Ontology-Based Documentation of Quality Assurance Measures Using the Example of a Visual Inspection
| Autor: | Tatyana Sheveleva, Kevin Herrmann, Max Leo Wawer, Christoph Kahra, Florian Nürnberger, Oliver Koepler, Iryna Mozgova, Roland Lachmayer, Sören Auer |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Lecture Notes in Networks and Systems ISBN: 9783031162800 Advances in System-Integrated Intelligence : Proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt 2022), September 7-9, 2022, Genova, Italy Lecture Notes in Networks and Systems;546 |
| DOI: | 10.15488/13181 |
| Popis: | The development of a novel manufacturing process chain is a com- plex scientific challenge and requires interdisciplinary and inter-institutional collaboration. Data need to be exchanged continuously between involved re- searchers in order to coordinate between individual process steps and to identify cause-effect relationships within the process. This publication describes an ap- proach to provide seamless digital access to quality-related data and to further structure, semantically annotate and link process- and quality-relevant data. It uses a domain-specific ontology called Visual Inspection Ontology embedded in a Knowledge Management System to support the documentation of a quality- determining process. The ontology is applied to a use case from the develop- ment of a novel process chain to manufacture multi-material shafts within the Collaborative Research Centre (CRC) 1153. A workflow to establish quality control measures regarding a novel process chain for multi-material high- performance components under development based on the proposed ontology is presented. |
| Databáze: | OpenAIRE |
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