Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021481
Zechao Shangguan, Shukun Cao, Chong Cao, Hongbin Wang
When the grain combine harvester is running in the field, it is easy for the corn harvester to fail due to the different density of crops, uneven roads, complex external environment, etc., so it is particularly important to monitor the components of the corn harvester. This paper designs an intelligent operation and maintenance system of grain combine based on STM32 and Onenet platform, which takes STM32 as the monitoring core and realizes real-time monitoring of grain combine operating parameters.fault diagnosis and data during operation are uploaded to Onenet platform and controlled by STM32 to ensure the stable operation of grain combine. It also facilitates the driver to know the operation state of the grain combine through the screen, facilitates remote monitoring and improves the level of intelligence.
{"title":"Intelligent Operation and Maintenance System of Grain Combine Harvester Based on STM32 and Onenet Platform","authors":"Zechao Shangguan, Shukun Cao, Chong Cao, Hongbin Wang","doi":"10.1109/WCMEIM56910.2022.10021481","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021481","url":null,"abstract":"When the grain combine harvester is running in the field, it is easy for the corn harvester to fail due to the different density of crops, uneven roads, complex external environment, etc., so it is particularly important to monitor the components of the corn harvester. This paper designs an intelligent operation and maintenance system of grain combine based on STM32 and Onenet platform, which takes STM32 as the monitoring core and realizes real-time monitoring of grain combine operating parameters.fault diagnosis and data during operation are uploaded to Onenet platform and controlled by STM32 to ensure the stable operation of grain combine. It also facilitates the driver to know the operation state of the grain combine through the screen, facilitates remote monitoring and improves the level of intelligence.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127351582","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021556
Huijuan Wang, Tingting Ren, Yang Xu, Chuandong Xie, Yingqian Ni
The shaking incubator is an incubator which consists of shaker and heating chamber for incubating and shaking sensitive samples. It replaces two devices, reducing both time and space needed. Both the shaking mechanism and temperature chamber are regulated via microprocessors, which control all sensors for motor speed, temperature and time. It is suitable for biochemistry, microbiology and clinical laboratories in which applications require temperature and shaking treatment. This article presents a novel design of calibration method of shaking incubator by studying the essential metrological parameters of the device. The experimental results show that the calibration method presented in this article can establish the metrological traceability system of shaking incubator in China in order to ensure the accuracy and reliability of the device, which is of great importance for the quality control of the device.
{"title":"Studies on Calibration of Shaking Incubator","authors":"Huijuan Wang, Tingting Ren, Yang Xu, Chuandong Xie, Yingqian Ni","doi":"10.1109/WCMEIM56910.2022.10021556","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021556","url":null,"abstract":"The shaking incubator is an incubator which consists of shaker and heating chamber for incubating and shaking sensitive samples. It replaces two devices, reducing both time and space needed. Both the shaking mechanism and temperature chamber are regulated via microprocessors, which control all sensors for motor speed, temperature and time. It is suitable for biochemistry, microbiology and clinical laboratories in which applications require temperature and shaking treatment. This article presents a novel design of calibration method of shaking incubator by studying the essential metrological parameters of the device. The experimental results show that the calibration method presented in this article can establish the metrological traceability system of shaking incubator in China in order to ensure the accuracy and reliability of the device, which is of great importance for the quality control of the device.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906114","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021555
Jing Li, Jiangchuan Fan, Nanyan Shen, Hui Qian
With the rapid development of the automobile manufacturing industry, major automobile manufacturers around the world are stepping up digital transformation to achieve high-flexible, efficient and high-quality automobile production to adapt to the increasingly competitive market situation. Robots play an important role in automobile manufacturing. In the process of automobile manufacturing, the performance and quality of the product largely depend on the size accuracy of the parts, so edge information needs to be tested before the product is produced and assembled. At the same time, in the field of path planning, people need to obtain geometric feature information such as the edge of components in advance. However, large components are due to various the accuracy of the error needs to be scanned on the spot, and then processed after obtaining the point cloud. In this paper, a new method for quickly extracting edge contours from large-scale point clouds is studied. In the process of finding edges in two-dimensional images, some optimizations have also been made to make them more suitable for edge extraction of such plane structures. We also demonstrated the application of this method in a bus and compared it with two traditional methods.
