| Autor: |
Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Materials Research and Technology, Vol 33, Iss , Pp 2672-2682 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2238-7854 |
| DOI: |
10.1016/j.jmrt.2024.09.237 |
| Popis: |
To develop medium-Mn steels with an ultimate tensile strength (UTS) exceeding 2 GPa and excellent ductility, we created a highly accurate UTS prediction machine learning (ML) model using a boosted decision tree model and 1520 dataset of tensile properties of medium-Mn steels with micro-alloying elements. We also optimized the hyper-parameters of a genetic algorithm (GA) using the Shannon diversity index to enhance search efficiency while retaining diversity. In a high-dimensional search space with millions of potential combinations, the ML-GA approach efficiently identified diverse chemical compositions and austenitizing conditions to achieve UTSs above 2 GPa. The k-means clustering method then grouped them into five distinct specimens based on similarities. These five specimens, fabricated using inversly designed chemical compositions and austenitizing temperatures, successfully exhibited UTSs exceeding 2 GPa and greater ductility compared to hot-stamped C steels. These excellent tensile properties were attributed to grain refinement resulting from the low austenitizing temperature and the pinning effect of micro-alloying element carbides, such as TiC and VC. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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