A zero-shot learning for property prediction of wear-resistant steel based on Multiple-source

Abstract

Abstract In order to address the scarcity of C-Cr-V-Mo steel samples, a zero-shot transfer component analysis (TCA) based on multi-source is proposed. TCA maps the features of multiple sources composed of different kinds of wear-resistant steels and target domain to the reproducing kernel Hilbert spaces (RKHS). And the proposed fitness parameter α derived from the maximum mean discrepancy (MMD) allows multiple sources to affect the prediction to varying degrees. The support vector regression (SVR) model, established after TCA, can then predict the hardness without homologous samples. The matrix is rapidly predicted by the minimum distance from the sample to the cluster centers of matrix. The (Cr+V)/C, V/Cr and predicted hardness are added to feature space and the abrasion loss of samples quenched at high temperature are predicted using these quenched at low temperature. Experiments show that the multi-source based TCA+SVR model improves the prediction accuracy of hardness of C-Cr-V-Mo steel with R of 0.98 and MAE less than 1.4HRC under zero-shot condition. The primary matrix is quickly identified as martensite. The abrasion loss is mostly effected by hardness, (Cr+V)/C and V/Cr, which is predicted with R of 0.95, MAE of 5.23 mg.