Pub Date : 2024-07-04DOI: 10.3389/fmech.2024.1397767
Yongcheng Mu
Introduction: As an economical and fast process method for surface modification of materials, overlay welding is increasingly widely used in the manufacturing and repair of parts in various industrial sectors.Methods: This study combines grating projection measurement to design an economical mold arc additive process, and introduces point cloud simplification algorithm for wear and repair design of the mold structure. Then, a new method for manufacturing low-cost, long-life, and economical hot stamping die inserts is designed, using low-cost forged steel and cast steel as substrates and surface welding of high-temperature alloy wear-resistant layers. Results and Discussion: The experiment shows that the proposed algorithm for simplifying scattered molds has a good evaluation effect, without any gaps, and has a good retention effect on point clouds. The maximum and minimum distances are 0.45 mm and 0.025 mm, respectively. The friction coefficient of cobalt based alloys at 300°C is lower than that at 200°C, and the fluctuation period at 200°C is significantly longer than that at 300°C. HSTS steel has the highest wear resistance, and the performance of cobalt-based alloys is significantly better than that of other alloys. The compressive yield strength of iron-based alloys is the highest, and the hardness of iron-based alloys is the highest, at 53.2 HRC. Therefore, iron-based alloys were selected as a surface wear-resistant layer welding material for economical molds with cooling channels, and cobalt-based alloys were also selected as a surface wear-resistant layer welding material for variable strength economical molds. The research results provide a reference for economic mold manufacturing and repair.
{"title":"Using surfacing welding technology to manufacture economical molds","authors":"Yongcheng Mu","doi":"10.3389/fmech.2024.1397767","DOIUrl":"https://doi.org/10.3389/fmech.2024.1397767","url":null,"abstract":"Introduction: As an economical and fast process method for surface modification of materials, overlay welding is increasingly widely used in the manufacturing and repair of parts in various industrial sectors.Methods: This study combines grating projection measurement to design an economical mold arc additive process, and introduces point cloud simplification algorithm for wear and repair design of the mold structure. Then, a new method for manufacturing low-cost, long-life, and economical hot stamping die inserts is designed, using low-cost forged steel and cast steel as substrates and surface welding of high-temperature alloy wear-resistant layers. Results and Discussion: The experiment shows that the proposed algorithm for simplifying scattered molds has a good evaluation effect, without any gaps, and has a good retention effect on point clouds. The maximum and minimum distances are 0.45 mm and 0.025 mm, respectively. The friction coefficient of cobalt based alloys at 300°C is lower than that at 200°C, and the fluctuation period at 200°C is significantly longer than that at 300°C. HSTS steel has the highest wear resistance, and the performance of cobalt-based alloys is significantly better than that of other alloys. The compressive yield strength of iron-based alloys is the highest, and the hardness of iron-based alloys is the highest, at 53.2 HRC. Therefore, iron-based alloys were selected as a surface wear-resistant layer welding material for economical molds with cooling channels, and cobalt-based alloys were also selected as a surface wear-resistant layer welding material for variable strength economical molds. The research results provide a reference for economic mold manufacturing and repair.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141837722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.3389/fmech.2024.1411456
Guanhua Sun, Jinzhao Peng
Introduction: With the development of the new energy vehicle industry, the research aims to improve the energy utilization efficiency of electric vehicles by optimizing their composite power supply parameters.Methods: An optimization model based on non-dominated sorting genetic algorithm II was designed to optimize the parameters of liquid cooling structure of vehicle energy storage battery. The objective function and constraint conditions in the optimization process were defined to maximize the heat dissipation performance of the battery by establishing the heat transfer and hydrodynamic model of the electrolyzer.Results: The results showed that the optimization method had excellent performance on multiple evaluation indicators, the material degradation rate after optimization was reduced by 42%, the corrosion rate was reduced by 36%, and the battery life was increased by 17%. The optimization method ensured the maximum temperature control for the safe operation of the lithium-ion battery pack. The temperature of the battery pack was effectively controlled. The temperature difference was kept within 5°C, preventing the battery from overheating and extending its service life.Discussion: The proposed liquid cooling structure design can effectively manage and disperse the heat generated by the battery. This method provides a new idea for the optimization of the energy efficiency of the hybrid power system. This paper provides a new way for the efficient thermal management of the automotive power battery.
