Handling anomalies in CAD assemblies: detecting anomalous and suggesting alternative parts
- In the design engineering process of assembly modeling, existing computer-aided design (CAD) models of parts are combined to build new products, termed assemblies. Due to the enormous variety of parts available, designs of inexperienced designers can easily show unusual part combinations stemming from unfamiliarity or a lack of suitable alternatives. This paper addresses the challenge of handling anomalies in CAD assemblies with a twofold goal: first, we aim to identify such anomalies, and second, to provide suggestions for alternative parts to correct these anomalies. We employ a graph-based representation of CAD assemblies and utilize graph neural networks (GNNs) to develop models for the two respective tasks modeled as node classification problems. The models are evaluated both separately and in an end-to-end (E2E) fashion, i.e., considering the task of handling anomalies as a whole. Our experiments demonstrate their effectiveness in improving the quality of CAD assemblies withIn the design engineering process of assembly modeling, existing computer-aided design (CAD) models of parts are combined to build new products, termed assemblies. Due to the enormous variety of parts available, designs of inexperienced designers can easily show unusual part combinations stemming from unfamiliarity or a lack of suitable alternatives. This paper addresses the challenge of handling anomalies in CAD assemblies with a twofold goal: first, we aim to identify such anomalies, and second, to provide suggestions for alternative parts to correct these anomalies. We employ a graph-based representation of CAD assemblies and utilize graph neural networks (GNNs) to develop models for the two respective tasks modeled as node classification problems. The models are evaluated both separately and in an end-to-end (E2E) fashion, i.e., considering the task of handling anomalies as a whole. Our experiments demonstrate their effectiveness in improving the quality of CAD assemblies with minimal human intervention.…
| Author: | Carola LenzenORCiDGND, Vinzenz Löffel, Wolfgang ReifORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1185484 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/118548 |
| ISBN: | 978-3-031-94939-5OPAC |
| Parent Title (English): | Computational Science and Computational Intelligence - 11th International Conference, CSCI 2024, Las Vegas, NV, USA, December 11–13, 2024, proceedings, part III |
| Publisher: | Springer |
| Place of publication: | Cham |
| Editor: | Hamid R. Arabnia, Leonidas Deligiannidis, Farzan Shenavarmasouleh, Soheyla Amirian, Farid Ghareh Mohammadi |
| Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2025/01/27 |
| Year of first Publication: | 2025 |
| Embargo Date: | 2026/08/15 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2025/01/28 |
| Tag: | Anomaly Detection; Assembly Modeling; Graph Machine Learning; Recommendation |
| First Page: | 257 |
| Last Page: | 270 |
| Series: | Communications in Computer and Information Science ; 2503 |
| DOI: | https://doi.org/10.1007/978-3-031-94940-1_21 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
