Estimation of Tool Wear Rate in Orthogonal Cutting Using Experimental and Statistical Approach

Abstract

This study presents a new methodology to estimate tool wear rate in orthogonal cutting based on experimental data and statistical approach. In metal cutting tool wear is strongly influenced by cutting forces, speed, feed, and depth of cut. Based on these variables and cutting forces measured by dynamometer, tool wear is estimated with desired accuracy. The major objective of this study is to develop a model (equation) to predict the tool wear in orthogonal cutting by regression analysis. The work presented in this paper uses the data of conducted experiments. This data is statistically analyzed to develop a model, which can predict the wear rate of cutting tool used in orthogonal cutting operation considering different machining variables such as, spindle speed, depth of cut, feed. The cutting forces predicted by the regression analysis equation (model) is closely matching with those with results obtained experimentally. So based on another statistical equation tool wear rate is estimated over the wide range of speed, feed and depth of cut values required for different types of machining operations. The proposed methodology can be used for developing another model which will predict the tool wear rate for other machining processes.