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.

在计算机数控（CNC）过程中对刀具进行有效的预测和健康管理（PHM），可以显着减少停机时间并减少整个制造过程中的损失。近年来，深度学习算法已经证明了PHM的巨大潜力。然而，仍然受到在实际制造情况下可用于有效算法训练的有限数量数据的挑战的阻碍。为了解决这个问题，在这项研究中，开发了一种支持转移学习的卷积神经网络（CNN）方法，以预测切削工具的健康状态。通过集成转移学习策略，CNN可以基于少量的相关刀具图像有效地执行刀具健康状态预测。进行了定量基准测试，并使用几种优化技术对基于六个典型CNN模型的已开发方法的性能进行了分析。结果表明所开发方法的适用性，特别是使用ResNet-18来估计切削刀具的磨损宽度。因此，通过利用CNN的集成设计和转移学习，可以为刀具建立可行的PHM策略，以支持实际的CNC加工应用。