ICAMS / Interdisciplinary Centre for Advanced Materials Simulation


Generation of statistically-based RVE for dual-phase microstructure using multiplicative weighted Voronoi tessellation and the application to crystal plasticity

Date: 24.09.2012
Place: International workshop on computational mechanics of materials IWCMM XXII

Napat Vajragupta
Junhe Lian
Mohamed Sharaf
Benjamin Schmaling
Anxin Ma
Sebastian Münstermann
Alexander Hartmaier
Wolfgang Bleck

Dual phase steels are well suited to the automotive application. Their microstructures comprise constituents of strong distinction in mechanical properties. As a result, dual phase steels exhibit remarkably high-energy absorption as well as an excellent combination of strength and ductility. Heterogeneous deformation can be observed on the microstructure scale due to the behavior of individual constituents. Hence, a reliable microstructure-based simulation approach for describing material deformation is needed. The developed approach takes the microstructure features such as microstructure morphology into account to reflect the influence of the features on the deformation behavior.
The aim of this study is to generate artificial dual phase microstructure models based on quantitative results of microstructure morphology. These quantitative results of microstructure morphology include individual phase grain size and orientation distribution functions. To achieve this aim, the multiplicative weighted Voronoi tessellation approach is deployed. Firstly, the dual phase material is characterized by EBSD analysis to investigate individual phase grain size and orientation distribution functions. Based on the characterization, the results are then input into a multiplicative weighted Voronoi tessellation based algorithm to generate artificial microstructure FE-models that are applicable for bimodal distribution. Afterward, a phenomenological based crystal plasticity model is applied to describe the strain-hardening behavior of the dual phase microstructure. For material parameters derivation, orientation is firstly assigned to all the grains based on characterized orientation distribution functions. Moreover, Nano-indentation coupled with virtual experiment and the optimization algorithm is also performed in order to obtain crystal plasticity parameters.


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