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doi:10.22028/D291-34335
Titel: | Artificial intelligence in engineering: evolution of virtual product development in the context of medical device industry |
VerfasserIn: | Schweitzer, Gregor M. Bitzer, Michael Vielhaber, Michael |
HerausgeberIn: | Lutters, Eric |
Sprache: | Englisch |
Titel: | Procedia CIRP |
Startseite: | 349 |
Endseite: | 354 |
Verlag/Plattform: | Elsevier |
Erscheinungsjahr: | 2021 |
Erscheinungsort: | Amsterdam |
Titel der Konferenz: | CIRP Design 2021 |
Konferenzort: | Enschede, The Netherlands |
Dokumenttyp: | Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag) |
Abstract: | In this paper a framework is introduced to elicit requirements of Artificial Intelligence (AI) towards the System Model in order to support engineers in Virtual Product Development (VPD). The framework supports to shape the evolution necessary in system modelling to provide the right data in the right quality for AI. Depending on the business benefit that a company wants to realize, AI can provide capabilities and solutions to be implemented in VPD. Therefore, differing requirements need to be fulfilled for each business benefit that a company pursues. This framework is applied in a case study in the Medical Device Industry, where a marked leader wants to improve their capability to create innovations by automatically increasing their market knowledge. Therefore, a Natural Language Processing system is applied to automatically enhance the company knowledge base with an external source. This is realized in an initial prototype by analyzing Tender Documents and automatically connecting the new knowledge generated to the company internal knowledge in the system model. This paper is part of research activities within the Research and Development department of a global Medical Device Company. The Objective of these research activities is to explore the use of Artificial Intelligence to analyze and support the Virtual Development Process. |
DOI der Erstveröffentlichung: | 10.1016/j.procir.2021.05.081 |
URL der Erstveröffentlichung: | https://www.sciencedirect.com/science/article/pii/S2212827121005485 |
Link zu diesem Datensatz: | hdl:20.500.11880/31499 http://dx.doi.org/10.22028/D291-34335 |
ISSN: | 2212-8271 |
Datum des Eintrags: | 9-Jul-2021 |
Bemerkung/Hinweis: | Procedia CIRP, Volume 100, 2021, Pages 349-354 |
Fakultät: | NT - Naturwissenschaftlich- Technische Fakultät |
Fachrichtung: | NT - Systems Engineering |
Professur: | NT - Prof. Dr. Michael Vielhaber |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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