引言随着新能源汽车产业的发展，研究旨在通过优化电动汽车的复合电源参数来提高其能量利用效率：方法：设计了基于非支配排序遗传算法 II 的优化模型，对车载储能电池的液冷结构参数进行优化。通过建立电解槽的传热和流体力学模型，确定了优化过程中的目标函数和约束条件，以实现电池散热性能的最大化：结果表明，优化方法在多个评价指标上表现优异，优化后材料降解率降低了 42%，腐蚀率降低了 36%，电池寿命提高了 17%。优化方法确保了锂离子电池组安全运行所需的最高温度控制。电池组的温度得到了有效控制。讨论：讨论：所提出的液体冷却结构设计可有效管理和分散电池产生的热量。该方法为优化混合动力系统的能效提供了新思路。本文为汽车动力电池的高效热管理提供了一种新方法。
{"title":"Optimization of liquid cooled heat dissipation structure for vehicle energy storage batteries based on NSGA-II","authors":"Guanhua Sun, Jinzhao Peng","doi":"10.3389/fmech.2024.1411456","DOIUrl":"https://doi.org/10.3389/fmech.2024.1411456","url":null,"abstract":"Introduction: With the development of the new energy vehicle industry, the research aims to improve the energy utilization efficiency of electric vehicles by optimizing their composite power supply parameters.Methods: An optimization model based on non-dominated sorting genetic algorithm II was designed to optimize the parameters of liquid cooling structure of vehicle energy storage battery. The objective function and constraint conditions in the optimization process were defined to maximize the heat dissipation performance of the battery by establishing the heat transfer and hydrodynamic model of the electrolyzer.Results: The results showed that the optimization method had excellent performance on multiple evaluation indicators, the material degradation rate after optimization was reduced by 42%, the corrosion rate was reduced by 36%, and the battery life was increased by 17%. The optimization method ensured the maximum temperature control for the safe operation of the lithium-ion battery pack. The temperature of the battery pack was effectively controlled. The temperature difference was kept within 5°C, preventing the battery from overheating and extending its service life.Discussion: The proposed liquid cooling structure design can effectively manage and disperse the heat generated by the battery. This method provides a new idea for the optimization of the energy efficiency of the hybrid power system. This paper provides a new way for the efficient thermal management of the automotive power battery.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141692852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.3389/fmech.2024.1393959
Jagannath I. Pattar, D. Ramesh, R. Malghan, Ajay Kumar, Pawan Kumar, V. H. M.
Aluminum metal–matrix composites (AMMCs) were prepared by dispersing TiO2 dispersoids of different volume fractions into an AA6063 matrix via stir casting and subjected to process–structure correlation studies. Four different samples based on weight ratio were considered herein: 99Al-1TiO2, 97Al-3TiO2, 95Al-5TiO2, and the as-received AA6063. Their mechanical properties namely, microhardness, tensile strength, and tribological behavior, were determined. In addition, the microstructure of the samples was also analysed. It was observed that the addition of 5% TiO2 particles enabled the AA6063 matrix to accommodate a higher strain energy while providing the required driving force to generate dislocations and substructures. Therefore, considering the plastic deformation, the ultimate tensile strength σut increased gradually with the addition of TiO2 (in weight%). The flow curves of the 95Al-5TiO2 sample showed the highest value of σut, whereas the as-received AA6063 matrix exhibited the lowest value. For linear elastic deformation, AA6063 showed the lowest yield strength (σys) as compared to the AMMC samples for all TiO2 weight% values; however, the variation in σys among the AMMC samples was minimal. The microhardness of the samples increased gradually with the addition of TiO2, and the percentage reduction in area at the fracture was largest for 95Al-5TiO2. The Taguchi’s L9 array and variance analysis of the process parameters indicated that the material wear was largely affected by the normal load, followed by weight% of TiO2 and sliding speed. Wear surface characteristics, such as microvoids, delamination, microcracks, and wear debris, were qualitatively observed in all the AMMC samples. The overall strength improvement was attributable to the effects of addition of the dispersoids. During melt solidification, the TiO2 particles surpassed/pinned and hindered the grain growth, resulting in grain-size refinement.
