A new paper, titled "Accelerating crystal plasticity simulations using GPU multiprocessors", was recently published in International Journal for Numerical Methods in Engineering and can be found at the publisher's site following this link or here.
Since recrystallization is driven by reduction of stored energy, the amount of cold work influences the grain size evolution. Materials with grain sizes down to the nanometer scale can be obtained by exposing the material to severe plastic deformation (SPD). Such SPD processes include Equal Channel Angular Pressing/Extrusion (ECAP/ECAE), Asymmetric Rolling (ASR), Multi-Directional Forging (MDF), High-Pressure Torsion (HPT) and Twist Extrusion (TE). Some of these processes are discussed further below.
During ECAP, studied in Hallberg et al. (2010), the material specimen is pressed through a channel die as illustrated below.
The amount of effective plastic deformation, denoted by , that is imposed onto the specimen in each pass can be estimated from knowledge of the die geometry according to
Varying the channel geometry thus allows control of the amount of plastic deformation that is exerted onto the specimen in each pass through the die. If the specimen is rotated between each pass through the die, different standardized processing routes can be obtained.
Another SPD process is asymmetric rolling, discussed in Hallberg (2013), where a conventional rolling process can be made asymmetric by different methods.
The asymmetry of the process can be induced by having different radii of the rolls, by different roller velocities, i.e. or by different friction/lubrication conditions at each side of the sheet. The asymmetry increases the shear deformation of the rolled sheet and hence the total amount of effective plastic deformation.
Beside casting and machining, forming is one of the main processes for manufacturing of components from metallic materials. Forming processes include sheet metal forming as well as bulk metal forging. Metal forming generally involves significant plastic deformations, elevated temperatures, high deformation velocities and an increased risk for initiation of cracks in the material. These macroscopic process conditions are intimately connected to the microstructure evolution inside the material.
In Hallberg et al. (2007) and Hallberg et al. (2010), deep-drawing of stainless steel is used as application examples for constitutive models where martensitic phase transformation is considered, see the illustration below. As the martensite phase is much harder than the parent austenitic phase, the material properties may change dramatically in the presence of this kind of diffusionless - and thus rapid - phase transformation.
The influence of deformation rate and material pre-processing in metal forging is studied in Hallberg et al. 2009. Different behavior of a 100Cr6 steel, due to previous tempering or annealing, was studied in high strain rate axisymmetric compression, experimentally as well as through numerical simulations.
The illustration below is taken from Hallberg et al. 2009. Note the development of a "shear cross" (white lines) in the tempered - and much harder - material. This localized deformation is absent in the annealed specimen.
Another example of metal forming is rolling, for example discussed in Hallberg (2013). A conventional rolling process can be made asymmetric by different methods in order to increase the deformation imposed onto the sheet.
The asymmetry of the process can be induced by having different radii of the rolls, by different roller velocities, i.e. or by different friction/lubrication conditions at each side of the sheet. The asymmetry increases the shear deformation of the rolled sheet and hence the total amount of effective plastic deformation. This is utilized in severe plastic deformation (SPD) processes for production of very fine-grained metals.
Mesoscale models of microstructure evolution allow studying of heterogeneous dislocation density distributions and related gradient effects. This is closely connected to the well-renowned Hall-Petch effect, stating a proportionality between the yield stress and the inverse of the square-root of the average grain size according to
The interaction between dislocation motion and grain boundaries can be modeled by different approaches. Some examples are given below.
In Hallberg and Ristinmaa (2013) (also discussed in this conference presentation), a hybrid finite difference/cellular automaton model is established where dislocation density gradient are modeled in a reaction-diffusion system. This approach results in the expected Hall-Petch behavior of the macroscopic flow stress – without including an explicit dependence on the grain size – in addition to providing a physically sound dislocation density distribution, indicated in the figure below.
By this modeling approach, the dislocation density will be concentrated at grain boundaries and particularly at triple junctions, directly providing the sites for nucleation of recrystallization. This is in contrast to the common modeling approach where nuclei are placed manually at appropriate sites in the microstructure.
Another approach to modeling of dislocation and grain boundary interaction is taken in Hallberg (2013) where a level set formulation is used to model polycrystal grain structures.
Many important aspects of the macroscopic material behavior in metals are controlled by the microlevel grain structure. Strength and ductility depends largely on the size and distribution of the grains. Especially the presence of grain boundaries plays an important role, not least in the macroscopic deformation hardening of the material. Grain boundaries pose obstacles to slip deformation by preventing dislocation motion, resulting in localized dislocation storage and heterogeneous deformation fields within the grains. This is closely related to the classical Hall-Petch relation, where the macroscopic flow stress is porportional to the inverse of the square root of the average grain size. More fine-grained materials will thus exhibit a higher flow stress than more coarse-grained materials.
The Hall-Petch effect will, however, tend to reverse as very small grains are considered. At a grain sizes of approximately 10 nm or below, other mechanisms such as grain boundary sliding usually cause a softening effect instead of the common Hall-Petch hardening.
Grain boundaries also contribute to the material behavior in many other aspects. They are often sites for initiation of defects such as cracks, they influence the material's susceptibility to chemical attack such as through corrosion and they influence the electrical conductivity of the material. In addition, migrating grain boundaries tend to interact with second-phase particles and the presence and arrangement of grain boundaries influence both the electrical conductivity and the diffusion properties of the material.
As the relatively ductile austenite phase is transformed in to harder and more brittle martensite in the vicinity stress-concentrations, the material conditions change and also the conditions for crack formation and propagation. Fatigue fracture can be considerably influenced by this kind of diffusionless phase transformation due to the higher fracture strength of martensite, compared to that of austenite. In Hallberg et al. (2012) the influence of martensite formation on fracture behavior and crack tip conditions is investigated, as illustrated below.
Taking a continuum-mechanical perspective, the isothermal model in Hallberg et al. (2007) introduces the volume fraction of martensite as an internal variable. Along with a transformation condition, dependent on the state of deformation and on temperature, this allows the evolution of the martensitic phase to be traced. The presence of a transformation condition allows establishment of a transformation potential surface, much like the yield condition and yield surface found in plasticity theory. The transformation surface is illustrated in deviatoric stress space and in the meridian plane below.
Depending on which one is active, the yield and transformation conditions determine the response of the material. The relative influence of austenite and martensite on mechanical material properties is considered through a homogenization procedure, based on the phase fractions.
The above isothermal model is further elaborated in Hallberg et. al (2010b), where full thermo-mechanical coupling is considered. These models are suitable for large-scale simulations of metal forming processes involving materials exposed to martensitic phase transformation. The application to sheet metal forming is illustrated below by images from simulations of a deep-drawing process.