Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

TL;DR

This paper presents a deep learning approach using convolutional neural networks to predict stress fields in 2D cantilevered structures, demonstrating improved accuracy over baseline models and potential for accelerating structural analysis.

Contribution

The study introduces two CNN architectures, StressNet and SCSNet, for stress prediction, with StressNet showing significantly lower prediction errors, advancing the application of deep learning in structural analysis.

Findings

StressNet achieves a mean relative error of 2.04%.

Deep learning models outperform classical methods in stress prediction.

Code and dataset are publicly available for further research.

Abstract

The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNN). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multi-channel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available at https://github.com/zhenguonie/stress_net