{"title":"Investigation of AA6063-based metal–matrix composites reinforced with TiO2 dispersoids through digitally assisted techniques for mechanical, tribological, and microstructural characterizations","authors":"Jagannath I. Pattar, D. Ramesh, R. Malghan, Ajay Kumar, Pawan Kumar, V. H. M.","doi":"10.3389/fmech.2024.1393959","DOIUrl":"https://doi.org/10.3389/fmech.2024.1393959","url":null,"abstract":"Aluminum metal–matrix composites (AMMCs) were prepared by dispersing TiO2 dispersoids of different volume fractions into an AA6063 matrix via stir casting and subjected to process–structure correlation studies. Four different samples based on weight ratio were considered herein: 99Al-1TiO2, 97Al-3TiO2, 95Al-5TiO2, and the as-received AA6063. Their mechanical properties namely, microhardness, tensile strength, and tribological behavior, were determined. In addition, the microstructure of the samples was also analysed. It was observed that the addition of 5% TiO2 particles enabled the AA6063 matrix to accommodate a higher strain energy while providing the required driving force to generate dislocations and substructures. Therefore, considering the plastic deformation, the ultimate tensile strength σut increased gradually with the addition of TiO2 (in weight%). The flow curves of the 95Al-5TiO2 sample showed the highest value of σut, whereas the as-received AA6063 matrix exhibited the lowest value. For linear elastic deformation, AA6063 showed the lowest yield strength (σys) as compared to the AMMC samples for all TiO2 weight% values; however, the variation in σys among the AMMC samples was minimal. The microhardness of the samples increased gradually with the addition of TiO2, and the percentage reduction in area at the fracture was largest for 95Al-5TiO2. The Taguchi’s L9 array and variance analysis of the process parameters indicated that the material wear was largely affected by the normal load, followed by weight% of TiO2 and sliding speed. Wear surface characteristics, such as microvoids, delamination, microcracks, and wear debris, were qualitatively observed in all the AMMC samples. The overall strength improvement was attributable to the effects of addition of the dispersoids. During melt solidification, the TiO2 particles surpassed/pinned and hindered the grain growth, resulting in grain-size refinement.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141342190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.3389/fmech.2024.1400728
Yong Tian, Jun Tan
Introduction: With the development of artificial intelligence and autonomous driving technology, the application of motion recognition in automotive autonomous driving is becoming more and more important. The traditional feature extraction method uses adaptive search hybrid learning and needs to design the feature extraction process manually, which is difficult to meet the recognition requirements in complex environments.Methods: In this paper, a fusion algorithm is proposed to classify the driving characteristics through time-frequency analysis, and perform backpropagation operation in artificial neural network to improve the convergence speed of the algorithm. The performance analysis experiments of the study were carried out on Autov data sets, and the results were compared with those of the other three algorithms.Results: When the vehicle action coefficient is 227, the judgment accuracy of the four algorithms is 0.98, 0.94, 0.93 and 0.95, respectively, indicating that the fusion algorithm is stable. When the road sample is 547, the vehicle driving ability of the fusion algorithm is 4.7, which is the best performance among the four algorithms, indicating that the fusion algorithm has strong adaptability.Discussion: The results show that the fusion algorithm has practical significance in improving the autonomous operation ability of autonomous vehicles, reducing the frequency of vehicle accidents during driving, and contributing to the development of production, life and society.
