Determination of Forming Limits in Sheet Metal Forming Using Deep Learning

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autorinnen und Autoren: Jaremenko C, Ravikumar N, Affronti E, Merklein M, Maier A
Zeitschrift: Materials
Jahr der Veröffentlichung: 2019
Band: 12
Heftnummer: 7
ISSN: 1996-1944


Abstract

The forming limit curve (FLC) is used to model the onset of sheet metal instability during forming processes e.g., in the area of finite element analysis, and is usually determined by evaluation of strain distributions, derived with optical measurement systems during Nakajima tests. Current methods comprise of the standardized DIN EN ISO 12004-2 or time-dependent approaches that heuristically limit the evaluation area to a fraction of the available information and show weaknesses in the context of brittle materials without a pronounced necking phase. To address these limitations, supervised and unsupervised pattern recognition methods were introduced recently. However, these approaches are still dependent on prior knowledge, time, and localization information. This study overcomes these limitations by adopting a Siamese convolutional neural network (CNN), as a feature extractor. Suitable features are automatically learned using the extreme cases of the homogeneous and inhomogeneous forming phase in a supervised setup. Using robust Student's t mixture models, the learned features are clustered into three distributions in an unsupervised manner that cover the complete forming process. Due to the location and time independency of the method, the knowledge learned from formed specimen up until fracture can be transferred on to other forming processes that were prematurely stopped and assessed using metallographic examinations, enabling probabilistic cluster membership assignments for each frame of the forming sequence. The generalization of the method to unseen materials is evaluated in multiple experiments, and additionally tested on an aluminum alloy AA5182, which is characterized by Portevin-LE Chatlier effects.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Affronti, Emanuela
Lehrstuhl für Fertigungstechnologie
Jaremenko, Christian
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Merklein, Marion Prof. Dr.-Ing.
Lehrstuhl für Fertigungstechnologie
Ravikumar, Nishant
Lehrstuhl für Informatik 5 (Mustererkennung)


Zitierweisen

APA:
Jaremenko, C., Ravikumar, N., Affronti, E., Merklein, M., & Maier, A. (2019). Determination of Forming Limits in Sheet Metal Forming Using Deep Learning. Materials, 12(7). https://dx.doi.org/10.3390/ma12071051

MLA:
Jaremenko, Christian, et al. "Determination of Forming Limits in Sheet Metal Forming Using Deep Learning." Materials 12.7 (2019).

BibTeX: 

Zuletzt aktualisiert 2019-07-05 um 14:08