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

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Vydáno v:JOM vol. 77, no. 1 (Jan 2025), p. 106
Hlavní autor: Zhang, Junhui
Další autoři: Gao, Haiyan, Liu, Yahui, Wang, Jun
Vydáno:
Springer Nature B.V.
Témata:
Materials research
Material properties
Optimization
Workflow
Data processing
Tetrahedra
Research & development > R&D
Manufacturing
Machine learning
Microstructure
Performance evaluation
Superalloys
Cognition & reasoning
Materials science
Artificial intelligence
Oxidation
Corrosion resistance
Temperature
Algorithms
Engineering
Deep learning
Alloys
Gas turbine engines
Composition
Process engineering
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Abstrakt: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.
ISSN:1047-4838
0022-2674
0148-6608
0098-4558
DOI:10.1007/sll837-024-06922-7
Zdroj:Science Database