{"title":"Improvement of action recognition based on ANN-BP algorithm for auto driving cars","authors":"Yong Tian, Jun Tan","doi":"10.3389/fmech.2024.1400728","DOIUrl":"https://doi.org/10.3389/fmech.2024.1400728","url":null,"abstract":"Introduction: With the development of artificial intelligence and autonomous driving technology, the application of motion recognition in automotive autonomous driving is becoming more and more important. The traditional feature extraction method uses adaptive search hybrid learning and needs to design the feature extraction process manually, which is difficult to meet the recognition requirements in complex environments.Methods: In this paper, a fusion algorithm is proposed to classify the driving characteristics through time-frequency analysis, and perform backpropagation operation in artificial neural network to improve the convergence speed of the algorithm. The performance analysis experiments of the study were carried out on Autov data sets, and the results were compared with those of the other three algorithms.Results: When the vehicle action coefficient is 227, the judgment accuracy of the four algorithms is 0.98, 0.94, 0.93 and 0.95, respectively, indicating that the fusion algorithm is stable. When the road sample is 547, the vehicle driving ability of the fusion algorithm is 4.7, which is the best performance among the four algorithms, indicating that the fusion algorithm has strong adaptability.Discussion: The results show that the fusion algorithm has practical significance in improving the autonomous operation ability of autonomous vehicles, reducing the frequency of vehicle accidents during driving, and contributing to the development of production, life and society.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.3389/fmech.2024.1410360
Fei Song, Kevin Shi, Ke Li, Amine Mahjoub, S. Ossia, Ives Loretz, Robson Serafim
In this study, a Bayesian data assimilation method that fuses physics with motion sensor data is demonstrated to infer the dynamic states at points of interest on the bottomhole assembly (BHA) with proper uncertainty quantification. A 4.75 inch-LWD (Logging-while-drilling) tool has been used as a use case, where the dynamic states at the formation evaluation sensor can be predicted in real time with the measurements at the motion sensor as the required inputs. This was achieved with a developed transfer function that utilizes unscented Kalman filtering technique. The robustness of the transfer function was evaluated with synthetic data obtained from finite element analysis (FEA) simulations for various BHA configurations and drilling conditions. It was found that the prediction by the transfer function agrees favorably well with the true states of motion at the formation evaluation sensor. Specifically, using the developed transfer function can help reduce the relative errors for the motion trajectories at the formation evaluation sensor by a factor of 3, and can significantly enhance measurement quality risk classification. The developed transfer function method was further assessed with experimental roll test data, which is considered as close to drilling conditions. The prediction by the transfer function was found consistently close to the ground truth in the presence of backward whirl. The developed modeling method can potentially have broader impacts by enabling fit-for-basin virtual V&V (Verification and Validation) to accelerate LWD tool development, or enabling future drilling optimization.
{"title":"A physics-informed Bayesian data assimilation approach for real-time drilling tool lateral motion prediction","authors":"Fei Song, Kevin Shi, Ke Li, Amine Mahjoub, S. Ossia, Ives Loretz, Robson Serafim","doi":"10.3389/fmech.2024.1410360","DOIUrl":"https://doi.org/10.3389/fmech.2024.1410360","url":null,"abstract":"In this study, a Bayesian data assimilation method that fuses physics with motion sensor data is demonstrated to infer the dynamic states at points of interest on the bottomhole assembly (BHA) with proper uncertainty quantification. A 4.75 inch-LWD (Logging-while-drilling) tool has been used as a use case, where the dynamic states at the formation evaluation sensor can be predicted in real time with the measurements at the motion sensor as the required inputs. This was achieved with a developed transfer function that utilizes unscented Kalman filtering technique. The robustness of the transfer function was evaluated with synthetic data obtained from finite element analysis (FEA) simulations for various BHA configurations and drilling conditions. It was found that the prediction by the transfer function agrees favorably well with the true states of motion at the formation evaluation sensor. Specifically, using the developed transfer function can help reduce the relative errors for the motion trajectories at the formation evaluation sensor by a factor of 3, and can significantly enhance measurement quality risk classification. The developed transfer function method was further assessed with experimental roll test data, which is considered as close to drilling conditions. The prediction by the transfer function was found consistently close to the ground truth in the presence of backward whirl. The developed modeling method can potentially have broader impacts by enabling fit-for-basin virtual V&V (Verification and Validation) to accelerate LWD tool development, or enabling future drilling optimization.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.3389/fmech.2024.1361929
