A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning
Junhui Zhang, Haiyan Gao, Yahui Liu, Jun Wang
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.