Optimized artificial neural networks to predict the impact response of functionally graded composite materials

Kada Zemani, Samir Khatir, Hana Slamani, Ecole Supérieure des Techniques de l, Dar El Baida, Center for Engineering Application and Technology Solutions, Ho Chi Minh City Open Univ, Ho Chi Minh, Research Center in Industrial Technologies

Journal of Composite Materials

Published online on March 03, 2026

Abstract

Journal of Composite Materials, Ahead of Print.
Accurately predicting the Depth of Penetration (DOP) is essential for understanding the impact behavior of Functionally Graded Composite Materials (FGCMs) under high-velocity conditions. However, the nonlinear dynamic behavior governing FGCMs, coupled ...