| Autor: |
Yuan Liu, Shi-Zhong Wei, Tao Jiang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Journal of Materials Research and Technology, Vol 24, Iss , Pp 9754-9764 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2238-7854 |
| DOI: |
10.1016/j.jmrt.2023.05.105 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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