VoroTO: Multiscale Topology Optimization of Voronoi Structures using Surrogate Neural Networks

TL;DR

This paper introduces VoroTO, a neural network-based method for efficient multiscale topology optimization of Voronoi structures, enabling faster design of bio-inspired cellular materials with desirable properties.

Contribution

It presents a novel neural network approach to predict homogenized properties and integrate them into topology optimization for Voronoi structures, reducing computational costs.

Findings

Neural network accurately predicts homogenized properties of Voronoi cells.

The method achieves optimized structures with improved compliance and connectivity.

Numerical examples demonstrate efficiency and effectiveness of the approach.

Abstract

Cellular structures found in nature exhibit remarkable properties such as high strength, high energy absorption, excellent thermal/acoustic insulation, and fluid transfusion. Many of these structures are Voronoi-like; therefore researchers have proposed Voronoi multi-scale designs for a wide variety of engineering applications. However, designing such structures can be computationally prohibitive due to the multi-scale nature of the underlying analysis and optimization. In this work, we propose the use of a neural network (NN) to carry out efficient topology optimization (TO) of multi-scale Voronoi structures. The NN is first trained using Voronoi parameters (cell site locations, thickness, orientation, and anisotropy) to predict the homogenized constitutive properties. This network is then integrated into a conventional TO framework to minimize structural compliance subject to a volume constraint. Special considerations are given for ensuring positive definiteness of the constitutive matrix and promoting macroscale connectivity. Several numerical examples are provided to showcase the proposed method.