![]() ![]() The quantification of plastic localization by slip (slip localization) is challenging in polycrystalline metals. These connections provide useful insight into the design and prediction of mechanical properties in structural components. The systematic investigation of slip as a function of the microstructure helps to better identify the relevant parameters that control the plastic deformation flow. This step is produced by dislocations emerging from the free surface during plastic deformation after gliding along crystallographic planes in the bulk 1. Consequently, slip traces develop and are observed at the free surface of deformed specimens, with each slip trace associated with a local surface step. The authors should be contacted for further information on this in-house software.ĭuring mechanical loading, polycrystalline materials, such as the nickel-based superalloy investigated here, develop irreversible plastic deformation that can manifest in the formation of slip bands. Digital image correlation were performed using the software Xcorrel HDIC. The second version was created with the software suite from Simmetrix (SimModeler Voxel), a commercially available software ( ). One version of a mesh structure was created with XtalMesh 21, a publicly available code on GitHub ( ). The 3D voxelized dataset has been reconstructed using DREAM.3D, a software publicly available on GitHub ( ) or in the following link for direct download (. Reindexing of the EBSD data with the dictionary indexing approach was performed with EMsoft version 4.2, the latest version of the code is available at. The distortion correction algorithms and data analysis tools used in this study can be found at. Multi-modal datasets are of great interest for evaluating the dataset-merging procedures for distortion correction and for correlative measurements analysis tools. Multi-modal dataset of a polycrystalline metallic material: 3d microstructure and deformation fields. Run SPPARKS on PACE on our pinned microstructure to simulate grain growth.Apply pinning algorithm with desired variables selected.Create initial/master random microstructure using SPPARKS.Pinning algorithm: allows pinning of different distributions.SPPARKS: open source code developed by Sandia National Labs.On each simulation we can output various snapshots of the Micro Structure so we can calculate the grain size distribution and as well observe its evolution history.Perform enough Simulations to have a enough statistical data.We will also vary the shape, size and volume fraction of Precipitates.We have an initial random Microstructure to which we are going to place various different distributions of precipitates.Thus we are establishing a Process-Structure Relationship.This will be achieved by using a Data Science Approach. Then we want to predict with sufficient accuracy and in a computationally non-expensive fashion the correlation that exists between a specified Distribution of Particles and the Final Microstructure.We are using Kinetic Monte Carlo equations through the SPPARKS Program to analyze the effect of different precipitate distributions on the Grain Growth of a Microstructure and ultimately its size distribution.The process Spparks simulates is ageing.Precipitates “pin”, deter, this grain growth since they reduce the surface area when a boundary crosses a precipitate.The driving force for grain growth is the grain boundary interfacial free energy.This is the post equivalent of the presentation given for our first progress report. ![]()
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