打破数据壁垒：通过新的机器学习框架推进高熵合金的相位预测

IF 1.3 4区材料科学 Q3 METALLURGY & METALLURGICAL ENGINEERING

Canadian Metallurgical Quarterly Pub Date : 2024-08-29 DOI:10.1080/00084433.2024.2395674

Amitava Choudhury, Sandeep Kumar

引用次数: 0

摘要

高熵合金（HEAs）是工业 4.0 背景下一项前景广阔的新技术，体现了互联、自动化和实时数据的原则。高熵合金（HEA）在工业 4.0 的背景下是一项大有可为的发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Breaking data barriers: advancing phase prediction in high entropy alloys through a new machine learning framework

High entropy alloys (HEAs) represent a promising ADVANCEMENT in the context of Industry 4.0, embodying the principles of interconnectedness, automation and real-time data. The formation of phases i...

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来源期刊

Canadian Metallurgical Quarterly 工程技术-冶金工程

CiteScore

1.90

自引率

11.10%

发文量

审稿时长

6 months

期刊介绍： Canadian Metallurgical Quarterly publishes original contributions on all aspects of metallurgy and materials science, including mineral processing, hydrometallurgy, pyrometallurgy, materials processing, physical metallurgy and the service behaviour of materials. An invaluable resource for international researchers and professionals engaged in interdisciplinary research in metallurgy and materials science.