The influence of grain size distribution on strain hardening behavior for dual phase steels using statistica ly informed artificial microstructure model and crystal plasticity

Abstract

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. Various deformation mechanisms can be observed on the microscale owing to their heterogeneous composition. A reliable microstructure-based simulation approach for describing these deformations is hence needed. Therefore, the approach to generate artificial dual phase microstructure models based on the quantitative results of metallographic microstructure analysis and their statistical representation is developed. This method captures several microstructural features such as microstructure morphology and thus enables a simulation-based analysis of the influence of these features on the meso- and macroscopic material behavior. The algorithm input contains representative information about individual phase grain size and orientation distributions. The statistical parameters to represent the grain size distribution function are then input into a multiplicatively weighted Voronoi tessellation based algorithm to generate artificial microstructure geometry models that are applicable to bimodal distribution and with which microstructure deformation (finite element) simulations can be performed. By implementation of the phenomenological based crystal plasticity model to the generated artificial microstructure model, the influence of grain size distribution on the strain hardening behavior can be investigated