Artificial Intelligence and Machine Learning Approaches for Composite Materials: Recent Developments and Applications

ABSTRACT This review presents a thorough examination of the current trends in Artificial Intelligence (AI) and Machine Learning (ML) as applied to the field of composite materials science and engineering. It delves into the utilization of diverse AI and ML methodologies, such as Support Vector Machines, Artificial Neural Networks, Convolutional Networks, Recurrent Networks, genetic algorithms, and generative adversarial networks, for tasks including the prediction of mechanical properties, microstructure analysis, defect detection, and design process optimization. The study underscores the indispensable role of combining experimental and computational approaches in advancing this discipline. Despite the promising capabilities of these technologies, their broader implementation is hampered by challenges like the scarcity of high‐quality data, difficulties in model interpretability, and substantial computational demands. Additionally, the study stresses the necessity of standardizing methodologies and encouraging data sharing to propel advancements in the field. By applying statistical and probabilistic techniques to capitalize on existing knowledge, these technologies illuminate the fundamental principles governing material behavior, providing novel insights that facilitate the development of more efficient, customized, and sustainable solutions in composite material engineering.