Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteilei-genschaften

DFX 2017: Proceedings of the 28th Symposium Design for X, 4-5 October 2017, Bamburg, Germany

Year: 2017
Editor: Dieter Krause, Kristin Paetzold, Sandro Wartzack
Author: Christopher, Sauer; Christof, Küstner; Benjamin, Schleich; Sandro, Wartzack
Series: DfX
Institution: FAU Erlangen-Nürnberg
Section: Data Mining
Page(s): 049-060
ISBN: 978-3-946094-20-3-5

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 charac-teristics. SLASSY enables product developers to evaluate the manufacturabil-ity of their current part design. For this, SLASSY uses data from manufactur-ing 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 manufac-turing 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.

Keywords: product properties, deep learning, data mining, neural networks