Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization

Autor:	Longjian Li, Jinchuan Jie, Xiaoyu Guo, Gaojie Liu, Huijun Kang, Zongning Chen, Enyu Guo, Tongmin Wang
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Machine learning feature engineering screening Cu-Ni-Si alloy multi-objective optimization Materials of engineering and construction. Mechanics of materials TA401-492
Zdroj:	Materials Research Letters, Pp 1-9 (2024)
Druh dokumentu:	article
ISSN:	21663831 2166-3831
DOI:	10.1080/21663831.2024.2424933
Popis:	Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/a7e412ffbdad48969c8cc3121fb9eded Zobrazit plný text záznamu View record in DOAJ