Classification of Computer Aided Engineering (CAE) Parts Using Graph Convolutional Networks
This provides a tool for CAE engineers to efficiently filter parts across body models, but it is incremental as it applies an existing GCN method to a new domain-specific dataset.
The study tackled the problem of classifying Computer Aided Engineering (CAE) parts by using a Graph Convolutional Network (GCN) on 3-D Finite Element Analysis meshes, achieving 88.5% accuracy on a test set with 866 parts despite variations like holes and rotations.
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.