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  4. A knowledge-based surrogate modeling approach for cup drawing with limited data
 
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2019
Conference Paper
Titel

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

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.
Author(s)
Morand, L.
Fraunhofer-Institut für Werkstoffmechanik IWM
Helm, D.
Fraunhofer-Institut für Werkstoffmechanik IWM
Iza-Teran, R.
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Garcke, J.
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Hauptwerk
38th International Deep Drawing Research Group Annual Conference, IDDRG 2019
Konferenz
International Deep Drawing Research Group (IDDRG Conference) 2019
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DOI
10.1088/1757-899x/651/1/012047
Externer Link
Externer Link
Language
English
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Fraunhofer-Institut für Werkstoffmechanik IWM
Tags
  • data preprocessing

  • data mining CRISP-DM

  • Finite element simula...

  • cup drawing

  • surrogate modeling

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