 min. These findings underscored the efficacy of the proposed solutions in addressing specific operational challenges faced by the company. Furthermore, this study facilitated a deeper understanding and visualization of the processes intrinsic to the digital factory environment. Elucidating workflow procedures at the workplace enabled stakeholders to identify areas for further improvement and refinement. In doing so, the research contributed to the overall efficiency and effectiveness of operations within the digital factory, paving the way for continued improvement and innovation in the field.
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(This article belongs to the Special Issue Advancing Human-Robot Collaboration in Industry 4.0)
Open AccessArticle
Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm
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Xuejian Zhang, Xiaobing Hu, Hang Li, Zheyuan Zhang, Haijun Chen and Hong Sun
Machines 2024, 12(5), 307; https://doi.org/10.3390/machines12050307 - 01 May 2024
Abstract
Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis
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Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry.
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(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)
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Tool Wear Prediction Based on Residual Connection and Temporal Networks
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Ziteng Li, Xinnan Lei, Zhichao You, Tao Huang, Kai Guo, Duo Li and Huan Liu
Machines 2024, 12(5), 306; https://doi.org/10.3390/machines12050306 - 01 May 2024
Abstract
Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due
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Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE.
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(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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Open AccessArticle
A Fault Diagnosis Method for Key Components of the CNC Machine Feed System Based on the DoubleEnsemble–LightGBM Model
by
Yiming Li, Yize Wang, Liuwei Lu and Lumeng Chen
Machines 2024, 12(5), 305; https://doi.org/10.3390/machines12050305 - 01 May 2024
Abstract
To solve the problem of fault diagnosis for the key components of the CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and an ensemble learning model is proposed in this
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To solve the problem of fault diagnosis for the key components of the CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and an ensemble learning model is proposed in this study. First, various monitoring signals including vibration signals, noise signals, and current signals are collected. Then, the monitoring signals are preprocessed and the time domain, frequency domain, and time–frequency domain feature indices are extracted to construct a multi-dimensional mixed-domain feature set. Finally, the feature set is entered into the constructed DoubleEnsemble–LightGBM model to realize the fault diagnosis of the key components of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the widely used dataset and the feed system test bench dataset, and the average overall accuracy is 91.07% and 98.06%, respectively. Compared with XGBoost and other advanced ensemble learning models, this method demonstrates better accuracy. Therefore, the proposed method provides technical support for the stable operation and intelligence of CNC machines.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
Open AccessArticle
Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears
by
Daniel Vietze, Josef Pellkofer and Karsten Stahl
Machines 2024, 12(5), 304; https://doi.org/10.3390/machines12050304 - 01 May 2024
Abstract
Calculating the service life of gears under variable loads requires a description of the load-carrying capacity. The current standard for this is the use of the S/N curve. International standards such as ISO 6336 stipulate the use of this approach for the calculation
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Calculating the service life of gears under variable loads requires a description of the load-carrying capacity. The current standard for this is the use of the S/N curve. International standards such as ISO 6336 stipulate the use of this approach for the calculation of the service of gears under variable loads. In this paper, five new approaches are developed and evaluated to describe the load-carrying capacity of gears in the load range of finite life. Four methods are based on machine learning, and one uses mathematical regression. To validate the new approaches, the results of an experimental study investigating the service life of gears under variable loads are presented. These results form the basis for the conducted study, which compares the five new methods with the existing approach. The comparison focuses on the ability of the load-carrying capacity descriptions to provide an accurate calculation of the service life and to reduce scattering as much as possible. The results of the study show significant potential for the new methods, especially the one based on a neural network.
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(This article belongs to the Special Issue Advancements in Mechanical Power Transmission and Its Elements)
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Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis
by
Yu Gu, Haishen Ni and Yuwei Li
Machines 2024, 12(5), 303; https://doi.org/10.3390/machines12050303 - 30 Apr 2024
Abstract
With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale
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With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 , 10 and 15 ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.
Full article
(This article belongs to the Special Issue Data-Driven Fault Diagnosis for Machines and Systems)
Open AccessArticle
Research on Multi-System Coupling Vibration of a Hot Tandem Mill
by
Yujie Liu, Shen Wang, Xuewei Wang and Xiaoqiang Yan
Machines 2024, 12(5), 302; https://doi.org/10.3390/machines12050302 - 30 Apr 2024
Abstract
Vibration in hot tandem rolling mills has been a problem in the iron and steel industry mainly due to its unpredictability. In this work, vibration data of the second finishing mill (F2) stand of a hot tandem rolling mill are collected and analyzed,
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Vibration in hot tandem rolling mills has been a problem in the iron and steel industry mainly due to its unpredictability. In this work, vibration data of the second finishing mill (F2) stand of a hot tandem rolling mill are collected and analyzed, and a mathematical model based on the coupling of a non-uniform deformation process, mill structure and hydraulic control system is constructed. The influence of different inlet thickness fluctuation forms, structural parameters and control parameters on the vibration behavior is analyzed. It is concluded that the low-frequency thickness fluctuation with additional skewness can cause the resonance of each subsystem of the rolling mill. The deviation angle of the roll system influences the vibration harmonic output of the rolling mill under a single low-frequency thickness fluctuation excitation. The compensation parameter in the thickness control system affects the natural frequency of the vertical system.
