Injection molding is a widely used process for manufacturing a large range of polymer parts, including fiber-reinforced polymer composites. The complex nature of this process, combined with the non-Newtonian behavior of molten polymers, implies significant challenges in terms of control, prediction, and optimization of the processing parameters that influence manufacturing quality. While various computational and numerical models have been proposed to simulate the injection molding process and ensure the desired part quality (see, for instance, industrial software such as Moldex3D or Moldflow, they bring their own challenges, including high computational costs, the need for solver tuning, frequent parametric studies, and the economic burden associated with licensing.

In this study, machine learning surrogates are explored as an alternative to conventional modeling software. They are based on Graph Neural Networks (GNNs) that have recently demonstrated remarkable potential for simulating physical systems. In a graph, physical systems are encoded as nodes end edges, capturing their organization. Then, state-of-the-art techniques such as the message passing framework are tested for their potential to learn physics. In the long term, this project aims to establish a novel graph-based framework capable of efficiently simulating complex physics across various material models and processing conditions.

A numerical model is developed for 2D mold filling and quantitively validated against a benchmark. The prediction results demonstrate that the learned simulator is able to capture the physics of melt front behavior. Further, the prediction runtime is significantly faster than the ground truth, completing in under 1 second compared to \textasciitilde hours, representing a substantial improvement in efficiency. However, the accuracy of the predictions can vary, with errors accumulating especially during testing. While the scalar loss metric provides an estimate of the model's performance, it lacks expressiveness and can be unreliable on its own.