Yu Zhao
The five axis linkage Computer Numerical Control machine tool for integral impeller can achieve blade machining through side milling, which is of great significance for improving the machining accuracy, production efficiency, and long-term stability of integral impeller blades. This study is based on non-uniform rational B-spline curves and aims to reduce the surface over cutting or under cutting of integral turbine blades. The path planning of non deployable ruled surfaces was analyzed in depth through side milling, and the path planning model of the side milling cutter axis was solved through a fusion algorithm of simulated annealing algorithm and particle swarm optimization algorithm, in order to find the optimal path through iterative process. As the number of iterations increased, the error values of particle swarm optimization algorithm and simulated annealing particle swarm optimization fusion algorithm gradually decreased, with convergence times of about 7 and 6, respectively. The stable error value of the fusion algorithm was 0.253, which is 30.45% lower than that of the particle swarm optimization algorithm. The optimal number of iterations for solving the model using particle swarm optimization algorithm and fusion algorithm was the 7th, with range values of 0.0213 and 0.0165 mm, respectively. The tool axis trajectory surface optimized by the fusion algorithm was closer to the tool axis motion state compared to the initial tool axis trajectory surface. The range of the sum of mean squared deviations for single and global cutting was 0.0011–0.0198 and 0.046–0.0341, but the overall error value was relatively small. This study effectively reduces the envelope error of machining tools and improves machining accuracy, thereby solving the principle error of non expandable ruled surfaces in the motion trajectory of the blade axis of the integral turbine. This provides new research ideas for the intelligent development of Computer Numerical Control machining technology.
{"title":"Optimization of machining path for integral impeller side milling based on SA-PSO fusion algorithm in CNC machine tools","authors":"Yu Zhao","doi":"10.3389/fmech.2024.1361929","DOIUrl":"https://doi.org/10.3389/fmech.2024.1361929","url":null,"abstract":"The five axis linkage Computer Numerical Control machine tool for integral impeller can achieve blade machining through side milling, which is of great significance for improving the machining accuracy, production efficiency, and long-term stability of integral impeller blades. This study is based on non-uniform rational B-spline curves and aims to reduce the surface over cutting or under cutting of integral turbine blades. The path planning of non deployable ruled surfaces was analyzed in depth through side milling, and the path planning model of the side milling cutter axis was solved through a fusion algorithm of simulated annealing algorithm and particle swarm optimization algorithm, in order to find the optimal path through iterative process. As the number of iterations increased, the error values of particle swarm optimization algorithm and simulated annealing particle swarm optimization fusion algorithm gradually decreased, with convergence times of about 7 and 6, respectively. The stable error value of the fusion algorithm was 0.253, which is 30.45% lower than that of the particle swarm optimization algorithm. The optimal number of iterations for solving the model using particle swarm optimization algorithm and fusion algorithm was the 7th, with range values of 0.0213 and 0.0165 mm, respectively. The tool axis trajectory surface optimized by the fusion algorithm was closer to the tool axis motion state compared to the initial tool axis trajectory surface. The range of the sum of mean squared deviations for single and global cutting was 0.0011–0.0198 and 0.046–0.0341, but the overall error value was relatively small. This study effectively reduces the envelope error of machining tools and improves machining accuracy, thereby solving the principle error of non expandable ruled surfaces in the motion trajectory of the blade axis of the integral turbine. This provides new research ideas for the intelligent development of Computer Numerical Control machining technology.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.3389/fmech.2024.1406559
Yi Lv
Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.
