Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteileigenschaften

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Sauer C, Küstner C, Schleich B, Wartzack S
Editor(s): Krause, D.; Paetzold, K.; Wartzack, S.
Publisher: TuTech Verlag
Publishing place: Hamburg
Publication year: 2017
Conference Proceedings Title: Design for X. Beiträge zum 28. DfX-Symposium
Pages range: 49-60
ISBN: 978-3-946094-20-3
Language: German


Abstract


Within the Transregional Collaborative Research Centre 73 (SFB/TR 73) a self-learning engineering workbench (SLASSY) is being developed. SLASSY assists product developers in designing sheet-bulk metal formed (SBMF) parts by computing product properties based on given product and process characteristics. SLASSY enables product developers to evaluate the manufacturability of their current part design. For this, SLASSY uses data from manufacturing experts to create metamodels. Currently, it can handle product properties which apply for a whole part variant (on whole part level), for instance the minimum form filling degree. The further development of the SBMF manufacturing technology requires the consideration of the product properties in higher detail (on local level). This requires a higher data density, that is, data for each part variant and product property need to be acquired on every point of interest. Due to the increased amount of data, the currently used data mining algorithms in SLASSY for creating the metamodels cannot be reused. To face this challenge, deep learning algorithms are utilized which are good in processing big data. In this contribution, two approaches for the use of deep learning to compute product properties on local level are presented.



FAU Authors / FAU Editors

Küstner, Christof
Lehrstuhl für Konstruktionstechnik
Sauer, Christopher
Sonderforschungsbereich/Transregio 73 Umformtechnische Herstellung von komplexen Funktionsbauteilen mit Nebenformelementen aus Feinblechen - Blechmassivumformung
Schleich, Benjamin Dr.-Ing.
Lehrstuhl für Konstruktionstechnik
Wartzack, Sandro Prof. Dr.-Ing.
Lehrstuhl für Konstruktionstechnik


How to cite

APA:
Sauer, C., Küstner, C., Schleich, B., & Wartzack, S. (2017). Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteileigenschaften. In Krause, D.; Paetzold, K.; Wartzack, S. (Hrg.), Design for X. Beiträge zum 28. DfX-Symposium (pp. 49-60). Bamberg, DE: Hamburg: TuTech Verlag.

MLA:
Sauer, Christopher, et al. "Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteileigenschaften." Tagungsband 28. DfX-Symposium, Bamberg Hrg. Krause, D.; Paetzold, K.; Wartzack, S., Hamburg: TuTech Verlag, 2017. 49-60.

BibTeX: 

Last updated on 2019-23-06 at 07:14

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