Data-driven Topology Optimization (DDTO) for Three-dimensional Continuum Structures

TL;DR

This paper introduces a data-driven topology optimization framework for 3D continuum structures under finite deformation, utilizing neural networks and experimental data to enable optimal design without explicit constitutive models.

Contribution

It presents a novel mechanistic-based DDTO approach that integrates neural networks with topology optimization for complex materials lacking explicit constitutive relations.

Findings

Effective optimization of 3D structures using limited experimental data.

Demonstrated success with numerical examples.

Enables design of novel materials without explicit constitutive models.

Abstract

Developing appropriate analytic-function-based constitutive models for new materials with nonlinear mechanical behavior is demanding. For such kinds of materials, it is more challenging to realize the integrated design from the collection of the material experiment under the classical topology optimization framework based on constitutive models. The present work proposes a mechanistic-based data-driven topology optimization (DDTO) framework for three-dimensional continuum structures under finite deformation. In the DDTO framework, with the help of neural networks and explicit topology optimization method, the optimal design of the three-dimensional continuum structures under finite deformation is implemented only using the uniaxial and equi-biaxial experimental data. Numerical examples illustrate the effectiveness of the data-driven topology optimization approach, which paves the way for the optimal design of continuum structures composed of novel materials without available constitutive relations.