Application of higher polynomial with variable coefficients to investigation of tool wear and fracture-fusion model in monitoring the cutting processes of turning and end-milling

Authors:
Publisher website
Google Scholar
Abstract
This paper presents a set of feature parameters characterizing tool wear and fracture in turning and endmilling processes which are extracted from the acoustic emission (AE) and vibration signals through frequency analysis and multistep extraction analysis. A tool wear and fracture fusion model is made on the relevance analysis trend analysis and the least square regression. Based on the recognition algorithm of selfsimilar analysis the value of tool wear can be calibrated. Using the abovementioned research results a realtime tool monitoring system is developed for turning and endmilling processes with higher rate of success fewer prediction errors and faster response time