Accelerating Modelling of Injection Molding with Geometric Deep Learning

Student: Misiutin Roman

Educational Programme: Data Science (Master)

Year of Graduation: 2020

Injection molding is one of the most popular manufacturing methods for complex plastic objects. Faster numerical simulation of technological process would allow for faster and cheaper design cycle of new products. In this work we consider deep learning models for fast approximate simulation of deflection for car dashboards and bumpers. We propose modification to existing mesh convolution to improve the fidelity of prediction on injection molding data and compare results with available baselines.

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