Graph machine learning for assembly modeling
- Assembly modeling refers to the design engineering process of composing assemblies (e.g., machines or machine components) from a common catalog of existing parts. There is a natural correspondence of assemblies to graphs which can be exploited for services based on graph machine learning such as part recommendation, clustering/taxonomy creation, or anomaly detection. However, this domain imposes particular challenges such as the treatment of unknown or new parts, ambiguously extracted edges, incomplete information about the design sequence, interaction with design engineers as users, to name a few. Along with open research questions, we present a novel data set.
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| Author: | Carola LenzenORCiDGND, Alexander SchiendorferORCiDGND, Wolfgang ReifORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1015696 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/101569 |
| URL: | https://openreview.net/forum?id=YcnAf3cEvH3 |
| Parent Title (English): | LoG 2022: The First Learning on Graphs Conference, 9-12 December 2022, virtual event |
| Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2022 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2023/02/02 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
