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典型文献
Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining
文献摘要:
To improve surface accuracy of the work-piece and ob-tain potentially valuable information,a dynamic milling force pre-diction model was proposed based on data mining.In view of the current dynamic milling force obtained through finite element simulation and analytical calculation,in the finite element model-ing,the model built is inevitably different from the actual working conditions,and the analytical calculation is slightly cumbersome and complex,and a dynamic milling force prediction model based on data mining is proposed.The model was established using a combination of regression analysis and Radial Basis Function(RBF)neural network.Using data mining as a means,the internal relationship between milling force,cutting parameters,tempera-ture,vibration and surface quality is deeply analyzed,and the in-fluence of dynamic milling force changes on different situations is extracted and summarized by the methods of cluster analysis and correlation analysis.The results show that the proposed dynamic milling force model has a good prediction effect,ensures the pro-duction quality,reduces the occurrence of flutter,improves the surface accuracy of the work-piece,and provides a more accurate basis for the selection of process parameters.
文献关键词:
作者姓名:
JIN Lan;WU Lishuang;ZHANG Xuefeng;XIE Liming
作者机构:
School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,Gansu,China
引用格式:
[1]JIN Lan;WU Lishuang;ZHANG Xuefeng;XIE Liming-.Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining)[J].武汉大学自然科学学报(英文版),2022(05):439-452
A类:
B类:
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AB值:
0.556631
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