Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteileigenschaften

Sauer C, Küstner C, Schleich B, Wartzack S (2017)


Publication Language: German

Publication Type: Conference contribution, Conference Contribution

Publication year: 2017

Publisher: TuTech Verlag

City/Town: Hamburg

Pages Range: 49-60

Conference Proceedings Title: Design for X. Beiträge zum 28. DfX-Symposium

Event location: Bamberg DE

ISBN: 978-3-946094-20-3

URI: https://www.archiv.mfk.tf.fau.de?file=pubmfk_59d7698c70864

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.

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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 (S. 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.

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