Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features

Abstract

It is important to develop an intelligent tool condition monitoring system to increase productivity and promoting automation in metal cutting process. Many attempts have been made in the past to develop such systems using signals from various sensors such as dynamometer, current, accelerometer, acoustic emission, current and voltage, etc. But the successes of different sensor based systems are limited due to the complexity of tool wear process. The research is still ongoing for improved tool condition monitoring system with applications of advance signal processing techniques and artificial intelligent models. In this study, tool conditions are monitored using the vibration and acoustic emission signatures during high speed machining of titanium alloy (Ti-6Al-4V). Using discrete wavelet transforms wavelets coefficients of vibration and acoustic emission signals are extracted using haar, daubechies, biorthogonal and reverse biorthogonal wavelets. Machine learning algorithms such as decision tree, naive bayes, support vector machine and artificial neural networks are used to predict the tool condition. Results indicate the effectiveness of acoustic emission and vibration data using wavelets for classifying the tool conditions with the aid of machine learning algorithms. A correlation is established between the tool conditions and sensor data. Support vector machine trained by vibration data appears to be predicting the tool conditions with good accuracy compared to decision trees, naive bayes and artificial neural network. Results obtained in this study will be useful to develop an intelligent on-line tool condition monitoring system.