TY - JOUR
T1 - A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning
AU - Zhang, Junhui
AU - Gao, Haiyan
AU - Liu, Yahui
AU - Wang, Jun
N1 - Publisher Copyright:
© The Minerals, Metals & Materials Society 2024.
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85206352487&partnerID=8YFLogxK
U2 - 10.1007/s11837-024-06922-7
DO - 10.1007/s11837-024-06922-7
M3 - 文章
AN - SCOPUS:85206352487
SN - 1047-4838
VL - 77
SP - 106
EP - 124
JO - JOM
JF - JOM
IS - 1
M1 - 224106
ER -