Fiber Reinforced

Fiber-reinforced materials research centers on optimizing the design, manufacturing, and performance of composites by integrating fibers (e.g., carbon, glass) within a matrix material. Current research heavily utilizes machine learning, particularly deep neural networks (including recurrent and feed-forward architectures) and metaheuristic algorithms, to model complex material behavior, predict mechanical properties, and guide efficient design and manufacturing processes like chemical vapor infiltration and additive manufacturing. This work is crucial for advancing lightweight, high-strength materials in diverse applications, from aerospace and automotive to construction, by enabling faster, more accurate simulations and improved quality control.