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A knowledge-based surrogate modeling approach for cup drawing with limited data

 
: Morand, L.; Helm, D.; Iza-Teran, R.; Garcke, J.

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Volltext ()

International Deep Drawing Research Group -IDDRG-:
38th International Deep Drawing Research Group Annual Conference, IDDRG 2019 : Forming 4.0: Big Data - Smart Solutions, 3-7 June 2019, Enschede, Netherlands
Bristol: IOP Publishing, 2019 (IOP conference series. Materials science and engineering 651)
Art. 012047, 8 S.
International Deep Drawing Research Group (IDDRG Conference) <38, 2019, Enschede>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IWM ()
data preprocessing; data mining CRISP-DM; Finite element simulation; cup drawing; surrogate modeling

Abstract
To predict the quality of a process outcome with given process parameters in real-time, surrogate models are often adopted. A surrogate model is a statistical model that interpolates between data points obtained either by process measurements or deterministic models of the process. However, in manufacturing processes the amount of useful data is often limited, and therefore setting up a sufficiently accurate surrogate model is challenging. The present contribution shows how to handle limited data in a surrogate modeling approach using the example of a cup drawing process. The purpose of the surrogate model is to classify the quality of the drawn cup and to predict its final geometry. These classification and regression tasks are solved via machine learning methods. The training data is sam pled on a relatively wide range varying three parameters of a finite element simulation, namely sheet metal thickness, blank holder force, and friction. The geometrical features of the cup are extracted using domain knowledge. Besides this knowledge-based approach, an outlook is given for a data-driven surrogate modeling approach.

: http://publica.fraunhofer.de/dokumente/N-574710.html