Enhancing composite laminate structures with tailored neural networks

Abstract

This paper presents a groundbreaking strategy for optimizing composite laminate structures by integrating finite element modeling with a specialized multi-layer neural network. The neural network is trained on precise ground truth data obtained from rigorous finite element simulations, allowing it to discern intricate correlations among layer orientations, boundary conditions, and optimized designs. Tailored to the nuances of laminar composite optimization, the developed neural network emerges as a potent predictive tool, providing deep insights into the intricate interdependencies of design parameters. The study's findings hold immense promise for advancing materials design and structural engineering, highlighting the transformative potential of combining computational intelligence with traditional modeling approaches.

Graphical abstract