Multi-source domain adaptation method based on domain similarity for tool wear states identification

Single source domain transfer learning has been used in the field of tool wear states monitoring. However, this method ignores the case of transfer learning from multiple source domains and it is unreasonable to transfer knowledge from multiple source domains to the target domain with equal weights. Therefore, this article proposes a new multi-source unsupervised domain adaptation method based on domain similarity (SBMUDA) for tool wear states identification. This paper utilizes cosine distance to assess the similarity between each source domain and target domain. Based on domain similarity, the feature distance of each source and target domain and classification loss of the source domains are weighted to enhance attention to source domains with high similarity and reduce the negative transfer damage. Moreover, a multi-source domain model with weight sharing is established. By integrating the advantages of multiple source domains, more classification knowledge is transferred to the target domain, enhancing the model’s generalization ability. The milling experimental results show that the classification accuracy of multi-source domain transfer learning is significantly higher than that of the optimal single source domain, with a maximum improvement of 4.4%. Compared with other methods, this method has the highest average accuracy of 98.7% and the smallest standard deviation.