A KPCA-based parameterization model for composite materials representation

2nd International Workshop on Materials Science and Mechanical Engineering

机械制造;材料科学

Xiao, M.Y.^1 ; Tian, S.B.^1 ; Guo, Z.W.^1 ; Xia, L.^2

Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an

710072, China^1

State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan

430074, China^2

关键词: Effective mechanical properties;    High-dimensional feature space;    Kernel principal component analyses (KPCA);    Material microstructures;    Parameterization model;    Processing parameters;    Projection coefficients;    Representative volume element (RVE);   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/504/1/012109/pdf
DOI  :  10.1088/1757-899X/504/1/012109

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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.