Precise modeling of cutting forces based on domain adaptation extreme learning machine under small sample conditions

Abstract

Given the high cost and complexity associated with acquiring a large number of experimental data of cutting forces, coupled with the challenges of overfitting and weak generalization in machine learning models for cutting forces prediction under small sample conditions, we propose two methods that employ the domain adaptation extreme learning machine (DAELM) algorithms to establish precise prediction models of cutting forces in small sample scenarios. In these methods, the large sample theoretical dataset of cutting forces calculated by parallel-sided shear zone model is used as the source domain dataset, while the small sample experimental dataset of cutting forces obtained by metal cutting experiments serves as the target domain dataset, and the cutting forces prediction models based on transfer learning are established employing DAELM algorithms. Applying these methods, precise prediction models of cutting forces in orthogonal cutting of 6061-T6 aluminum alloy have been established. Compared to the cutting force prediction models established using traditional neural network algorithms, those established using the proposed methods exhibit higher prediction precision and stronger generalization ability, even when only a small sample experimental dataset of cutting forces is available. The research findings can be applied to the transfer learning-based precise modeling of other continuously varying physical quantities in metal cutting processes under small sample conditions.