Microstructure-Sensitive Computational Estimates of Driving Forces for Surface Versus Subsurface Fatigue Crack Formation in Duplex Ti-6Al-4V and Al 7075-T6

PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

“…The scaling performance of the PRISMS-Fatigue framework is investigated first for a large-scale fatigue simulation of aluminum alloy 7075-T6. Volume-averaged FIPs are fit to the Gumbel extreme value distribution (EVD), which permits comparison of fatigue resistance between different microstructures 17,37 , or fatigue life prediction with a proper calibration 27 . In the following step, a slightly smaller simulation is used as a benchmark to compare the FIP EVD response and performance scalability of an identical Al 7075-T6 microstructure instantiation simulated in both PRISMS-Fatigue and a similar framework implemented using ABAQUS.…”

“…Five different fatigue-related applications are investigated to demonstrate the flexibility of the PRISMS-Fatigue framework. The applications include the effects of different crystallographic textures, grain morphologies, boundary conditions, multiaxial strain states and strain magnitudes, and sample sizes, i.e., number of grains, on the FIP EVDs (fatigue crack formation driving force) 17,35,37 . The first four applications employ the more traditional strategy of simulating Statistical Volume Element (SVE) ensembles to build up the extreme value response.…”

“…Moreover, morphological texture (e.g., shape of grains) and boundary conditions also play important roles, even for high symmetry fcc metals such as Al alloys 49 . Accordingly, SVE ensembles of three different crystallographic textures are instantiated using DREAM.3D and include cubic, random, and rolled textures (See Supplementary Information section) 37 . For each of the three textures, three ensembles were instantiated, i.e., nine ensembles in total, as follows: (1) equiaxed grains with fully periodic boundary conditions, (2) elongated grains with fully periodic boundary conditions, and (3) equiaxed grains with one set of parallel faces in each SVE set as traction-free/free surfaces/ nonperiodic while the other two pairs of faces are set as periodic.…”

“…The framework is linked to the open-source software DREAM.3D 34 , PRISMS-Plasticity 31 , Python, and ParaView to respectively generate microstructure instantiations, calculate material response, execute scripted workflows to compute volume-averaged FIPs, and to visualize data. DREAM.3D is widely used by the materials science community to generate synthetic 17,25,[35][36][37] and reconstruct experimental (e.g., EBSD with serial sectioning 38 , Laguerre tessellations using grain centroids and relative volumes from HEDM experiments 15 ) microstructures, largely for subsequent numerical simulation. PRISMS-Plasticity 31 serves as the CPFE engine for PRISMS-Fatigue.…”

Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow

“…This reduces the FIP averaging domain to regions within the grain, but results in bands with various volumes. Following the SBA approach of Stopka and McDowell [50], which built on the band averaging scheme as proposed by Castelluccio and McDowell, the bands were further divided into sub-band regions as shown in Fig. 1.…”