In an industrial context, selecting an appropriate crystal plasticity (CP) model that balances efficiency and accuracy when modelling deformation texture (DT) is crucial. This study compared DTs in IF-steel after undergoing cold rolling reductions using different CP models for two input texture scenarios. Three mean-field (MFCP) models were utilised in their most basic configurations, without considering grain fragmentation or strain hardening, in addition to a dislocation-density-based full-field (FFCP) model. The study quantitatively compared the results from the MFCP models with those from the FFCP models. Furthermore, all CP model results were compared with experimental textures obtained from electron back-scatter diffraction (EBSD) experiments. The findings revealed that certain MFCP models could predict deformation textures as accurately as the FFCP models. Notably, one of the MFCP models exhibited a superior match with experimental textures for cold rolling reductions at 60%. Upon closer examination of specific crystallographic components, it was observed that MFCP models tended to predict a stronger {111}〈211〉 component, while the full-field model favours the {111}〈011〉 component. It is crucial to emphasise the importance of quantifying the texture within individual grains when assessing the macro-level deformation texture in rolling simulations.

High-resolution three-dimensional crystal plasticity simulations are used to investigate deformation heterogeneity and microstructure evolution during cold rolling of interstitial free (IF-) steel. A Fast Fourier Transform (FFT)-based spectral solver is used to conduct crystal plasticity simulations using a dislocation-density-based crystal plasticity model. The in-grain texture evolution and misorientation spread are consistent with experimental results obtained using electron backscatter diffraction (EBSD) experiments. The crystal plasticity simulations show that two types of strain localization features develop during the large strain deformation of IF-steel. The first type forms band-like areas with large strain accumulation that appear as river patterns extending across the specimen. In addition to these river-like patterns, a second type of strain localization with rather sharp and highly localized in-grain shear bands is identified. These localized features are dependent on the crystallographic orientation of the grain and extend within a single grain. In addition to the strain localization, the evolution of in-grain orientation gradients, misorientation features, dislocation density, kernel average misorientation, and stress in major texture components are discussed.

Predicting microstructure and (micro-)texture evolution during thermo-mechanical processing requires the combined simulation of plastic deformation and recrystallization. Here, a simulation approach based on the coupling of a full-field dislocation density based crystal plasticity model and a cellular automaton model is presented. A regridding/remeshing procedure is used to transfer data between the deformed mesh of the large-strain crystal plasticity model and the regular grid of the cellular automaton. Moreover, a physics based nucleation criterion has been developed based on dislocation density difference and changes in orientation due to deformation. The developed framework is used to study meta-dynamic recrystallization during double-hit compression tests and multi-stand rolling in high-resolution representative volume elements. These simulations reveal a good agreement with experimental results in terms of texture evolution, mechanical behaviour and growth kinetics, while enabling insights regarding the effect of nucleation on kinetics and crystallographic texture evolution.

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.

The capability of high-resolution modeling of crystals subjected to large plastic strain is essential in predicting many important phenomena occurring in polycrystalline materials, such as microstructure, deformation localization and in-grain texture evolution. However, due to the heterogeneity of the plastic deformation in polycrystals, the simulation mesh gets distorted during the deformation. This mesh distortion deteriorates the accuracy of the results, and after reaching high local strain levels, it is no longer possible to continue the simulation. In this work, two different adaptive remeshing approaches are introduced for simulating large deformation of 3D polycrystals with high resolution under periodic boundary conditions. In the first approach, a new geometry with a new mesh is created, and then the simulation is restarted as a new simulation in which the initial state is set based on the last deformation state that had been reached. In the second approach, the mesh is smoothened by removing the distortion part of the deformation, and then the simulation is continued after finding a new equilibrium state for the smoothed mesh and geometry. The first method is highly efficient for conducting high-resolution large-deformation simulations. On the other hand, the second method's primary advantage is that it can overcome periodicity issues related to shear loading, and it can be used in conjunction with complex loading conditions. The merits of the methodologies are demonstrated using full-field simulations performed using a dislocation-density-based crystal plasticity model for Interstitial free (IF-) steel. Particular emphasis is put on studying the effect of resolution and adaptive meshing. The algorithms presented have been implemented into the free and open-source software package, DAMASK (Düsseldorf Advanced Material Simulation Kit).

