Online Prediction of Cutting Temperature Using Self-Adaptive Local Learning and Dynamic CNN

To maintain a high-quality machining process, it is necessary to develop an online cutting temperature prediction model accurately. However, using the conventional cutting temperature modeling methods, it is difficult to capture the continuous and multivariate time-variant characteristic of cutting temperature. Recently, local learning and switching dynamics have been employed to identify the time-variant characteristic. However, it requires quantitative and precise analysis with certain switching patterns, which are hard to realize in the machining process. In this article, a self-adaptive local learning method integrated with dynamic convolutional neural networks (DCNN) is proposed. The moving window strategy and t-test analysis of performance maps are first to give the local domains. Then, a DCNN is designed to capture spatial cross-correlations and temporal autocorrelations in each local domain. Finally, the local models are ensemble using Bayesian ensemble learning. The effectiveness of the proposed method is demonstrated through different process information and tool conditions.