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(This article belongs to the Section Machine Design and Theory)
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Experimental Evaluation of Effect of Leaves on Railroad Tracks in Loss of Braking
by
Nikhil Kumar, Ahmad Radmehr and Mehdi Ahmadian
Machines 2024, 12(5), 301; https://doi.org/10.3390/machines12050301 - 29 Apr 2024
Abstract
This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf
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This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf contamination, which can cause partial loss of braking and lead to undesired events such as station overruns. Better understanding the adhesion-reducing impact of leaf contamination significantly improves railway engineering practices to counter their effects on train braking and traction. This experimental study evaluates the reduction in traction and braking forces (collectively called “adhesion”) as a function of leaf volume, using two leaf species that commonly grow along U.S. railroad tracks. The test methods rely on the chosen leaves’ transpiration characteristics while ensuring the result’s reproducibility. Leaves were symmetrically positioned on the wheel surface, centered around the mid-rib area within the wear band, and taped on the edges far from the wear band. The critical test parameters (i.e., wheel load, wheel velocity, and percentage creepage) are kept constant among the tests. At the same time, leaf volume is reduced from a maximum amount that covers the entire wheel surface (100% coverage) to no leaves (0%). The latter is used as the baseline. The percentage creepage is kept constant at an exaggerated amount of 2% to accelerate the test time. The results indicate a nonlinear relationship between leaf volume and the loss of braking. Even a small amount of leaf contamination causes a significant reduction in adhesion by as much as 50% compared with no contamination (i.e., baseline). Increasing leaf volume results in contact saturation, beyond which adhesion is not reduced. The minimum adhesion observed in this study is 20% of the maximum adhesion that occurs when no leaf contamination is present.
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(This article belongs to the Special Issue Research on Braking Systems of Railway Vehicles)
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Investigation of the Effect of Pumping Depth and Frequency of Flapping Hydrofoil on Suspended Matter Discharge Characteristics
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Ertian Hua, Mingwang Xiang, Tao Wang, Yabo Song, Caiju Lu and Qizong Sun
Machines 2024, 12(5), 300; https://doi.org/10.3390/machines12050300 - 29 Apr 2024
Abstract
In order to study the effect of the pumping depth and pumping frequency of the flapping hydrofoil device on suspended solids in the waters, this paper takes raceway aquaculture as an example, and introduces a flapping hydrofoil device to improve the discharge of
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In order to study the effect of the pumping depth and pumping frequency of the flapping hydrofoil device on suspended solids in the waters, this paper takes raceway aquaculture as an example, and introduces a flapping hydrofoil device to improve the discharge of suspended solids in the raceway, in response to the problem of the deposition of suspended solids from fish faeces and bait residues in water. The CFD method was used to compare and analyze the discharge of suspended solids at different pumping depths, and the combined effect of the two was studied according to different combinations of pumping frequency and pumping depth. The results proved that the flapping hydrofoil motion can improve the bottom hydrodynamic insufficiency in ecological waters and thus enhance the discharge effect of suspended particles in water. In addition, the pumping depth of the flapping hydrofoil is too deep for the movement to be disturbed by the bottom surface, while the thrust generated by the flapping hydrofoil is weakened if the depth is too shallow. When the pump water depth is 1.1 H, the reversed Kármán vortex street is more stable under the balancing effect of the bottom surface and gravity, and the rate curve of the flapping hydrofoil acting on the discharge of suspended particles is better. From our comprehensive consideration of the joint effect of the pumping depth and pumping frequency, we recommend the use of a 1.1 H of pumping depth and 2.0 Hz pumping frequency in combination to achieve the best effect of discharging suspended particles. This study provides valuable insights into the actual engineering applications of flapping hydrofoil devices for improving water quality and ecological sustainability in raceway aquaculture.
Full article
(This article belongs to the Special Issue Agricultural Machinery and Robotics: Design, Control and Applications)
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Open AccessArticle
An Improved Fourier-Based Method for Path Generation of Planar Four-Bar Linkages without Prescribed Timing
by
Yahui Qian, Hong Zhong, Tao Wang and Liangmo Wang
Machines 2024, 12(5), 299; https://doi.org/10.3390/machines12050299 - 28 Apr 2024
Abstract
Four-bar linkages are critical fundamental elements of many mechanical systems, and their design synthesis is often mathematically complicated with iterative numerical solutions. Analytical methods based on Fourier coefficients can circumvent these difficulties but have issues with time parameters assignment for path generation without
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Four-bar linkages are critical fundamental elements of many mechanical systems, and their design synthesis is often mathematically complicated with iterative numerical solutions. Analytical methods based on Fourier coefficients can circumvent these difficulties but have issues with time parameters assignment for path generation without prescribed time in previous studies. In this paper, an improved Fourier-based point-to-point combination method is presented, which generates more points by Fourier approximation and assigns the time parameters to the given points while allowing discarding solutions with order defects. This method relies on the Fourier coefficients, improving the accuracy of synthesis solutions, and simplifying the computational procedure. Time parameters are assigned directly to the given points, which avoids the complex calculations to find intersection points in the given path, eliminates combinations that would lead to solutions with order defects, and simplifies the assessment process of synthesis results. The parameters obtained by the point-to-point combination method can be used as the parameters of the input dyad, skipping the first set of design equations for faster calculation. Several examples are presented to demonstrate the advantages of the proposed synthesis method, which is easy-understanding, computationally efficient, and yields more accurate solutions than available synthesis methods.
Full article
(This article belongs to the Section Machine Design and Theory)
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