Grinding wheel condition prediction with discrete hidden Markov model using acoustic emission signature

Abstract

Tool Condition Monitoring (TCM) is a vital activity to monitor and maintain the quality of products manufactured in any machining process without any manual intervention. Hidden Markov Models (HMM) are developed in this study for predicting grinding wheel conditions in the auto-feed surface grinding process using Acoustic Emission (AE) signatures captured during the grinding operation. A correlation between AE features and tool conditions are established using Baum-Welch and Viterbi algorithms. HMMs proposed in this study are integrated with the K-means clustering algorithm. The clustered data are represented as an integer sequence and are divided into three tool states such as ‘sharp’, ‘intermediate’ and ‘worn out’. HMM, models are created for each state of the grinding wheel. The performances of the HMMs are evaluated using log-likelihood measure. It is observed that HMM models build with AE–RMS feature are predicting the grinding wheel conditions with good accuracy.