A domain adversarial graph convolutional network for intelligent monitoring of tool wear in machine tools

The prediction of tool wear on CNC machine tools holds great significance for enhancing both the safety of tool machining and the quality of product machining. The current data-driven prediction methods for tool wear in machining have yielded very promising results. However, tool wear prediction models for the same machine tool under different machining conditions lack universality since the data feature space distribution under varying work conditions is different. To bridge the gap between source and target domains, data structure information is all too commonly ignored beyond domain and class labels. To attack these problems, a transfer learning method for acquiring domain-invariant features of tool wear based on a dynamic domain adversarial graph convolutional network (DDAGCN) is proposed in this paper. The network integrates three effective alignment mechanisms - domain, data structure, and class centroid alignments - and evaluates the relative importance of the marginal and conditional distributions quantitatively. In addition, a dynamic factor is introduced to quantitatively assess the relative significance of marginal and conditional distributions, which effectively facilitate the learning of domain-invariant features and decrease differences between source and target domains. The superiority of the proposed method was validated through experiments under two distinct conditions.