Classification of Computer Aided Engineering (CAE) Parts Using Graph Convolutional Networks

TL;DR

This paper presents a Graph Convolutional Network-based classifier for CAE parts, achieving high accuracy in identifying parts across varied mesh representations, aiding engineers in efficiently filtering CAE components.

Contribution

The study introduces a GCN model for classifying CAE parts represented as 3D meshes, outperforming traditional neural networks in accuracy and robustness to mesh variations.

Findings

Achieved 88.5% classification accuracy on a dataset of 866 parts.

GCN outperformed fully connected and PointNet models in robustness.

Model effectively identified parts despite significant mesh and positional variations.

Abstract

CAE engineers work with hundreds of parts spread across multiple body models. A Graph Convolutional Network (GCN) was used to develop a CAE parts classifier. As many as 866 distinct parts from a representative body model were used as training data. The parts were represented as a three-dimensional (3-D) Finite Element Analysis (FEA) mesh with values of each node in the x, y, z coordinate system. The GCN based classifier was compared to fully connected neural network and PointNet based models. Performance of the trained models was evaluated with a test set that included parts from the training data, but with additional holes, rotation, translation, mesh refinement/coarsening, variation of mesh schema, mirroring along x and y axes, variation of topographical features, and change in mesh node ordering. The trained GCN model was able to achieve 88.5% classification accuracy on the test set i.e., it was able to find the correct matching part from the dataset of 866 parts despite significant variation from the baseline part. A CAE parts classifier demonstrated in this study could be very useful for engineers to filter through CAE parts spread across several body models to find parts that meet their requirements.