A review of artificial intelligence (AI)-based applications to nanocomposites

被引：0

作者：

Logakannan, Krishna Prasath ^{[1
]}

Guven, Ibrahim ^{[2
]}

Odegard, Gregory ^{[3
]}

Wang, Kan ^{[4
]}

Zhang, Chuck ^{[4
,5
]}

Liang, Zhiyong ^{[6
]}

Spear, Ashley ^{[1
]}

机构：

[1] Univ Utah, Dept Mech Engn, Salt Lake City, UT 84112 USA

[2] Virginia Commonwealth Univ, Dept Mech & Nucl Engn, Richmond, VA USA

[3] Michigan Technol Univ, Dept Mech & Aerosp Engn, Houghton, MI USA

[4] Georgia Inst Technol, Georgia Tech Mfg Inst, Atlanta, GA USA

[5] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA USA

[6] FAMU, High Performance Mat Inst, FSU Coll Engn, Dept Ind & Mfg Engn, Tallahassee, FL USA

来源：

COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING | 2025年 / 197卷

关键词：

Nanocomposites; Artificial intelligence; Machine learning; CARBON NANOTUBES; DESIGN; COMPOSITES; FRACTURE; PREDICTIONS; OPPORTUNITIES; DISCOVERY;

D O I：

10.1016/j.compositesa.2025.109027

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Recent progress in artificial intelligence (AI) techniques has attracted interest from researchers in various engineering fields, including materials science and engineering. AI has enabled materials researchers to explore vast materials design spaces, which were previously inaccessible due to the inherent limitations of conventional techniques (viz., experiments and physics-based computational models). This is particularly true for the design of nanocomposites because of the many degrees of freedom associated with both material composition and manufacturing parameters. The primary motivation of this review is to report how AI techniques are being used in nanocomposite materials design, with special attention given to the manufacturing and property prediction of nanocomposites using AI techniques.

引用

页数：20

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