ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

An innovation in finite element simulation via crystal plasticity assessment of grain morphology effect on sheet metal formability

Experimental and numerical study regarding the uniaxial tensile test and the forming limit diagram are addressed in this paper for AL2024 with the face-centered cube structure. First, representation of a grain structure can be obtained directly by mapping metallographic observations via scanning electron microscopy approach. Artificial grain microstructures produced by Voronoi Tessellation method are employed in the model using VGRAIN software. By resorting to the finite element software (ABAQUS) capabilities, the constitutive equations of the crystal plasticity were utilized and implemented as a user subroutine material UMAT code. The hardening parameters were calibrated by a trial and error approach in order to fit experimental tensile results with the simulation. Then the effect of the changing grain size, the heterogeneity factor, and the grain aspect ratio were studied for a uniaxial tensile test to emphasize the importance of the microstudy behavior of grains in material behavior. Furthermore, the polycrystal plasticity grain distribution was employed in the Nakazima test in order to obtain the forming limit diagram. The crystal plasticity-driven forming limit diagram reveals more accurate strains, taking into account the involving the micromechanical features of the grains. An innovative approach is pursued in this study to discover the necking angle, both in tensile test or Nakazima samples, showing a good agreement with the experiment results.

Polycrystal Simulation of Texture-Induced Grain Coarsening during Severe Plastic Deformation

During severe plastic deformation (SPD), there is usually extended grain fragmentation, associated with the formation of a crystallographic texture. The effect of texture evolution is, however, coarsening in grain size, as neighbor grains might coalesce into one grain by approaching the same ideal orientation. This work investigates the texture-induced grain coarsening effect in face-centered cubic polycrystals during simple shear, in 3D topology. The 3D polycrystal aggregate was constructed using a cellular automaton model with periodic boundary conditions. The grains constituting the polycrystal were assigned to orientations, which were updated using the Taylor polycrystal plasticity approach. At the end of plastic straining, a grain detection procedure (similar to the one in electron backscatter diffraction, but in 3D) was applied to detect if the orientation difference between neighboring grains decreased below a small critical value (5°). Three types of initial textures were considered in the simulations: shear texture, random texture, and cube-type texture. The most affected case was the further shearing of an initially already shear texture: nearly 40% of the initial volume was concerned by the coalescence effect at a shear strain of 4. The coarsening was less in the initial random texture (~30%) and the smallest in the cube-type texture (~20%). The number of neighboring grains coalescing into one grain went up to 12. It is concluded that the texture-induced coarsening effect in SPD processing cannot be ignored and should be taken into account in the grain fragmentation process.