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

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

Acknowledgments

None.

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.

Author contributions

Conceptualization: Hyunsoo Hong, Samuel Kim

Visualization: Samuel Kim

Writing - original draft: Hyunsoo Hong

Writing - review & editing: All authors

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

No datasets were generated or analyzed during the current study.

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