Fast approximation of fiber reinforced injection molding

Discontinuous fiber reinforced composites are used in many application areas ranging from automotive to healthcare. Such parts are often manufactured in and injection molding process, as it is an economical process for high volume markets. The simulation of the injection molding process is well established and specific commercial tools have been developed for this task. However, the transient solution of the underlying non-linear multi-phase flow is computationally expensive and computation may take multiple hours for complex geometries. This computational time is prohibitively large for computational optimization of the product design or the process parameters. Hence, we propose a two-step process to accelerate the mold filling prediction: i) Solve a modified Eikonal equation to compute distance maps to the injection gate and nearest walls. This is computationally cheap, as it is only a stationary equation to solve. ii) Train feed forward neural networks to obtain a data-driven relation between the encoded distance maps and mold filling features, such as fill time and fiber orientation. We sample a set of geometries, automatically generate CAD models, and simulate these in a commercial injection molding solver to build a training data set. Subsequently, we apply different feed forward neural network architectures and evaluate their performance