Research on Optimization of Robot Grinding and Polishing Parameters Considering Time-Varying Wear

被引:0
|
作者
Wang C. [1 ,2 ]
Zheng Q. [1 ]
Xiao J. [1 ]
Zhou Y. [1 ]
Liu H. [1 ]
Huang T. [1 ]
机构
[1] School of Mechanical Engineering, Tianjin University, Tianjin
[2] Craftsman Research Institute, Weichai Power Co.,Ltd., Weifang
关键词
material removal model; parameter optimization; support vector regression; time-varying wear;
D O I
10.11784/tdxbz202110006
中图分类号
学科分类号
摘要
Addressing the problem of grinding and polishing tool wear in the automatic grinding and polishing process,an issue that makes it difficult to establish an accurate grinding and polishing removal model to achieve a stable and consistent grinding and polishing effect,a robot grinding and polishing parameter optimization method considering time-varying wear is proposed. First of all,combining the rich information in the physical modeling method with the tracking learning characteristics of the regression modeling method,a regression model for grinding and polishing material removal that integrates prior knowledge is proposed. Such a model will greatly reduce the large amount of experimental data required. The proposed model can track the changes in grinding and polishing wear. The prediction results of the model used are compared with theoretical models and the support vector regression prediction model proposed in other research. Finally,the prediction model obtained is used to optimize the robot grinding and polishing parameters,and experimental verification is carried out on the polishing experiment platform of the hybrid robot. The results show that the proposed grinding and polishing material removal model fused with prior knowledge has higher prediction accuracy and less experimental data. The proposed optimization method for robot grinding and polishing parameters can effectively compensate for the wear of grinding and polishing tools,ensure the consistency of material removal in the grinding and polishing process,and improve the efficiency of grinding and polishing. © 2023 Tianjin University. All rights reserved.
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页码:127 / 136
页数:9
相关论文
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