MODELLING OF TURNING PROCESS FOR PREDICTION OF TOOL WEAR

Abstract: The tool wear is measured by using a Profile Projector PP-200. In this indirect measurement technique the tools wear parameters are cutting speed, feed and depth of cut. In addition to that software computer techniques adaptive neuro-fuzzy inference system is applied for Modelling prediction. The objective of this study is also to correlate flank wear in regression and compare with ANFIS in prediction studies. The proposed model is for prediction of flank wear of the mild steel work piece. The machining experiments are performed under various cutting conditions using Cutting speed, Feed and Depth of Cut. The flank wear is measured. It is also observed that the flank wear prediction accuracy of Adaptive neuro fuzzy inference system using trapezoidal membership function is better than regression analysis. The flank wear prediction accuracy with ANFIS is 87.87% as input parameters are cutting speed, feed and depth of cut.

Keywords: Turning, ANFIS, Regression, Flank Wear, Crater Wear.