{"title":"Research of high-efficiency extraction of edges of large complex components in three-dimensional point clouds","authors":"Jing Li, Jiangchuan Fan, Nanyan Shen, Hui Qian","doi":"10.1109/WCMEIM56910.2022.10021555","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021555","url":null,"abstract":"With the rapid development of the automobile manufacturing industry, major automobile manufacturers around the world are stepping up digital transformation to achieve high-flexible, efficient and high-quality automobile production to adapt to the increasingly competitive market situation. Robots play an important role in automobile manufacturing. In the process of automobile manufacturing, the performance and quality of the product largely depend on the size accuracy of the parts, so edge information needs to be tested before the product is produced and assembled. At the same time, in the field of path planning, people need to obtain geometric feature information such as the edge of components in advance. However, large components are due to various the accuracy of the error needs to be scanned on the spot, and then processed after obtaining the point cloud. In this paper, a new method for quickly extracting edge contours from large-scale point clouds is studied. In the process of finding edges in two-dimensional images, some optimizations have also been made to make them more suitable for edge extraction of such plane structures. We also demonstrated the application of this method in a bus and compared it with two traditional methods.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116557935","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021468
Yimo Zong, Jianzhong Huang, Jiahan Bao, Deshang Sun, Y. Cen
To solve the problem of autonomous and precise positioning of the robot arm in the non-structural environment of the demolition robot, binocular vision is used for the measurement of the target point for robot arm positioning, and the real-time tracking and measurement of the end of the robot arm during the positioning process. For demolition robots operating in non-structural environments, their tracking process will have the problems of randomly appearing tracking targets being obscured and a complex background environment. The paper solves this problem by extending the Camshift algorithm to 3D space, using depth information to separate occluders and targets in space. Through experiments, it is proved that this method can effectively solve the problem that the tracking of the end of the robotic arm is lost due to random occlusions during the positioning and tracking of the end of the robotic arm, so as to provide the feedback information of the spatial position of the end of the robotic arm for the autonomous positioning of the robotic arm in a real-time and stable manner.
{"title":"Target Tracking Method for Visual Autonomous Localization of Demolition Robot","authors":"Yimo Zong, Jianzhong Huang, Jiahan Bao, Deshang Sun, Y. Cen","doi":"10.1109/WCMEIM56910.2022.10021468","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021468","url":null,"abstract":"To solve the problem of autonomous and precise positioning of the robot arm in the non-structural environment of the demolition robot, binocular vision is used for the measurement of the target point for robot arm positioning, and the real-time tracking and measurement of the end of the robot arm during the positioning process. For demolition robots operating in non-structural environments, their tracking process will have the problems of randomly appearing tracking targets being obscured and a complex background environment. The paper solves this problem by extending the Camshift algorithm to 3D space, using depth information to separate occluders and targets in space. Through experiments, it is proved that this method can effectively solve the problem that the tracking of the end of the robotic arm is lost due to random occlusions during the positioning and tracking of the end of the robotic arm, so as to provide the feedback information of the spatial position of the end of the robotic arm for the autonomous positioning of the robotic arm in a real-time and stable manner.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116642364","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021387
Zi-sheng Li, Shuo Wang, Haomiao Xin, Zhuwei Liang, Zhi Li
The geological subsidence of substations is sudden and complex, and its rapid and accurate monitoring and early warning is of great significance to the safe and stable operation of the power system. This paper presents a method for monitoring the geological subsidence of substations with high-precision Beidou positioning technology. On the one hand, it can optimize the PTK technology, and on the other hand, it can comprehensively process the data of the reference station and monitoring station and use the reference station as a reference to grasp and understand. The deformation of monitoring points ensures the accuracy of monitoring. In this paper, the output of pseudorange, carrier phase and other information of high-precision Beidou satellites is used to meet the requirements of positioning solution. Secondly, the principle of vertical and horizontal displacement monitoring of geology substations of high-precision Beidou satellite receivers is given. Finally, the settlement is given. The selection principle of monitoring base station, monitoring station and communication network. The high-precision Beidou positioning monitoring method proposed in this paper can quickly, real-timely, and accurately monitor and warn the geological subsidence of substations, changing the previous situation of manual monitoring of geological subsidence.
