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

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 R-a, R-t and R-z 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 R-a, R-t and R-z are measured. A probabilistic model consisting of tool wear and surface parameters is developed using Naive based classifier. This model is capable to predict a particular stage of tool wear given randomly selected values of R-a, R-t and R-z: 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 R-a= 1:98, R-t= 17:17 and R-z =. 18:75