Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading

TL;DR

This paper introduces a novel graph neural network approach with a specialized embedding technique to efficiently predict stress distributions in 3D stiffened panels, offering a promising reduced-order modeling alternative to finite element analysis.

Contribution

The study develops a new graph embedding method for GNNs to represent complex 3D geometries, enabling accurate stress prediction in stiffened panels with varying designs.

Findings

GNN with the proposed embedding accurately predicts stress distributions.

The approach outperforms traditional finite element models in computational efficiency.

Structural geometry significantly influences prediction accuracy.

Abstract

Machine learning (ML) and deep learning (DL) techniques have gained significant attention as reduced order models (ROMs) to computationally expensive structural analysis methods, such as finite element analysis (FEA). Graph neural network (GNN) is a particular type of neural network which processes data that can be represented as graphs. This allows for efficient representation of complex geometries that can change during conceptual design of a structure or a product. In this study, we propose a novel graph embedding technique for efficient representation of 3D stiffened panels by considering separate plate domains as vertices. This approach is considered using Graph Sampling and Aggregation (GraphSAGE) to predict stress distributions in stiffened panels with varying geometries. A comparison between a finite-element-vertex graph representation is conducted to demonstrate the effectiveness of the proposed approach. A comprehensive parametric study is performed to examine the effect of structural geometry on the prediction performance. Our results demonstrate the immense potential of graph neural networks with the proposed graph embedding method as robust reduced-order models for 3D structures.