{"title":"Research on Monitoring and Early Warning of Substation Geological Subsidence Based on High-Precision Beidou Positioning","authors":"Zi-sheng Li, Shuo Wang, Haomiao Xin, Zhuwei Liang, Zhi Li","doi":"10.1109/WCMEIM56910.2022.10021387","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021387","url":null,"abstract":"The geological subsidence of substations is sudden and complex, and its rapid and accurate monitoring and early warning is of great significance to the safe and stable operation of the power system. This paper presents a method for monitoring the geological subsidence of substations with high-precision Beidou positioning technology. On the one hand, it can optimize the PTK technology, and on the other hand, it can comprehensively process the data of the reference station and monitoring station and use the reference station as a reference to grasp and understand. The deformation of monitoring points ensures the accuracy of monitoring. In this paper, the output of pseudorange, carrier phase and other information of high-precision Beidou satellites is used to meet the requirements of positioning solution. Secondly, the principle of vertical and horizontal displacement monitoring of geology substations of high-precision Beidou satellite receivers is given. Finally, the settlement is given. The selection principle of monitoring base station, monitoring station and communication network. The high-precision Beidou positioning monitoring method proposed in this paper can quickly, real-timely, and accurately monitor and warn the geological subsidence of substations, changing the previous situation of manual monitoring of geological subsidence.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131176034","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021406
Yong Zhou, Ligang Qiang, Yong Zhang
The robot described in this paper is used to transport the FAST feed receiver; its lifting part is a spatial multi-bar mechanism; in order to achieve accurate positioning in the transport and avoid deformation caused by large load and thus affecting the normal operation of the mechanism, the whole mechanism needs to have good stiffness. To analyze the stiffness of such complex mechanical mechanism, finite element method can be used for simulation. Based on Abaqus finite element software, the stresses and deformations of the handling robot, especially its lifting system, were analyzed under rated working conditions; the stresses and overall deformations of key parts of the lifting system were obtained. The analysis results showed that the maximum stress and deformation of the lifting system did not exceed the predefined targets. The obtained analysis results provide an important reference for the actual performance of the lifting system.
{"title":"FAST Handling Robot Stiffness Analysis Based on Finite Element Simulation","authors":"Yong Zhou, Ligang Qiang, Yong Zhang","doi":"10.1109/WCMEIM56910.2022.10021406","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021406","url":null,"abstract":"The robot described in this paper is used to transport the FAST feed receiver; its lifting part is a spatial multi-bar mechanism; in order to achieve accurate positioning in the transport and avoid deformation caused by large load and thus affecting the normal operation of the mechanism, the whole mechanism needs to have good stiffness. To analyze the stiffness of such complex mechanical mechanism, finite element method can be used for simulation. Based on Abaqus finite element software, the stresses and deformations of the handling robot, especially its lifting system, were analyzed under rated working conditions; the stresses and overall deformations of key parts of the lifting system were obtained. The analysis results showed that the maximum stress and deformation of the lifting system did not exceed the predefined targets. The obtained analysis results provide an important reference for the actual performance of the lifting system.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132691962","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021355
Cheng Qian, Yingfeng Zhang
The rapidly changing market has stimulated the emergence of self-organizing mechanisms in intelligent manufacturing systems, where resources are generally capable of making decisions through intercommunications and interoperations to maximize system adaptivity. With the increasing number of exceptional events during manufacturing, those mechanisms might eventually fail to react timely to situations of multiple resource conflicts. In this work, we discussed the social networks consisting of various manufacturing resources and provided the relevant modeling techniques including the Resource Description Framework and the Finite State Machine. The function and behavior models can support the autonomous interactions and collaborations among resources, while the communication hub connected the resources and formed a peer-to-peer network. On this basis, the dynamic features of manufacturing networks including the exception propagation phenomenon can be analyzed. A case was studied to identify the bottleneck resources using the complex network theory. This work has provided an analytical platform to optimize manufacturing resources using complex network and graph theory.
{"title":"Modeling of manufacturing resources towards an interactive Resource Social Network","authors":"Cheng Qian, Yingfeng Zhang","doi":"10.1109/WCMEIM56910.2022.10021355","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021355","url":null,"abstract":"The rapidly changing market has stimulated the emergence of self-organizing mechanisms in intelligent manufacturing systems, where resources are generally capable of making decisions through intercommunications and interoperations to maximize system adaptivity. With the increasing number of exceptional events during manufacturing, those mechanisms might eventually fail to react timely to situations of multiple resource conflicts. In this work, we discussed the social networks consisting of various manufacturing resources and provided the relevant modeling techniques including the Resource Description Framework and the Finite State Machine. The function and behavior models can support the autonomous interactions and collaborations among resources, while the communication hub connected the resources and formed a peer-to-peer network. On this basis, the dynamic features of manufacturing networks including the exception propagation phenomenon can be analyzed. A case was studied to identify the bottleneck resources using the complex network theory. This work has provided an analytical platform to optimize manufacturing resources using complex network and graph theory.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134137023","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021498
Zhuangchao Zhan, Xingguo Han
Robots have the characteristics of perception, decision-making and execution, and are widely used in manufacturing. In practical production, mobile robots are increasingly widely used as the main part of material handling robots. Based on these reasons, this paper designs the 5 DOF material handling robot. The robot is loaded with Cortex-M3 kernel, which is composed of patrol module, mobile module, multifunctional module, task setting and image recognition. The tracking differentiator and PID algorithm are used for the velocity planning and control adjustment to improve movement stability and control accuracy of the robot. The experimental results show that the robot can complete the functions of autonomous patrol, material clamping and handling, task acquisition, color recognition and information display. It has the characteristics of small size, material grabbing, high degree of automation and simple maintenance. Its diversified functions can meet the dangerous and heavy handling tasks in production, and its high automation level can improve production efficiency.