{"title":"Application of image processing technology based on field programmable gate array in mechanical part inspection","authors":"Yi Lv","doi":"10.3389/fmech.2024.1406559","DOIUrl":"https://doi.org/10.3389/fmech.2024.1406559","url":null,"abstract":"Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep-groove ball bearings for the eAxles of electric vehicles must adapt to higher rotational speed conditions because the speed of eAxle motors have been increasing as the size and weight of the motors decrease. Therefore, understanding the oil-lubricated conditions inside ball bearings at high rotational speeds is essential for optimizing their design for eAxles. To clarify the oil-lubricated conditions inside ball bearings at these high speeds, a new test apparatus was developed. This apparatus is capable of simultaneously measuring the friction torque of deep-groove ball bearings, the oil-film thickness on the rolling balls, and observing the oil distributions inside the bearings at rotational speeds up to 20,000 min-1. The oil-film thickness was measured using three-wavelength optical interferometry, and the oil distribution was observed using fluorescence. It was found that the oil-film thickness became constant at rotational speed conditions exceeding approximately 7,700 min-1. Oil starvations were observed on the raceway around the rolling ball, and these regions increased with increasing rotational speeds. Additionally, in the deep-groove ball bearing with a crown-shaped cage, the oil was mainly supplied to the rolling balls from the inner ring side through the space between the cage claws that held the ball. Moreover, the amount of mixed air tended to increase as the rotational speed increased to approximately 7,700 min-1. Those oil starvations and increasing air in oils were considered to be factors that prevent the increase in oil-film thickness. The findings of the reported study will contribute to the development of multibody dynamic technology for high-speed ball bearings necessary in electric vehicles.
{"title":"Visualization of oil-lubrication ball bearings at high rotational speeds","authors":"Mamoru Tohyama, Yasuhiro Ohmiya, Michiru Hirose, Hiroki Matsuyama, Takuya Toda, Kenichi Hasegawa, Takaaki Onizuka, Hideaki Sato, Masahiro Yokoi, Norikazu Sato","doi":"10.3389/fmech.2024.1416656","DOIUrl":"https://doi.org/10.3389/fmech.2024.1416656","url":null,"abstract":"Deep-groove ball bearings for the eAxles of electric vehicles must adapt to higher rotational speed conditions because the speed of eAxle motors have been increasing as the size and weight of the motors decrease. Therefore, understanding the oil-lubricated conditions inside ball bearings at high rotational speeds is essential for optimizing their design for eAxles. To clarify the oil-lubricated conditions inside ball bearings at these high speeds, a new test apparatus was developed. This apparatus is capable of simultaneously measuring the friction torque of deep-groove ball bearings, the oil-film thickness on the rolling balls, and observing the oil distributions inside the bearings at rotational speeds up to 20,000 min-1. The oil-film thickness was measured using three-wavelength optical interferometry, and the oil distribution was observed using fluorescence. It was found that the oil-film thickness became constant at rotational speed conditions exceeding approximately 7,700 min-1. Oil starvations were observed on the raceway around the rolling ball, and these regions increased with increasing rotational speeds. Additionally, in the deep-groove ball bearing with a crown-shaped cage, the oil was mainly supplied to the rolling balls from the inner ring side through the space between the cage claws that held the ball. Moreover, the amount of mixed air tended to increase as the rotational speed increased to approximately 7,700 min-1. Those oil starvations and increasing air in oils were considered to be factors that prevent the increase in oil-film thickness. The findings of the reported study will contribute to the development of multibody dynamic technology for high-speed ball bearings necessary in electric vehicles.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.3389/fmech.2024.1336828
A. Kengpol, Pornthip Tabkosai
In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry.