A cellular automaton algorithm for curvature-driven coarsening is applied to a cold-rolled interstitial-free steel's microstructure - obtained through electron backscatter diffraction (EBSD). Recrystallization nucleation occurs naturally during the simulation, due to the highly heterogeneous and hence competitive growth among pre-existing (sub) grains. The spatial inhomogeneity of the subgrain growth that takes place derives from the large local variations of subgrain sizes and misorientations that comprise the prior deformed state. The results show that capillary-driven selective growth takes place to the extent that the prior elongated and deformed grains are replaced by equiaxed grains with no interior small-angle boundaries. Additionally, during the simulation certain texture components intensify and others vanish, which indicates that preferential growth occurs in a fashion that relates to the crystal orientations’ topology. The study of the early stages of recrystallization (i.e. nucleation) shows that the pre-existing subgrains that eventually recrystallize, exhibit certain topological characteristics at the prior deformed state. Successful nucleation occurs mostly for pre-existing matrix subgrains abutting shear bands or narrow deformation bands and particularly at regions where the latter intersect.

This is a viewpoint paper on recent progress in the understanding of the microstructure–property relations of advanced high-strength steels (AHSS). These alloys constitute a class of high-strength, formable steels that are designed mainly as sheet products for the transportation sector. AHSS have often very complex and hierarchical microstructures consisting of ferrite, austenite, bainite, or martensite matrix or of duplex or even multiphase mixtures of these constituents, sometimes enriched with precipitates. This complexity makes it challenging to establish reliable and mechanism-based microstructure–property relationships. A number of excellent studies already exist about the different types of AHSS (such as dual-phase steels, complex phase steels, transformation-induced plasticity steels, twinning-induced plasticity steels, bainitic steels, quenching and partitioning steels, press hardening steels, etc.) and several overviews appeared in which their engineering features related to mechanical properties and forming were discussed. This article reviews recent progress in the understanding of microstructures and alloy design in this field, placing particular attention on the deformation and strain hardening mechanisms of Mn-containing steels that utilize complex dislocation substructures, nanoscale precipitation patterns, deformation-driven transformation, and twinning effects. Recent developments on microalloyed nanoprecipitation hardened and press hardening steels are also reviewed. Besides providing a critical discussion of their microstructures and properties, vital features such as their resistance to hydrogen embrittlement and damage formation are also evaluated. We also present latest progress in advanced characterization and modeling techniques applied to AHSS. Finally, emerging topics such as machine learning, through-process simulation, and additive manufacturing of AHSS are discussed. The aim of this viewpoint is to identify similarities in the deformation and damage mechanisms among these various types of advanced steels and to use these observations for their further development and maturation.

A severe obstacle for the routine use of crystal plasticity models is the effort associated with determining their constitutive parameters. Obtaining these parameters usually requires time-consuming micromechanical tests that allow probing of individual grains. In this study, a novel, computationally efficient, and fully automated approach is introduced which allows the identification of constitutive parameters from macroscopic tests. The approach presented here uses the response surface methodology together with a genetic algorithm to determine an optimal set of parameters. It is especially suited for complex models with a large number of parameters. The proposed approach also helps to develop a quantitative and thorough understanding of the relative influence of the different constitutive parameters and their interactions. Such general insights into parameter relations in complex models can be used to improve constitutive laws and reduce redundancy in parameter sets. The merits of the methodology are demonstrated on the examples of a dislocation-density-based crystal plasticity model for bcc steel, a phenomenological crystal plasticity model for fcc copper, and a phenomenological crystal plasticity model incorporating twinning deformation for hcp magnesium. The approach proposed is, however, model-independent and can be also used to identify parameters of, for instance, fatigue, creep and damage models. The method has been implemented into the Düsseldorf Advanced Material Simulation Kit (DAMASK) and is available as free and open-source software. The capability of translating complex material response into a micromechanical digital twin is an essential precondition for the ongoing digitalization of material property prediction, quality control of semi-finished parts, material response in manufacturing and the long-term behavior of products and materials when in service.