|Fr 18. August 2023
|Graph Neural Networks (GNNs) have demonstrated great potential for simulating physical systems that can be represented as graphs. However, training GNNs presents unique challenges due to the complex nature of graph data. The focus of this thesis is to examine their learning abilities by developing a GNN-based surrogate model for the injection molding process from materials science. While numerical simulations can accurately model the mold filling with molten plastic, they are computationally expensive and require significant trial-and-error for parameter optimization.
We propose a GNN-based model that can predict the fill times and physical properties of the mold filling process. We model the mold geometry as a static graph and encode the process information into node, edge, and global features. We employ a self-attention mechanism to enhance the learning of the direction and magnitude of the fluid flow. To further enforce the physical constraints and behaviors of the process, we leverage domain knowledge to construct features and loss functions. We train our model on simulation data, using a multi-step loss to capture the temporal dependencies and enable it to iteratively predict the filling for unseen molds. Thereby, we compare our models with different distance-based heuristics and conventional machine learning models as baselines in terms of predictive performance, computational efficiency, and generalization ability. We evaluate our architectural and training choices, and discuss both the potential applications and challenges of using GNNs for surrogate modeling of injection molding.