Artificial Intelligence in Materials Modeling and Design

被引:35
|
作者
Huang, J. S. [1 ]
Liew, J. X. [2 ]
Ademiloye, A. S. [3 ]
Liew, K. M. [1 ]
机构
[1] City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Hong Kong, Peoples R China
[2] Univ British Columbia, Fac Appl Sci, Kelowna, BC V1V 1V7, Canada
[3] Swansea Univ, Coll Engn, Zienkiewicz Ctr Computat Engn, Bay Campus, Swansea SA1 8EN, W Glam, Wales
基金
中国国家自然科学基金;
关键词
PREDICTING MECHANICAL-PROPERTIES; RESPONSE-SURFACE METHODOLOGY; NETWORK-BASED PREDICTION; METAL-MATRIX COMPOSITES; NEURAL-NETWORK; TRIBOLOGICAL BEHAVIOR; COMPRESSIVE STRENGTH; BIG DATA; SHEAR-STRENGTH; SIMULATION;
D O I
10.1007/s11831-020-09506-1
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented.
引用
收藏
页码:3399 / 3413
页数:15
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