Journal of Materials Research and Technology (May 2023)

Property prediction for high-chromium high-vanadium steel based on transfer component analysis with few-shot guided

  • Yuan Liu,
  • Shi-Zhong Wei,
  • Tao Jiang

Journal volume & issue
Vol. 24
pp. 9754 – 9764

Abstract

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The transfer learning model improves accuracy by reducing the marginal and conditional probability distribution discrepancy between source and target domains. Based on the hypothesis of the ideal carbides of high-chromium high-vanadium steel, a mass fraction ratio written as (Cr + V)/C is deduced as vital feature to narrow the marginal probability distribution discrepancy. To align the conditional probability distribution of the source domain with the target domain, a few-shot guided transfer component analysis (TCA) method is proposed that a limited number of labeled samples taken from the target domain are used to guide the mapping. Then, the V/Cr combines with the optimal (Cr + V)/C is proposed to predict the composition of sample with the best wear resistance. Experimental results show that the proposed few-shot guided TCA method can considerably improve the prediction accuracy (R is higher than 0.99, RMSE is lower than 0.63HRC). The constructed (Cr + V)/C is the most critical feature. In addition, the predicted sample consisting of 2.1%C, 4%Cr, 4%V and 1.5%Mo has the best wear resistance with minimal abrasion weight loss in the test.

Keywords