  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.
