Page 7 - IJAMD-2-3

International Journal of AI for
Materials and Design

REVIEW ARTICLE
A comprehensive review of artificial intelligence

applications in composite materials: Predictive,
generative, and automation approaches

Hyunsoo Hong † , Samuel Kim † , Jeeeun Lee , and Seong Su Kim*

Department of Mechanical Engineering, College of Engineering, Korea Advanced Institute of
Science and Technology, Daejeon, Republic of Korea

Abstract

The rapid advancement of artificial intelligence (AI) has led to its widespread
adoption across various engineering fields, including composite materials
research. Composite materials, known for their superior mechanical properties
and lightweight characteristics, play a crucial role in industries such as aerospace,
automotive, and robotics. However, their inherent complexity–such as anisotropic
behavior, nonlinear characteristics, and intricate microstructures–poses significant
challenges for traditional design and analysis methods. To address these challenges,
AI-driven approaches have emerged as powerful tools, offering solutions in
prediction, generation, and automation. This review systematically explores
† These authors contributed equally applications of machine learning and deep learning in composite materials research,
to this work.
categorized into three major approaches: predictive, generative, and automation
*Corresponding author: models. Predictive models enhance the accuracy of property prediction and
Seong Su Kim microstructure analysis. Generative models facilitate novel material discovery and
(seongsukim@kaist.ac.kr)
microstructure design. Automatic models improve quality control and can be used
Citation: Hong H, Kim S, Lee J, to optimize manufacturing processes through real-time data analysis. By leveraging
Kim SS. A comprehensive review
of artificial intelligence applications diverse large-scale datasets, AI provides innovative solutions to the key challenges
in composite materials: Predictive, associated with composite materials and enhances research and design efficiency.
generative, and automation This review highlights the transformative potential of AI in composite materials
approaches. Int J AI Mater Design.
2025;2(3):1-30. research, providing insights into future research directions and challenges.
doi: 10.36922/IJAMD025210016
Received: May 19, 2025 Keywords: Composite; Artificial intelligence; Prediction; Generation; Automation;
Revised: July 02, 2025 Manufacturing
Accepted: July 15, 2025
Published online: August 4, 2025
1. Introduction
Copyright: © 2025 Author(s).
This is an Open-Access article Since the emergence of AlphaGo, artificial intelligence (AI) technology has rapidly
distributed under the terms of the
Creative Commons Attribution advanced over the past decade, alongside the development of graphics processing units
License, permitting distribution, (GPUs) for parallel computing. AI technology has recently reached a level where it is
1,2
and reproduction in any medium, easily accessible in daily life, as demonstrated by the widespread adoption of generative
provided the original work is
3,4
properly cited. conversational AI models, such as ChatGPT. Furthermore, AI is now a core technology
driving innovation across various engineering industries, including autonomous driving,
Publisher’s Note: AccScience
Publishing remains neutral with biotechnology, robotics, aerospace, semiconductors, and composite materials. 5-12
regard to jurisdictional claims in
published maps and institutional Composite materials are engineered by combining multiple constituent materials
affiliations. to achieve properties superior to those of conventional materials. The exceptional
Volume 2 Issue 3 (2025) 1 doi: 10.36922/IJAMD025210016