  In engineering design, surrogate models are widely employed to replace
computationally expensive simulations by leveraging design variables and
geometric parameters from computer-aided design (CAD) models. However, these
models often lose critical information when simplified to lower dimensions and
face challenges in parameter definition, especially with the complex 3D shapes
commonly found in industrial datasets. To address these limitations, we propose
a Bayesian graph neural network (GNN) framework for a 3D deep-learning-based
surrogate model that predicts engineering performance by directly learning
geometric features from CAD using mesh representation. Our framework determines
the optimal size of mesh elements through Bayesian optimization, resulting in a
high-accuracy surrogate model. Additionally, it effectively handles the
irregular and complex structures of 3D CADs, which differ significantly from
the regular and uniform pixel structures of 2D images typically used in deep
learning. Experimental results demonstrate that the quality of the mesh
significantly impacts the prediction accuracy of the surrogate model, with an
optimally sized mesh achieving superior performance. We compare the performance
of models based on various 3D representations such as voxel, point cloud, and
graph, and evaluate the computational costs of Monte Carlo simulation and
Bayesian optimization methods to find the optimal mesh size. We anticipate that
our proposed framework has the potential to be applied to mesh-based
simulations across various engineering fields, leveraging physics-based
information commonly used in computer-aided engineering.
