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Determination of Forming Limits in Sheet Metal Forming Using Deep Learning

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Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr. 3, 91058 Erlangen, Germany
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Institute of Manufacturing Technology, Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 13, 91058 Erlangen, Germany
*
Author to whom correspondence should be addressed.
Materials 2019, 12(7), 1051; https://doi.org/10.3390/ma12071051
Received: 18 March 2019 / Revised: 25 March 2019 / Accepted: 27 March 2019 / Published: 30 March 2019
(This article belongs to the Special Issue Formability of Materials)
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. View Full-Text
Keywords: pattern recognition; machine learning; deep learning; forming limit curve; sheet metal forming pattern recognition; machine learning; deep learning; forming limit curve; sheet metal forming
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Jaremenko, C.; Ravikumar, N.; Affronti, E.; Merklein, M.; Maier, A. Determination of Forming Limits in Sheet Metal Forming Using Deep Learning. Materials 2019, 12, 1051.

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