Residual Strength Prediction of Composite Laminates Subjected to Compression After Impact (CAI) Using Intelligent Reconstruction of Impact-Induced Damage

Abstract

This paper presents an equivalent damage model for efficiently predicting compression-after-impact (CAI) behaviors of laminated composites, based on the intelligent numerical reconstruction of impact-induced damage. Using the k-means +  + clustering algorithm, the 3D spatial distribution of delamination is quantitatively identified from C-scanning time-of-flight (TOF) images and then discretized into a numerical mesh along with the soft inclusion. With the incorporation of interlaminar and intralaminar damage models, the CAI residual strength, failure modes, and damage scenarios of the composite laminate after low-velocity impact are predicted, showing good agreement with the experimental results at various impact energies. The proposed model enables fast evaluation of CAI strength within 1.5 h, without requiring impact energy information and maintaining accuracy, which is beneficial for application in damage tolerance design and optimization of engineering laminated structures.