Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning

Abstract

Multivariate machining quality prediction of thin-walled parts with multiple machining features is a complex problem due to different data distribution between training and unlabelled test samples. Traditional quality prediction methods ignore the correlation of multiple quality labels and do not consider changes in data distribution, resulting in low accuracy of multivariate quality prediction. Therefore, a multivariate quality prediction method using multi-task parallel deep transfer learning is proposed to solve this problem. Specifically, a multi-output quality prediction model of cross-machining features is constructed through the joint design of multi-task parallel learning and deep transfer learning. Furthermore, a domain matcher is designed to form multiple transfer strategies, which can be used for dynamic matching of multi-source and multi-target machining features with multiple quality labels. The domain invariant data features through dynamic domain adaptation are extracted to deal with data distribution discrepancy between the source and target domains. Finally, the results of multiple comparison experiments show that the proposed method can effectively achieve the accurate quality prediction of the target domain with unlabelled labels and different distributions. Compared with the traditional methods, the proposed method has improved by 8.34%, 7.14%, and 9.09%, respectively, in MAE, RMSE, and score on average.