| Autor: |
Tao Xu, Binguo Fu, Yanfei Jiang, Jinghui Wang, Guolu Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Materials Research and Technology, Vol 31, Iss , Pp 1270-1281 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2238-7854 |
| DOI: |
10.1016/j.jmrt.2024.06.169 |
| Popis: |
Machine learning combined with traditional experimental methods can promote the efficient research and development of materials. In this work, five kinds of algorithm models combined with a genetic algorithm (GA) were used to optimize the compositions of the alloyed high manganese steel. And then the effect of solid solution temperatures on the microstructure, mechanical properties, and impact wear properties of the steel were systematically investigated. The results showed that Categorical Boosting (CB) model exhibited the high validation accuracy (R2 > 0.95, RMSE |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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