Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

被引：63
作者：
Kibrete, Fasikaw [1 ,2 ,3 ]
Trzepiecinski, Tomasz [4 ]
Gebremedhen, Hailu Shimels [2 ,3 ]
Woldemichael, Dereje Engida [2 ,3 ]
机构：
[1] Univ Gondar, Dept Mech Engn, POB 196, Gondar, Ethiopia
[2] Addis Ababa Sci & Technol Univ, Artificial Intelligence & Robot Ctr Excellence, POB 16417, Addis Ababa, Ethiopia
[3] Addis Ababa Sci & Technol Univ, Coll Engn, Dept Mech Engn, POB 16417, Addis Ababa, Ethiopia
[4] Rzeszow Univ Technol, Dept Mfg Proc & Prod Engn, Al Powst Warszawy 8, PL-35959 Rzeszow, Poland
来源：
JOURNAL OF COMPOSITES SCIENCE | 2023年 / 7卷 / 09期
关键词：
artificial intelligence; composite materials; deep learning; machine learning; mechanical properties; CONVOLUTIONAL NEURAL-NETWORKS; TRANSFER LEARNING APPROACH; HETEROGENEOUS MATERIALS; MICROSTRUCTURE GENERATION; STRENGTH PREDICTION; TENSILE-STRENGTH; INVERSE PROBLEM; FUZZY DATA; DESIGN; FRAMEWORK;
D O I：
10.3390/jcs7090364
中图分类号：
TB33 [复合材料];
学科分类号：
摘要：
The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.
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页数：36
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