Self-adjusting on-line cutting condition for high-speed milling process

Abstract

The paper presents an intelligent control system for self-adjusting on-line cutting condition for high speed machining (self-HSM) with considering the tool-wear amount to keep the machined product’s quality in allowable limit. For realizing the self-HSM, the empirical analysis of variance (ANOVA) and artifical neural network (ANN) are used. The ANOVA is used for generating the empirical functions which are used as the boundary condition as well as constraint evaluation. The ANN is used for generating the new optimal cutting condition. Then, the self-HSM updates this cutting condition on the real machine — HS Super MC500. The new optimal cutting parameter is sent to the controller for updating the new machining condition to keep the machined part’s quality. The integration of the empirical analysis and ANN enables generating the optimal cutting parameters correctly and efficiently for high-speed milling.