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Numerical assessment of springback for the deep drawing process by level set interpolation using shape manifolds

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Abstract

In this paper, we introduce an original shape representation approach for post-springback characterization based on the automatic generation of parameterized level set functions. The central idea is the concept of the shape manifold representing the design domain in the reduced-order shape-space. Performing Proper Orthogonal Decomposition on the shapes followed by using the Diffuse Approximation allows us to efficiently reduce the problem dimensionality and to interpolate uniquely between admissible input shapes, while also determining the smallest number of parameters needed to characterize the final formed shape. We apply this methodology to the problem of springback assessment for the deep drawing operation of metal sheets.

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Acknowledgments

This work was carried out in the framework of the Labex MS2T, which was funded by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02).

The results in this paper were obtained as part of the OASIS project, supported by OSEO within the contract FUI no. F1012003Z.

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Authors and Affiliations

  1. Laboratoire Roberval UMR 7337 UTC-CNRS, Universite de Technologie de Compiegne, Compiegne, France

    Balaji Raghavan, Guenhael Le Quilliec, Piotr Breitkopf, Alain Rassineux, Jean-Marc Roelandt & Pierre Villon

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  1. Balaji Raghavan
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  2. Guenhael Le Quilliec
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  3. Piotr Breitkopf
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  4. Alain Rassineux
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  5. Jean-Marc Roelandt
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  6. Pierre Villon
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Correspondence to Piotr Breitkopf.

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Raghavan, B., Le Quilliec, G., Breitkopf, P. et al. Numerical assessment of springback for the deep drawing process by level set interpolation using shape manifolds. Int J Mater Form 7, 487–501 (2014). https://doi.org/10.1007/s12289-013-1145-8

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