A Data-driven Multiscale Analysis by Combination of Cluster-based Non-uniform Transformation Field Analysis and Artificial Neural Network
Year: 2025
Type: article
Abstract: Data-driven computational mechanics have been used in the field of multiscale analysis where the constitutive modeling of composites is obtained by learning the material database obtained experimentally or numerically, using artificial neural network (ANN). In this paper, we present a data-driven multiscale analysis by combining the cluster-based non-uniform transformation field analysis (CNTFA), a reduced order model for the numerical homogenization of composites with periodically arranged microstructure, with ANN. The stiffness of homogenized material is approximately calculated using the gradient of ANN. The proposed method can be effectively used in reproduction of field information (e.g. strain and stress) at the microscale as well as the analysis of structures at the macroscale. An example calculation of three-point bending beam shows that the proposed method is very effective for the multiscale analysis of nonlinear composite structures.
Source: Science Frontiers
Language: English
Topic: Composite Material Mechanics
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414585129