Advancing Polymer Composites Through Computational Learning and Artificial Intelligence Integration

The revolutionary effects of computational learning and artificial intelligence (AI) on materials science and engineering—particularly in the field of high-performance fiber -reinforced polymer (FRP) composites—are examined in this article. Efficiency, accuracy, and innovation have significantly increased as a result of the integration of AI and machine learning (ML) approaches across the product lifecycle, from material selection and development to manufacturing process optimization and property prediction. AI-powered deep generative models in material design speed up the development of novel composite formulations, while automated fiber placement and Physics-Informed Machine Learning (PIML) improve manufacturing processes to guarantee consistent quality and cut costs. The large-scale dataset-based predictive models have significantly enhanced mechanical property prediction, simplified material selection and cut down on experimental testing expenses and time. Sustainability, efficiency, and performance in material research and production will be fueled by the ongoing advancements in AI and computational learning, which will further transform material science by allowing the design and production of superior composites suited for contemporary industrial applications.