在注塑行业中，注塑成型是应用最广泛的大规模生产技术之一，并且近年来持续增长。成本评估在企业运营中至关重要，有助于提高市场份额和塑料零件定价的领先地位。由于塑料零件和制造数据的复杂性，导致数据等待时间长，成本评估不准确。因此，本研究旨在应用一种成本评估方法，将调谐蜂群算法（TSA）与人工神经网络（ANN）的混合深度学习相结合，用于注塑行业复杂表面产品的成本评估，以实现更快的最优解收敛速度和更高的精确度。该方法采用人工神经网络，对复杂表面产品的三维模型进行基于特征的提取，从而建立成本评估模型。TSA 被用于为 ANN 的学习模型构建初始权重，从而可以生成收敛速度更快的最优解和更高的精度。结果表明，新的混合深度学习 TSA 与方差分析网络相结合，能提供比方差分析网络更精确的成本评估。零件成本和模具成本的预测准确率分别约为 96.66% 和 93.75%。这项研究的贡献在于开发了一种结合了 TSA 和 ANN 的新型混合深度学习模型，其中包括专门针对复杂表面产品的隐层数计算，这在文献中是没有的。该成本评估方法可实际应用于注塑行业的复杂表面产品，并具有较高的准确性。
{"title":"Design of hybrid deep learning using TSA with ANN for cost evaluation in the plastic injection industry","authors":"A. Kengpol, Pornthip Tabkosai","doi":"10.3389/fmech.2024.1336828","DOIUrl":"https://doi.org/10.3389/fmech.2024.1336828","url":null,"abstract":"In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-24DOI: 10.3389/fmech.2024.1390341
Xinyan Wang, Yichao Li
Introduction: The rapid development of electric vehicle technology in the field of renewable energy has brought significant challenges to wireless charging systems. The efficiency of these systems is crucial for improving availability and sustainability. The main focus of the research is to develop an intelligent charging strategy that utilizes fuzzy logic to optimize the efficiency of wireless charging systems for electric vehicles.Method: Introduce a model that combines fuzzy logic algorithm with automatic control system to improve the wireless charging process of electric vehicles. The model adopts dynamic tracking and adaptive control methods by analyzing the characteristics of static wireless charging systems. Utilizing primary phase shift control and secondary controllable rectifier regulation, combined with optimized fuzzy control algorithm.Result and discussion: The experimental results show that when the secondary coil is stable, the model maintains a stable duty cycle of about 75.6% and a stable current of 5A. It was observed that when the mutual inductance values were set to 10, 15, and 20 uH, the efficiency of the primary coil before applying control decreased with increasing resistance.Conclusion: The proposed system has shown great potential for application in real-world electric vehicle charging systems, demonstrating good applicability and feasibility in controlling the charging process and tracking the optimal efficiency point. The integration of fuzzy logic enhances the system’s ability to adapt to different operating conditions, which may lead to wider implementation and improved operational efficiency.
{"title":"Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles","authors":"Xinyan Wang, Yichao Li","doi":"10.3389/fmech.2024.1390341","DOIUrl":"https://doi.org/10.3389/fmech.2024.1390341","url":null,"abstract":"Introduction: The rapid development of electric vehicle technology in the field of renewable energy has brought significant challenges to wireless charging systems. The efficiency of these systems is crucial for improving availability and sustainability. The main focus of the research is to develop an intelligent charging strategy that utilizes fuzzy logic to optimize the efficiency of wireless charging systems for electric vehicles.Method: Introduce a model that combines fuzzy logic algorithm with automatic control system to improve the wireless charging process of electric vehicles. The model adopts dynamic tracking and adaptive control methods by analyzing the characteristics of static wireless charging systems. Utilizing primary phase shift control and secondary controllable rectifier regulation, combined with optimized fuzzy control algorithm.Result and discussion: The experimental results show that when the secondary coil is stable, the model maintains a stable duty cycle of about 75.6% and a stable current of 5A. It was observed that when the mutual inductance values were set to 10, 15, and 20 uH, the efficiency of the primary coil before applying control decreased with increasing resistance.Conclusion: The proposed system has shown great potential for application in real-world electric vehicle charging systems, demonstrating good applicability and feasibility in controlling the charging process and tracking the optimal efficiency point. The integration of fuzzy logic enhances the system’s ability to adapt to different operating conditions, which may lead to wider implementation and improved operational efficiency.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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