Transfer learning enabled convolutional neural networks for estimating health state of cutting tools

Abstract Effective Prognostics and Health Management (PHM) for cutting tools during Computerized Numerical Control (CNC) processes can significantly reduce downtime and decrease losses throughout manufacturing processes. In recent years, deep learning algorithms have demonstrated great potentials for PHM. However, the algorithms are still hindered by the challenge of the limited amount data available in practical manufacturing situations for effective algorithm training. To address this issue, in this research, a transfer learning enabled Convolutional Neural Networks (CNNs) approach is developed to predict the health state of cutting tools. With the integration of a transfer learning strategy, CNNs can effectively perform tool health state prediction based on a modest number of the relevant images of cutting tools. Quantitative benchmarks and analyses on the performance of the developed approach based on six typical CNNs models using several optimization techniques were conducted. The results indicated the suitability of the developed approach, particularly using ResNet-18, for estimating the wear width of cutting tools. Therefore, by exploiting the integrated design of CNNs and transfer learning, viable PHM strategies for cutting tools can be established to support practical CNC machining applications.