Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining

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.