Low-velocity impact damage significantly compromises the structural integrity and residual strength of carbon fiber-reinforced polymer (CFRP) composites. This study proposes a hybrid approach integrating progressive damage finite element modeling with convolutional neural networks (CNNs) to accurately predict compressive strength after impact (CAI). A 3D continuum damage model was developed to characterize interlaminar damage, employing a bilinear traction-separation law combined with the Benzeggagh-Kenane (B-K) criterion for delamination simulation. Finite element results under varying impact energies demonstrated strong agreement with experimental data in terms of force-time and force-displacement responses. A dataset pairing delamination damage profiles with corresponding CAI values was constructed from simulations across different impact scenarios. A deep CNN architecture achieved an Root Mean Square Error (RMSE) of 2.5197 MPa in mapping damage images to residual strength, eliminating the need for manual feature extraction or material parameter dependency. This image-driven method enables high-fidelity strength prediction and shows promising potential for intelligent health monitoring of composite structures.
Shangbin Su (Mon,) studied this question.