Simulation domain size requirements for elastic response of 3D polycrystalline materials

Abstract

A fast Fourier transform (FFT) based spectral algorithm is used to compute the full field mechanical response of polycrystalline microstructures. The field distributions in a specific region are used to determine the sensitivity of the method to the number of surrounding grains through quantification of the divergence of the field values from the largest simulation domain, as successively smaller surrounding volumes are included in the simulation. The analysis considers a mapped 3D structure where the location of interest is taken to be a particular pair of surface grains that enclose a small fatigue crack, and synthetically created statistically representative microstructures to further investigate the effect of anisotropy, loading condition, loading direction, and texture. The synthetic structures are generated via DREAM3D and the measured material is a cyclically loaded, Ni-based, low solvus high refractory (LSHR) superalloy that was characterized via 3D high energy x-ray diffraction microscopy (HEDM). Point-wise comparison of distributions in the grain pairs shows that, in order to obtain a Pearson correlation coefficient larger than 99%, the domain must extend to at least the third nearest neighbor. For an elastic FFT calculation, the stress–strain distributions are not sensitive to the shape of the domain. The main result is that convergence can be specified in terms of the number of grains surrounding a region of interest.

Grain interaction mechanisms leading to intragranular orientation spread in tensile deformed bulk grains of interstitial-free steel

“…This gives a unique possibility of investigating the behaviour of individual grains and how they interact with their environment. Particular emphasis has been on understanding the deformation behaviour of metals by mapping intra-granular orientation gradients as a function of plastic deformation Lind et al, 2014;Pokharel et al, 2015;Toda et al, 2016) and measuring grain-resolved stresses associated with deformation twinning in hexagonal close packed metals (Abdolvand et al, 2015a(Abdolvand et al, , 2015bAydiner et al, 2009;Bieler et al, 2014;, but also processes such as grain nucleation (West et al, 2009), growth (Poulsen et al, 2011;Schmidt et al, 2004Schmidt et al, , 2008 and coarsening (Dake et al, 2016;Sharma et al, 2012), crack evolution (Cerrone et al, 2015;Chatterjee et al, 2015;Oddershede et al, 2012;Ozturk et al, 2016), stress relaxation (Tang et al, 2015), creep (Schuren et al, 2015) and phase transformations (Barton and Bernier, 2012;Hedström et al, 2010;Offerman et al, 2006) have been investigated using 3DXRD. For completeness applications of 3DXRD to minerals (Borthwick et al, 2012;Hall and Wright, 2015;Sørensen et al, 2012a), deep earth science (Nisr et al, 2014(Nisr et al, , 2012Rosa et al, 2015), nuclear materials (Brown et al, 2014;X.…”

Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations

“…Therefore, considerable experimental effort [8,18] has been devoted to the characterization of these deformation microstructure heterogeneities, both in 2D [19][20][21][22][23][24] and recently also 3D [25,26]. Nowadays, it is common practice to investigate the evolution of the deforming microstructure with full-field 3D crystal plasticity representative volume element (RVE) computer modeling [27][28][29] and compare explicitly such results to experiments [18,[30][31][32][33][34][35][36][37][38].…”

Prediction of Steel Nanohardness by Using Graph Neural Networks on Surface Polycrystallinity Maps

“…Graph-based representations of polycrystals have been quite common in the attempt to describe microstructure-property relationships [11][12][13][14]. However, individual grain behaviors in polycrystals have been challenging to identify, with a wealth of constitutive parameters being commonly used to model them [14].…”