Abstract
Physics-based crystal plasticity models rely on certain statistical assumptions about the collective behavior of dislocation populations on one slip system and their interactions with the dislocations on the other slip systems. One main advantage of using such physics-based constitutive dislocation models in crystal plasticity kinematic frameworks is their suitability for predicting the mechanical behavior of polycrystals over a wide range of deformation temperatures and strain rates with the same physics-based parameter set. In this study, the ability of a widely used temperature-dependent dislocation-density-based crystal plasticity formulation to reproduce experimental results, with a main focus on the yield stress behavior, is investigated. First, the material parameters are identified from experimental macroscopic stress–strain curves using a computationally efficient optimization methodology that uses a genetic algorithm along with the response surface methodology. For this purpose, a systematic set of compression tests on interstitial free (IF) steel samples is performed at various temperatures and strain rates. Next, the influence of the individual parameters on the observed behavior is analyzed. Based on mutual interactions between various parameters, the ability to find a unique parameter set is discussed. This allows identifying shortcomings of the constitutive law and sketch ideas for possible improvements. Particular attention is directed toward identifying possibly redundant material parameters, narrowing the acceptable range of material parameters based on physical criteria, and modifying the crystal plasticity formulation numerically for high-temperature use.
Highlights
Crystal plasticity (CP) models are powerful and indispensable tools for modeling and understanding the relationship between the microstructure and the mechanical behavior of crystalline materials (Roters et al, 2010)
Particular attention is directed toward identifying possibly redundant material parameters, narrowing the acceptable range of material parameters based on physical criteria, and modifying the crystal plasticity formulation numerically for high-temperature use
The optimization methodology introduced in this study explores different combinations of material parameters at different loading conditions to find a set of parameters that reproduces all experimental results
Summary
Crystal plasticity (CP) models are powerful and indispensable tools for modeling and understanding the relationship between the microstructure and the mechanical behavior of crystalline materials (Roters et al, 2010). Phenomenological models use laws with fitted variables and are numerically cost-effective While they incorporate the relevant features of plastic slip in metals via kinematic and kinetic assumptions, phenomenological models suffer from the drawback that they consider very limited physical information to define slip rates and the evolution of the internal variables. Any value of the constitutive parameters is deemed valid as long as the behavior of interest, such as the stress–strain curve, is predicted correctly, under the exact boundary and initial conditions where the parameters were fitted. This means, that there is no guarantee that the model correctly predicts the behavior for other loading conditions, e.g. strain rate and temperature, than the ones that have been used for fitting (Mandal et al, 2017)
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