{"title":"Design and Research of 5 DOF Material Handling Robot","authors":"Zhuangchao Zhan, Xingguo Han","doi":"10.1109/WCMEIM56910.2022.10021498","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021498","url":null,"abstract":"Robots have the characteristics of perception, decision-making and execution, and are widely used in manufacturing. In practical production, mobile robots are increasingly widely used as the main part of material handling robots. Based on these reasons, this paper designs the 5 DOF material handling robot. The robot is loaded with Cortex-M3 kernel, which is composed of patrol module, mobile module, multifunctional module, task setting and image recognition. The tracking differentiator and PID algorithm are used for the velocity planning and control adjustment to improve movement stability and control accuracy of the robot. The experimental results show that the robot can complete the functions of autonomous patrol, material clamping and handling, task acquisition, color recognition and information display. It has the characteristics of small size, material grabbing, high degree of automation and simple maintenance. Its diversified functions can meet the dangerous and heavy handling tasks in production, and its high automation level can improve production efficiency.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128944081","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021389
Hu Zheng, Libin Tan, Xiaoliu Yu
Aiming at the problem that the traditional fault diagnosis method is difficult to effectively extract the fault features of the high-speed train wheelset bearing signal, this paper proposes a fault diagnosis method based on the two-dimensional image method. First, the one-dimensional vibration signal is converted into a Two-dimensional grayscale image, eliminating the influence of expert experience on the feature extraction process. Then an improved network model is proposed, which can automate the process of feature extraction and fault diagnosis. Finally, this paper simulates the complex driving environment of high-speed trains by adding noise with different SNRs to the vibration signal and analyzes the influence of noise on the diagnostic ability of the method. The results show that the method is effective.
{"title":"A Fault Diagnosis Method for High-Speed Train Wheelset Bearings Based on Deep Learning","authors":"Hu Zheng, Libin Tan, Xiaoliu Yu","doi":"10.1109/WCMEIM56910.2022.10021389","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021389","url":null,"abstract":"Aiming at the problem that the traditional fault diagnosis method is difficult to effectively extract the fault features of the high-speed train wheelset bearing signal, this paper proposes a fault diagnosis method based on the two-dimensional image method. First, the one-dimensional vibration signal is converted into a Two-dimensional grayscale image, eliminating the influence of expert experience on the feature extraction process. Then an improved network model is proposed, which can automate the process of feature extraction and fault diagnosis. Finally, this paper simulates the complex driving environment of high-speed trains by adding noise with different SNRs to the vibration signal and analyzes the influence of noise on the diagnostic ability of the method. The results show that the method is effective.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129085964","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 : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021388
Hao Yin, Jinhua Xiao, Guoxian Wang
The production and sales of new energy vehicles has brought the recycling needs of retired Electric Vehicle (EV) battery for sustainable and green resource utilization. The purpose of this paper is to provide solutions for uncertain disassembly of EV battery in flexible production workshops. In this paper, a novel human-robot collaborative flexible re-manufacturing system is proposed to deal with the difficulties for the uncertain disassembly and re-manufacturing process of EV-battery recycling. The uncertainty in the disassembly process are analyzed to build the architecture of human-robot collaboration disassembly system. The D-H parameter method was used to analyze the kinematics of collaboration robot and its control system. The trajectory planning of collaboration robot is performed using the Rapidly-exploring Random Tree (RRT) algorithm. The neural network YOLOv7 algorithm is used to support the depth vision-based system to improve the recognition accuracy and motion accuracy for disassembly process.
{"title":"Human-Robot Collaboration Re-Manufacturing for Uncertain Disassembly in Retired Battery Recycling","authors":"Hao Yin, Jinhua Xiao, Guoxian Wang","doi":"10.1109/WCMEIM56910.2022.10021388","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021388","url":null,"abstract":"The production and sales of new energy vehicles has brought the recycling needs of retired Electric Vehicle (EV) battery for sustainable and green resource utilization. The purpose of this paper is to provide solutions for uncertain disassembly of EV battery in flexible production workshops. In this paper, a novel human-robot collaborative flexible re-manufacturing system is proposed to deal with the difficulties for the uncertain disassembly and re-manufacturing process of EV-battery recycling. The uncertainty in the disassembly process are analyzed to build the architecture of human-robot collaboration disassembly system. The D-H parameter method was used to analyze the kinematics of collaboration robot and its control system. The trajectory planning of collaboration robot is performed using the Rapidly-exploring Random Tree (RRT) algorithm. The neural network YOLOv7 algorithm is used to support the depth vision-based system to improve the recognition accuracy and motion accuracy for disassembly process.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115344929","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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