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AI-Based knowledge extraction for automatic design proposals using design-related patterns

Krahe, Carmen ORCID iD icon; Kalaidov, Maksym; Doellken, Markus ORCID iD icon; Gwosch, Thomas; Kuhnle, Andreas; Lanza, Gisela; Matthiesen, Sven


Engineering competence and the digitization of all processes along the product development process are highly decisive for today’s success of industrial companies. The design process is very individual and strongly based on design engineers’ experience. Part of this knowledge and the result of the design approach are fixated in the existing variations of the product generations, but are difficult to extract and to formalize. Conclusions about design-related patterns between products of different generations or variants can be drawn from the model tree representing the design engineer’s thinking process for each individual CAD model. However, the model tree has hardly been used so far. The aim of this paper is to examine whether there exist any common design patterns between CAD models of certain component classes by the exemplary use case in the area of mechanical engineering. To identify patterns and to extract knowledge out of complex data sets, Machine Learning (ML), especially Deep Learning, has proven an immense capability. Finally, based on the learned patterns, meaningful next design steps are to be proposed in the form of an assistance system. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000134123
Veröffentlicht am 17.06.2021
DOI: 10.1016/j.procir.2021.05.093
Zitationen: 6
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktentwicklung (IPEK)
Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000134123
Erschienen in Procedia CIRP
Verlag Elsevier
Band 100
Seiten 397–402
Bemerkung zur Veröffentlichung 31st CIRP Design Conference 2021 (CIRP Design 2021), 19 - 21 May 2021, Online
Vorab online veröffentlicht am 02.06.2021
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