Aus SDQ-Institutsseminar
Termin (Alle Termine)
Datum Freitag, 31. März 2023
Uhrzeit 14:00 – 14:40 Uhr (Dauer: 40 min)
Ort Raum 348 (Gebäude 50.34)
Vorheriger Termin Fr 31. März 2023
Nächster Termin Fr 14. April 2023

Termin in Kalender importieren: iCal (Download)


Vortragende(r) Aaron Gätje
Titel Efficient Training of Graph Neural Networks for Dynamic Phenomena (Proposal)
Vortragstyp Proposal
Betreuer(in) Daniel Ebi
Vortragsmodus in Präsenz
Kurzfassung Graph Neural Networks (GNNs) have shown great potential for use cases that can be described as graphs. However, training GNNs presents unique challenges due to the characteristics 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 model the mold filling accurately, they are computationally expensive and require significant trial-and-error for parameter optimization. We propose representing the mold geometry as a static graph and constructing additional node and edge features from domain knowledge. We plan to enhance our model with a self-attention mechanism, allowing dynamic weighting of a node's neighbors based on their current states. Further improvements may come from customizing the model’s message passing function and exploring node sampling methods to reduce computational complexity. We compare our approach to conventional machine learning models w.r.t. predictive performance, generalizability to arbitrary mold geometries and computational efficiency.

This thesis is a follow-up work to a bachelor thesis written at the chair in 2022.

Vortragende(r) Aleksandr Eismont
Titel Surrogate Model Based Process Parameters Optimization of Textile Forming
Vortragstyp Proposal
Betreuer(in) Bela Böhnke
Vortragsmodus in Präsenz
Kurzfassung Manufacturing optimization is crucial for organizations to remain competitive in the market. However, complex processes, such as textile forming, can be challenging to optimize, requiring significant resources. Surrogate-based optimization is an efficient method that uses simplified models to guide the search for optimal parameter combinations of manufacturing processes. Moreover, incorporating uncertainty estimates into the model can further speed up the optimization process, which can be achieved by using Bayesian deep neural networks. Additionally, convolutional neural networks can take advantage of spatial information in the images that are part of the textile forming parameters. In this work, a Bayesian deep convolutional surrogate model is proposed that uses all available process parameters to predict the shear angle of a textile element. By incorporating background information into the surrogate model, it is expected to predict detailed process results, leading to greater efficiency and increased product quality.
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