Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals

Abstract Sonic monitoring presents itself as one of the least invasive but easiest to implement methods of machine condition characterization. This work investigates the viability of categorically classifying cutting tool wear using only sonic output from a vertical milling center and proposes a statistical model of milling acoustic signals as well as a novel machine learning-integrated method of acoustic signal differentiation. To this end, a deep convolutional neural network is used for data classification. Experimental results support the proposed sonic model and demonstrate that tool wear classification accuracy as high as 99.5% is possible using a two-dimensional deep convolutional neural network.