Domain adaptation extreme learning machines for regression and their application in precise modeling of cutting forces under small sample conditions

Abstract

Existing transfer learning algorithms for cutting forces modeling do not consider the impact of conditional shift on prediction results, leading to principle errors. When the sample size of the target domain dataset is small, these principle errors become more pronounced, resulting in larger prediction errors that cannot be ignored. To address this issue, we propose two transfer learning algorithms that consider the conditional shift, specifically designed for regression tasks such as cutting forces modeling: the regression domain adaptation extreme learning machine algorithms RDAELM-CEOD-A and RDAELM-CEOD-B. These two algorithms, compared to those designed for classification tasks, eliminate conditional shift by minimizing the conditional embedding operator discrepancy instead of maximum mean discrepancy, which makes them more suitable for regression tasks. With the large sample theoretical dataset of cutting forces (calculated from the unequal division shear zone model) as the source domain dataset and the small sample experimental dataset (obtained from cutting experiments) as the target domain dataset, we employ the proposed algorithms to develop prediction models of cutting forces in orthogonal cutting of 6061-T6 aluminum alloy and 42CrMo4 steel. Simulation of the constructed model shows that the prediction errors decrease as the sample size of the target domain dataset increases and are significantly smaller than those of similar models built using other domain adaptation extreme learning machine algorithms. The research findings can be extended to the precise transfer learning modeling of other continuously varying physical quantities under small sample conditions.