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Deep Learning for Automated Product Design

Krahe, Carmen; Bräunche, Antonio; Jacob, Alexander; Stricker, Nicole; Lanza, Gisela

Product development is a highly complex process that has to be individually adapted depending on the companies involved, the product to be developed and the related designers. Within this process, the approach and the know-how of the designer are very individual and can often only be described with high effort in a rule-based manner. Nevertheless, numerous routine tasks can be identified that offer enormous automation potential. Machine Learning, especially Deep Learning, has proven an immense capability to identify patterns and extract knowledge out of complex data sets. Autoencoder networks are suitable for the conversion of different 3D input data, e.g. Point Clouds, into compact latent representations and vice versa. Point Clouds are a universal representation of 3D objects and can be derived from various 3D data formats. The goal of the approach presented is to use Deep Learning algorithms to identify design patterns specific to a product family out of their underlying latent representation and use the extracted knowledge to automatically generate new latent object representations fulfilling distinct product feature specifications. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000127884
Veröffentlicht am 19.12.2020
DOI: 10.1016/j.procir.2020.01.135
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Young Investigator Network (YIN)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000127884
Erschienen in Procedia CIRP
Verlag Elsevier
Band 91
Seiten 3–8
Bemerkung zur Veröffentlichung 30th CIRP Design on Design, CIRP Design 2020; Pretoria; South Africa; 5 May 2020 through 8 May 2020
Schlagwörter Artificial Intelligence, Machine Learning, Computer Aided Design, Automation
Nachgewiesen in Scopus
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