Statistical Analysis for Detection Cutting Tool Wear Based on Regression Model

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter Technique, I-kaz was used for developed regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out on a CNC turning machine Colchester Master Tornado T4 in dry cutting condition, and Kistler 9255B dynamometer was used to measure the cutting force signals, which then stored and displayed in the DasyLab software. A number of force signals from machining was analyzed and each has a characteristic value called I-kaz 3D coefficient. These coefficients have relationship with flank wear land (VB). Results of regression model shows the I-kaz 3D coefficient value decreases when the tool wear increases. This result can be used for real time tool wear monitoring.