Prediction and analysis of material removal characteristics for robotic belt grinding based on single spherical abrasive grain model

被引:0
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作者
Yang, Zeyuan [1 ,2 ,4 ]
Chu, Yao [1 ,4 ]
Xu, Xiaohu [1 ,2 ,4 ]
Huang, Haojie [1 ,2 ,4 ]
Zhu, Dahu [2 ,3 ,4 ]
Yan, Sijie [1 ,2 ,4 ]
Ding, Han [1 ,2 ,4 ]
机构
[1] State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan,430074, China
[2] Blade Intelligent Manufacturing Division, HUST-Wuxi Research Institute, Wuxi,214174, China
[3] Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan,430070, China
[4] Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan,430070, China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Elastic deformation - Grinding (machining) - Poisson ratio - Mean square error - Elastic moduli - Pressure distribution - Robotics - Wheels;
D O I
暂无
中图分类号
学科分类号
摘要
Comprehensive study of the microscopic material removal mechanism remains an open challenge facing the robotic belt grinding of complex geometries. In the present paper, a new material removal rate (MRR) model is developed underlying the motion trajectory of single spherical abrasive grain by taking into consideration the elastic deformation of the heterogeneous contact wheel. In this model, the contact pressure distribution at the contact wheelworkpiece interface is determined by converting the three-dimensional contact problem into a one-dimensional linear spring model with respect to the large deformation of the contact wheel. In addition, the combined modulus and Poisson's ratio of the heterogeneous contact wheel are further considered based on the geometric relation of the Young's modulus and Poisson's ratio. Compared with the existing MRR model, the proposed MRR model can significantly reduce both the root mean square error (RMSE) and mean absolute percentage error (MAPE) values from 2.401 to 1.725, and 18.426% to 14.942%, respectively. Particularly, an optimization strategy from the perspective of process parameters is implemented to balance the grinding quality and efficiency. © 2020
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