A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning

Junhui Zhang, Haiyan Gao, Yahui Liu, Jun Wang

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

The advent of revolutionary advances in artificial intelligence (AI) has sparked significant interest among researchers across a spectrum of disciplines. Machine learning (ML) has become a potent tool for advancing materials research, offering solutions beyond traditional methods. This study discusses traditional machine learning (TML) and deep learning (DL) algorithms, providing a concise overview of commonly used ML algorithms in materials research. It also examines the general workflow of ML applications in superalloys, focusing on key aspects such as data preparation, feature engineering, model selection, and optimization, offering insights into the ML modeling process. From the perspective of the materials tetrahedron, this review explores ML applications in the research and development of superalloy composition, microstructure, processing, and performance. It highlights the use of advanced ML models to predict material properties, optimize alloy compositions and microstructure, and enhance manufacturing processes. It covers the use of advanced ML models and discusses the prospects of ML in superalloy research, highlighting its transformative potential in alloy material science.

源语言英语
文章编号224106
页(从-至)106-124
页数19
期刊JOM
77
1
DOI
出版状态已出版 - 1月 2025
已对外发布

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