Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features

Various methods of tool condition monitoring techniques are used to control the tool wear during machining in CNC machine tools. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is used to probe into the structural information hidden in the signals acquired. This paper discusses machine tool condition monitoring of carbide tipped tool by using Naïve Bayes and Bayes Net classifiers and compares the results of histogram features with the statistical features to establish better classification among the two. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better feature–classifier combine. The results are discussed.