An improved domain adaption method for roughness prediction of milling surfaces under variable processes

Abstract

The cutting processes of complex products are complicated and various, and the processing data is a small sample. These characteristics lead to the poor generalization and overfitting problems of the roughness prediction model, further resulting in a reduction in prediction accuracy. To address this issue, a domain adaption method combining Multi-Representation Adaptation Network with Deep Residual Shrinkage Network (DRSN-MRAN) is proposed for roughness prediction of milling surfaces under variable processes. Firstly, regarding to the issues of tedious noise reduction and limited feature extraction, the DRSN is proposed to extract underlying feature information. Subsequently, the MRAN is proposed to address the distribution differences of multi-scale features in the source and target domains and obtain an integrated loss function for the prediction model. In the MRAN, the Conditional Maximum Mean Discrepancy (CMMD) based domain adaptor is introduced to construct the domain adaptive loss function and align the feature distributions between the source and target domains in different spaces and the same category. Finally, a multi-process milling experiment is designed and conducted to obtain a small sample of milling roughness dataset, and the proposed method is verified. It is demonstrated that the DRSN-MRAN can effectively extract domain-invariant features between the source and target domains with limited samples and accurately predict the roughness of milling surfaces under variable processes.