Correlating tool wear and surface integrity of a CNC turning process using Naïve based classifiers

Abstract

Surface finish is an important phenomenon in hard turning. There are many factors which can influence the finishing of a product. Literature review reveals that substantial research has been performed on hard machining, still relationship of tool wear and surface finish parameters like [Formula: see text] and [Formula: see text] is not established as the process is so dynamic and transient in nature. As a result, most of the responses like tool wear, surface integrity parameters, cutting force, and vibration are random in nature. In this investigation, Topic Modelling (TM), a relatively new topic particularly used in machine learning is applied to determine a particular stage of tool wear. Tool wear is divided into three distinct groups namely initial stage (IS), progressive stage (PS), and exponential stage (ES) from a number of experimental observations. Then, surface parameters namely [Formula: see text] and [Formula: see text] are measured. A probabilistic model consisting of tool wear and surface parameters is developed using Naïve based classifier. This model is capable to predict a particular stage of tool wear given randomly selected values of [Formula: see text] and [Formula: see text] To validate this probabilistic model, an alternative machine learning method called multinomial logistic regression is used. Each of this method indicates that the tool has reached to exponential stage when [Formula: see text] and [Formula: see text] =. [Formula: see text]