Institutsseminar/2021-12-17

Termin (Alle Termine)
	Datum	Freitag, 17. Dezember 2021
	Uhrzeit	11:30 – 11:50 Uhr (Dauer: 20 min)
	Ort	Raum 348 (Gebäude 50.34), https://conf.dfn.de/webapp/conference/979160755
	Webkonferenz
	Vorheriger Termin	Fr 10. Dezember 2021
	Nächster Termin	Fr 14. Januar 2022

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Vorträge

Vortragende(r)	Jonas Zoll
Titel	Injection Molding Simulation based on Graph Neural Networks
Vortragstyp	Proposal
Betreuer(in)	Moritz Renftle
Vortragssprache
Vortragsmodus
Kurzfassung	Injection molding simulations are important tools for the development of new injection molds. Existing simulations mostly are numerical solvers based on the finite element method. These solvers are reliable and precise, but very computionally expensive even on simple part geometries. In this thesis, we aim to develop a faster injection molding simulation based on Graph Neural Networks (GNNs). Our approach learns a simulation as a composition of three functions: an encoder, a processor and a decoder. The encoder takes in a graph representation of a 3D geometry of a mold part and returns a numeric embedding of each node and edge in the graph. The processor updates the embeddings of each node multiple times based on its neighbors. The decoder then decodes the final embeddings of each node into physically meaningful variables, say, the fill time of the node. The envisioned GNN architecture has two interesting properties: (i) it is applicable to any kind of material, geometry and injection process parameters, and (ii) it works without a “time integrator”, i.e., it predicts the final result without intermediate steps. We plan to evaluate our architecture by its accuracy and runtime when predicting node properties. We further plan to interpret the learned GNNs from a physical perspective.

The presentation will be held in person in Room 348, and it will be streamed via the DFN Conf Tool at the same time.