Tensile performance prediction of CFRPs with voids using multiscale analysis and neural networks

Abstract

The overall work provides a novel multiscale modeling strategy applicable to the mechanical response prediction of arbitrarily laid porous laminates under uniaxial tension by establishing representative defective volumes (RDVs) for the inter-ply and intra-ply regions. A representative defective volume modeling method based on void characterization results using X-ray computed tomography for the interlaminar region was proposed. Elastic properties, tensile strength, and fracture energies of porous and poreless microscale models were obtained and used in the macroscale model. Defective properties were assigned to partial elements in the macroscale model according to the preset distribution mode. The Extended Finite Element Method was taken to simulate the failure process. Tensile properties of porous laminates with stacking sequences of [90]8, [90/0/90/0]2S, and [45/90/-45/0]2S were experimentally tested and numerically simulated. The predicted tensile properties and crack propagation behavior coincide well with the test results. Besides, the effects of micro-voids and macroscopic void distribution were discussed in detail. Further, a back-propagation neural network was used to achieve a fast prediction of the tensile properties and save computational costs. The computation time (<1 s) greatly improved compared with multiscale modeling (>38 h). This work provides an efficient tool for composite performance prediction, design, and optimization.