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REVIEW ARTICLE

A comprehensive review of artificial intelligence applications in composite materials: Predictive, generative, and automation approaches

Hyunsoo Hong1 ,  Samuel Kim1 ,  Jeeeun Lee1 ,  Seong Su Kim1*
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1 Department of Mechanical Engineering, College of Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
IJAMD, 025210016 https://doi.org/10.36922/IJAMD025210016
Received: 19 May 2025 | Revised: 2 July 2025 | Accepted: 15 July 2025 | Published online: 4 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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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 applications of machine learning and deep learning in composite materials research, categorized into three major approaches: predictive, generative, and automation models. Predictive models enhance the accuracy of property prediction and microstructure analysis. Generative models facilitate novel material discovery and microstructure design. Automatic models improve quality control and can be used to optimize manufacturing processes through real-time data analysis. By leveraging diverse large-scale datasets, AI provides innovative solutions to the key challenges associated with composite materials and enhances research and design efficiency. This review highlights the transformative potential of AI in composite materials research, providing insights into future research directions and challenges.

Keywords
Composite
Artificial intelligence
Prediction
Generation
Automation
Manufacturing
Funding
This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (No. RS-2024-00450477). This work was supported by the National R&D Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (RS- 2023-00260461).
Conflict of interest
Seong Su Kim is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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