Datasets within this collection
- 3D-EBSD data and analysis of Ti-6Al-4V fabricated using electron powder bed fusion with a random scan strategy (R3) This data is a companion to the manuscript '3D Electron backscatter diffraction characterization of fine α titanium microstructures: collection, reconstruction, and analysis methods', by Ryan DeMott, Nima Haghdadi, Charlie Kong, Ziba Gandomkar, Matthew Kenney, Peter Collins, and Sophie Primig, currently submitted to Ultramicroscopy. It includes the raw data and code used for analysis of a slightly truncated version of the 'random' dataset which appears in that manuscript as well as DeMott, R., Haghdadi, N., Gandomkar, Z. et al. 3D characterization of microstructural evolution and variant selection in additively manufactured Ti-6Al-4 V. J Mater Sci 56, 14763–14782 (2021). https://doi.org/10.1007/s10853-021-06216-2 and DeMott, R., Haghdadi, N., Liao, X. et al. Formation and 3D Morphology of Interconnected α Microstructures in Additively Manufactured Ti-6Al-4V. Acta mater. In Review. Please cite the above publications and acknowledge the authors if using this data or code in your own work. The files include the raw EBSD scans as a library of .ctf files, the pipeline for reconstructing and analyzing the data using the DREAM.3D software package, and the code used for performing further analysis of the data with the MTEX toolbox for MATLAB. DREAM.3D v6.5.138 , MATLAB R2019b, and MTEX v5.1.1 were the software versions used. RandomCTFFiles.zip contains the library of .ctf files The DREAM3D pipelines include ImportCTFLibrary.json, which generates an H5EBSD file from the ctf library and FullReconstruction.json, which includes all of the steps described in chapter 4 of the manuscript. A new user may find it easier to split it into several shorter pipelines with outputs at each step. The MTEX code folder includes all of the MATLAB functions and scripts used for the analyses described in chapter 5 of the manuscript. AssignBoundaryTypes.m is a function which uses a .dream3d file path and tolerance to classify intervariant boundary types as described in section 5.1 TripleJunctByNodes.m is a script which uses the functions AssignBoundaryTypes.m and TriadPlot.m to classify and plot three-variant clusters in terms of triple junctions as described in section 5.1 (figure 10a) PlotTriads.m is a script which uses the functions findTriads.m, AssignBoundarTypes.m, and TriadPlot.m to classify and plot three-variant clusters in terms of mutally neighboring grains as described in section 5.2 (figure 10b) NewAssignVariants.m is a script for assigning grains to variants as described in section 5.3
- File Set
- Ti-10-2-3 Thermophysical Properties for Thermal SimulationsTi-10-2-3 Thermophysical Properties for Thermal Simulations with SYSWELD and FLOW-3D.
- High speed and high spatial resolution IR imaging data for single spot meltingThis dataset including the high-speed and high-spatial resolution IR data for single spot ti-6-4 melting. The high speed dataset was captured by Telops m3k high speed camera with 20kHz frame rate and 30um spatial resolution. The high spatial dataset was captured by Micro-Epsilon M1 IR camera with 1kHz frame rate and 8um spatial resolution. The details description is included inside the folder
- Tabular Data
- OM data 2020Here, we are using three different EB PBF scan strategies: linear scan (LS), ordered spot scan (OS), and random spots scan (RS) to fuse the widely used Ti-6Al-4V powder (Ti64). The change in scan strategies and therefore the thermal gradients create variation in the defects within the microstructure of the material. Such variations can be analyzed using optical microscopy (OM). After standard metallography sample preparation, imaging was performed using an Olympus DSX510 optical microscope on 5x magnification to characterize the entire surface of each sample with sufficiently high image resolution. High magnification images of the entire surface with smaller field-of-view were first taken step-by-step and subsequently stitched together. Samples were then etched to reveal the underlying microstructure to compare to existing EBSD scans to compare the grain structure exhibited from using different techniques. Using image processing (MIPAR) software, AM build defects were identified, quantified, and exported to a coding language for further data analysis. The code translates the provided quantitative data and generates histograms for eccentricity (roundness of defects), orientation (with respect to the positive x-axis), and caliper diameter (size), site-specifically. This allows to analysis the spatial distribution of defects, e.g., difference between defects in the center of the build vs. edge defects in each sample.
- MC Simulation of ClusteringSimple approach to describe Spinodal decomposition