Indian Journal of Science and Technology

Abstract

Objective: Tool condition monitoring is an important aspect of the modern day manufacturing system. It plays a significant role in increasing the efficiency of machining operation by identifying defects at a very early stage. Tool wear decreases the life of the tool considerably, increases the length of the machining process, also affects the surface finish and the dimensional accuracy of the product. To identify whether the tool is in a good or faulty condition, a monitoring system is essential. Method/Analysis: The fault diagnosis of the single point cutting tool was accomplished with the vibration signals obtained from auniaxial accelerometer attached to the cutting tool in a lathe machine. In this study, three different spindle speeds, feed rates and depth of cuts and four different wear levels of cutting tool are considered. Statistical data obtained from the signals is classified using a decision tree algorithm to get substantial features. The recognized features are considered in classifying data by using Simple CART classifier. Findings: The accuracy of the classifier was found to be 73.38% for the model with all the signals combined. The classification accuracy was observed to improve with the reduction in complexity of the model. The classification accuracy obtained for the model with only varying feed rate and depth of cut was in the range of 81–87 %. On further reduction of the model to have varying depth of cut was found to have a classification accuracy in the range of 81.5–91 %. The model with all the parameters independent yielded classification accuracy in the range of 81–100 %. Applications/Improvements: This study broadly analysed the use of simple CART classifier to diagnose fault in the cutting tool during machining. It can be used to increase productivity and reduce machine downtime. The improvements can be made to this study by considering different feature extraction techniques for more reliability
Keywords: Decision Tree, Feature Extraction, Simple CART, Statistical Features, Tool Condition Monitoring