Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine Learning

Abstract

Computer-numerical control (CNC) machining has become the norm in most manufacturing businesses.
In order to maximize machining efficiency, prevent unintended damage, and maintain product quality all at once,
tool wear measurement is essential. Wear on the tools is typically an inevitable side effect of machining. Because it
endangers the machining process, flank wear, the most frequent type of tool wear, should be avoided. This study's
objective is to fill this growing need by developing a system that can use machine learning to monitor tool condition
during the turning process using MATLAB. The regression method with boosted decision trees and SVR with
Gaussian kernels are applied to predict the flank wear based on the vibration signal and cutting parameters. This
study found that the regression with boosted decision tree method has a lower mean average percentage error, 6.43%,
while SVR is 10.11%. Plus, R² for regression is slightly better than SVR. It shows that the system successfully
produced an accurate prediction of flank wear.