Drill wear monitoring through current signature analysis using wavelet packet transform and artificial neural network

Abstract

In automated manufacturing systems, tool wear monitoring plays an important role in ensuring the dimensional accuracy of the workpiece and automatic cutting process without any failure. It is, therefore, very essential to develop simple, reliable, process condition independent and cost effective online tool wear monitoring system. In this work, an attempt has been made to develop a drill tool wear monitoring system which fulfills most of the above essential requirements. A cost effective Hall-effect current sensor, which does not interfere with the process, has been used for acquiring motor current signature during drilling under different cutting conditions. A more advanced signal processing technique, wavelet packet transform has been implemented on the acquired current signature to extract features which are more sensitive to drill wear and less sensitive to the process conditions. A multilayer neural network model has been developed to correlate the extracted features with drill wear. Experimental results show that the proposed drill wear monitoring system can successfully predict the drill wear with acceptable accuracy.