A Comprehensive Analysis of Machine Learning Techniques in Advancing Composite Materials

Composite materials are heavily implemented throughout many industries because they offer immense strength in combination with the ability to be lightweight without compromising their flexibility in a many-faceted environment. However, these are nonlinear and multi-dimensional materials; that require sophisticated models to predict the optimal properties and performance. Currently, an impressive method making predictions, optimizing, detecting defects in composites is ML as compared with considerable experimental tests. This paper reviews the current state of the art of advanced ML in the field of composite materials for different aspects such as property prediction, defect identification, design optimization and additive manufacturing. A comparative assessment also shows the challenges and the methods used and final results in various types of composites. The study also recognises the ML's capability to open a new path in composites material design and test with existing scales, interpretability, and integration issues.