Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process

Process monitoring is necessary in machining operation to increase productivity, improve surface quality, and reduce unscheduled downtime. Tool wear and breakage are important and common source of machining problems due to high temperatures and forces of the machining process. Therefore, it is highly beneficial to develop an online tool condition monitoring (TCM) system. This paper investigates a robust tool wear monitoring system for milling operation. Recent developments in machine learning, in particular deep learning methods, result in significant improvement in automation of different industries. Therefore, in this research, we employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation. Wavelet packet-based features are extracted for tool wear monitoring as a powerful time-frequency fault indicator. Moreover, a hybrid feature extraction method is proposed using wavelet time-frequency transformation and spectral subtraction algorithms to intensify the effect of tool wear in the signal and reduce the effect of other cutting parameters. CNN-based monitoring systems are compared with three other machine learning methods (support vector machine, Bayesian rigid network, and K nearest neighbor method) as the baseline. The research is validated using different datasets. The algorithms are implemented and compared using experimental force and vibration signals from LIPPS lab of ETS university as well as using current signals as the fault indicator from Nasa_Ames dataset.