Surrogate PHFGMC micromechanical models for multiscale analysis: AI-enhanced low-velocity impact analysis of hat-stiffened composite panels

Abstract

The Parametric High-Fidelity Generalized Method of Cells (PHFGMC) has been established as an advanced micromechanical approach well-suited for analyzing the material nonlinearity and failure behavior of diverse periodic composite materials. In order to overcome the prohibitive computational cost of integrating micromechanical models into multiscale structural analyses as constitutive models, a proxy-surrogate modeling approach has been proposed by implementing a reduction modeling approach with deep Artificial Neural Networks (DNNs or ANNs). The PHFGMC-ANN approach has been employed to investigate the low velocity impact (LVI) analyses of hat-stiffened laminated composite panels under impact loading at various locations and energies with two different support conditions. Subsequent analysis of stiffened panels under compression loading has been conducted to understand the failure behavior of impacted panels. Further investigation was conducted into the separation between the skin and the stiffener, focusing on a single hat-stiffened coupon subjected to LVI. The analysis results have been compared against the experimental tests, and the comparison of interlaminar delamination has been used to demonstrate the efficacy of the new framework in integrating refined nonlinear micromechanical models within a multiscale analysis.