3D Topology Optimization using Convolutional Neural Networks

TL;DR

This paper introduces a deep learning method using 3D CNNs to accelerate topology optimization, achieving significant speedups and high accuracy in predicting optimal structures compared to traditional physics-based methods.

Contribution

It presents a novel 3D CNN architecture for direct prediction of optimized structures, reducing computation time by 40% and maintaining 96% structural accuracy.

Findings

Achieved 40% reduction in computation time.

Attained 96% accuracy in predicted structures.

Compared multiple training strategies for optimal deployment.

Abstract

Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis. As a complementary alternative to the traditional physics-based topology optimization, we explore a data-driven approach that can quickly generate accurate solutions. To this end, we propose a deep learning approach based on a 3D encoder-decoder Convolutional Neural Network architecture for accelerating 3D topology optimization and to determine the optimal computational strategy for its deployment. Analysis of iteration-wise progress of the Solid Isotropic Material with Penalization process is used as a guideline to study how the earlier steps of the conventional topology optimization can be used as input for our approach to predict the final optimized output structure directly from this input. We conduct a comparative study between multiple strategies for training the neural network and assess the effect of using various input combinations for the CNN to finalize the strategy with the highest accuracy in predictions for practical deployment. For the best performing network, we achieved about 40% reduction in overall computation time while also attaining structural accuracies in the order of 96%.