A KPCA-based parameterization model for composite materials representation

Abstract

It is supposed that there exist means to generate material microstructure representations by varying processing parameters numerically [1] or experimentally [2], the goal of this work is to establish a parameterized geometrical description model from learning the set of given snapshot instances. In this work, we propose a parameterization model for the representation of the Representative Volume Elements (RVE) of composite materials based on Kernel Principal Component Analysis (KPCA) method. The set of synthetic RVE snapshots governed by a series of control parameters is firstly mapped into a high-dimensional feature space. Then, Principal Component Analysis (PCA) is performed, together with the establishment of approximated response surfaces of the retained PCA projection coefficients. We showcase the performance of KPCA-based surrogate by applying it for an optimal design of the effective mechanical properties of a two-phase composite material.