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

IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM Pub Date : 2024-10-10 DOI:10.1007/s11837-024-06922-7
Junhui Zhang, Haiyan Gao, Yahui Liu, Jun Wang
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引用次数: 0

Abstract

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.

超级合金成分、微观结构、加工和性能的机器学习应用综述
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来源期刊
JOM
JOM 工程技术-材料科学:综合
CiteScore
4.50
自引率
3.80%
发文量
540
审稿时长
2.8 months
期刊介绍: JOM is a technical journal devoted to exploring the many aspects of materials science and engineering. JOM reports scholarly work that explores the state-of-the-art processing, fabrication, design, and application of metals, ceramics, plastics, composites, and other materials. In pursuing this goal, JOM strives to balance the interests of the laboratory and the marketplace by reporting academic, industrial, and government-sponsored work from around the world.
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