Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

Abstract

Predicting, understanding, and controlling the mechanical behavior is the most important task when designing structural materials. Modern alloy systems-in which multiple deformation mechanisms, phases, and defects are introduced to overcome the inverse strength-ductility relationship-give raise to multiple possibilities for modifying the deformation behavior, rendering traditional, exclusively experimentally-based alloy development workflows inappropriate. For fast and efficient alloy design, it is therefore desirable to predict the mechanical performance of candidate alloys by simulation studies to replace time-and resource-consuming mechanical tests. Simulation tools suitable for this task need to correctly predict the mechanical behavior in dependence of alloy composition, microstructure, texture, phase fractions, and processing history. Here, an integrated computational materials engineering approach based on the open source software packages DREAM.3D and DA-MASK (Dü sseldorf Advanced Materials Simulation Kit) that enables such virtual material development is presented. More specific, our approach consists of the following three steps: (1) acquire statistical quantities that describe a microstructure, (2) build a representative volume element based on these quantities employing DREAM.3D, and (3) evaluate the representative volume using a predictive crystal plasticity material model provided by DAMASK. Exemplarily, these steps are here conducted for a high-manganese steel.

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

“…[456,457] Yet, knowing the true 3D microstructure is often required to simulate a realistic stress and strain distribution. [32,453,458,459] Second, using computational tools to generate artificial 3D microstructures allows to systematically vary specific microstructural features such as, for example, microstructure banding, phase topologies, volume fractions of the different microstructure constituents, and to investigate their effects on the macroscopic properties both, individually and in concert.…”

Computer-Aided Material Design for Crash Boxes Made of High Manganese Steels

“…The CP model was used to simulate the deformation behavior under plane strain compression conditions and resulted in the determination of the dislocation densities and twin volume fractions after cold rolling degrees of 30%, 40%, and 50%. A similar procedure is described in [3,10,11]. Afterwards, the corresponding dislocation densities for each cold rolling degree were used as input parameters for full field RVE simulations (using 100 grains) of the deformation behavior under uniaxial tensile loading conditions.…”

Quantifying the Contribution of Crystallographic Texture and Grain Morphology on the Elastic and Plastic Anisotropy of bcc Steel

“…Even though this results already in a significantly improved local stress-strain response, taking the full grain size distributions into account would result in a significantly more realistic grain morphology. DREAM.3D, a software developed by Groeber et al [65,66], Groeber and Jackson [67] provides tools for this purpose; the generated microstructures can be directly imported into DAMASK as shown by Diehl et al [68]. Last but not least, the preexisting inhomogeneity of the hardening state among the different orientations/grains should be considered when setting up the simulation.…”