TR73

  • Increase font size
  • Default font size
  • Decrease font size


C3 - Parameter and Formoptimization



Parameter and Shape Optimization in Finite Elastoplasticity

Project Status: Active

Last Update: 20.02.2019



Members


The subproject deals with methods and applications of parameter and shape optimization in finite elastoplasticity with a focus on solid sheet metal forming (BMU). The identification of the material parameters as well as the determination of the optimal workpiece shape represent a non-linear inverse optimization problem. In phases 1 and 2, the characterization of the steels DC04 and DP600 was realized on a biaxial tensile compression testing machine and methods for inverse parameter identification were developed. At the same time, efficient and stable algorithms were developed to compute the optimal initial workpiece geometry for a defined target geometry of a deformed workpiece. A validation was carried out by real forming tests. Based on these results, the investigations of parameter variations and the adaptation of the form finding to more complex functional components in a comprehensive view of the manufacturing process are the main aspects of the further work.
For this purpose, the previously neglected uncertainties from the experiments (e.g. force sensors, sheet thickness fluctuations, measurement of the deformation field) and the influences on the simulation must first be analyzed and the resulting deviations of the identified material parameters be quantified. The corresponding distributions are then applied in the multi-stage processes of the BMU in order to investigate the influence of the parameter fluctuations on the process simulation. A central finding is the effect of different influencing variables, e.g. measurement uncertainties on the inverse identification and thus a statement about the robustness of the characterisation. On the other hand, the influence of the material parameter distributions on the BMU is shown, whereby statements about the necessary quality of the parameter identification can be made.
An important field of application for inverse form finding is the optimal design of tailored blanks. In order to be able to apply the developed methodology to the entire process portfolio of the SFB/Transregio 73, the algorithms are adapted on the one hand to mesh refinement for the mapping of secondary form elements and on the other hand to multi-stage forming processes with pre-hardening. The optimization process can already be coupled with any FE software via subroutines as a black box and can therefore be used for any purpose. In order to increase the application possibilities in the entire product development cycle, however, manufacturing restrictions and process limits still have to be integrated into the formfinding methodology. In order to also consider scattering influencing variables, methods for robust shape optimization are also implemented and tested.

TRHomepage3


Working Groups


Publications

    2018

    • Caspari, M.; Landkammer, P.; Steinmann, P.: A non-invasive node-based form finding approach with discretization-independent target configuration5(2018)1, Advanced Modeling and Simulation in Engineering Sciences, pp. 11
    • Härtel, M.; Pfeiffer, S.; Schmaltz, S.; Söhngen, B.; Kulawinski, D.; Willner, K.; Henkel, S.; Biermann, H.; Wagner, M.F.-X.: On the identification of an effective cross section for a cruciform specimen. In: Strain, 54(2018)1, John Wiley & Sons Ltd, pp. 1-12
    • Caspari, M.; Landkammer, P.; Steinmann, P.: Illustration of an improved non-invasive form finding algorithm. In: AIP Conference Proceedings (Edt.): 21st International ESAFORM Conference on Material Forming, ESAFORM 2018; Palermo, 1960(2018)110003, Parlermo, Italy, published

    2017

    • Landkammer, P.; Söhngen, B.; Steinmann, P.; Willner, K.: On gradient-based optimization strategies for inverse problems in metal forming. In: Menzel, A. (Edt.): GAMM-Mitteilungen - Themenheft: Computational Manufacturing, 40(2017), pp. 27-56
    • Landkammer, P.; Caspari, M.; Steinmann, P.: Improvements on a non-invasive, parameter-free approach to inverse form finding. In: Computational Mechanics, (2017), pp. 1-15
    • Caspari, M.; Landkammer, P.; Steinmann, P.: Shape optimization with application to inverse form finding and the use of mesh adaptivity. In: 7th GACM Colloquium on Computational Mechanics, (2017), published
    • Caspari, M.; Landkammer, P.; Steinmann, P.: Inverse from finding with h-adaptivity and an application to a notch stamping process. In: Computational Plasticity XIV on Fundamentals and Applications, (2017), Barcelona, Spain, published
    • Söhngen, B.; Willner, K.: Identification of nonlinear kinematic hardening parameters for sheet metal from biaxial loading tests. In: Oñate E., Owen D.R.J. , Peric D. , Chiumenti M. (Edt.): XIV International Conference on Computational Plasticity (COMPLAS 2017), (2017), pp. 373-384

    Presentations

      2018

      • 25.04.2018: Caspari, M.: Illustration of an improved non-invasive form finding algorithm, Palermo, Sizilien
      • 24.07.2018: Caspari, M.: Node-Based Form Finding with Shape-Dependent Target Definition, New York

      2017

      • 05.09.2017: Caspari, M.: Inverse from finding with h-adaptivity and an application to a notch stamping process, Barcelona, Spain
      • 13.10.2017: Caspari, M.: Shape optimization with application to inverse form finding and the use of mesh adaptivity, Stuttgart