{"title":"Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining","authors":"Lan Jin, Lishuang Wu, Xuefeng Zhang, L. Xie","doi":"10.1051/wujns/2022275439","DOIUrl":null,"url":null,"abstract":"To improve surface accuracy of the work-piece and obtain potentially valuable information, a dynamic milling force prediction 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 modeling, 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, temperature, vibration and surface quality is deeply analyzed, and the influence 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 production 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.","PeriodicalId":23976,"journal":{"name":"Wuhan University Journal of Natural Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wuhan University Journal of Natural Sciences","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1051/wujns/2022275439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Multidisciplinary","Score":null,"Total":0}
引用次数: 1
Abstract
To improve surface accuracy of the work-piece and obtain potentially valuable information, a dynamic milling force prediction 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 modeling, 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, temperature, vibration and surface quality is deeply analyzed, and the influence 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 production 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.
期刊介绍:
Wuhan University Journal of Natural Sciences aims to promote rapid communication and exchange between the World and Wuhan University, as well as other Chinese universities and academic institutions. It mainly reflects the latest advances being made in many disciplines of scientific research in Chinese universities and academic institutions. The journal also publishes papers presented at conferences in China and abroad. The multi-disciplinary nature of Wuhan University Journal of Natural Sciences is apparent in the wide range of articles from leading Chinese scholars. This journal also aims to introduce Chinese academic achievements to the world community, by demonstrating the significance of Chinese scientific investigations.