Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal

Abstract

The monitoring of machining process can improve the quality of product and economy of production. The monitoring system helps to recognize and monitor the surface roughness, dimensional tolerance and tool condition. In this way, the condition monitoring system provides precise dimensional products, high productivity and enhanced machine tool life. This paper presents the classification of healthy and faulty conditions of the face milling tool using Decision tree (J48 algorithm) technique through machine learning approach. The sound signals of the face milling tool under healthy and faulty conditions are acquired. A set of discrete wavelet features are extracted from the sound signals using discrete wavelet transform (DWT) method. Decision tree technique is used to select prominent features out of all extracted features. The selected features are fed to the same algorithm for classification. Output of the algorithm is used to study and categorize the tool conditions. The decision tree model has provided a good classification accuracy of about 81% for the given sound signals and can be considered for fault diagnosis/condition monitoring. From the experimental results, it is suggested that the proposed method which comprises of decision tree and DWT techniques with sound signals can be recommended for the applications of fault diagnosis of the face milling tool. � 2017 Elsevier Ltd. All rights reserved.