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  "title": "X-ray computed tomography analysis of pore deformation in IN718 made with directed energy deposition via in-situ tensile testing",
  "contactPoint": {
    "fn": "Orion Kafka",
    "hasEmail": "mailto:orion.kafka@nist.gov"
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  "description": [
    "Directed energy deposition (DED) is a metal additive manufacturing technique often used for larger-scale components and part repair, consisting in this case of coaxial powder deposition and laser heating. In essence, this dataset contains many X-ray Computed Tomography images and stress-strain data taken during in-situ mechanical testing of DED IN718. The material was deposited as a \"thin wall\" meaning there are no lack-of-fusion defects. The different specimens were taken from spatially varying locations and in two different orientations with respect to the build direction. Anisotropy and spatial variability are thus measurable. Pore (void) deformation as a function of loading is also measured from these images. Multi-modal data included are: 1) 2D grayscale images and Digital Image Correlation based on same to measure surface strains; 2) 3D x-ray computed tomography images of a portion of the gauge region at a series of different strain/stress levels; 3) Force data that is paired with the surface strain data to construct stress-strain curves. In total, 69 specimens were tested. Some test record of each is preserved here, however not all datasets are complete for all specimens (for instance, due to experimenter error, some are missing DIC images or complete force data). Five example specimens have been selected as demonstration. The associated manuscript explains and discusses all these data, with a focus on the five example specimens. It also demonstrates computational crystal plasticity modeling based on the images, including inverse computation of grain orientation and overall pore shape prediction validation.",
    "General Information:",
    "Notice: see the readme.txt file before proceeding!",
    "Accessing the data:",
    "Instructions for downloading and using the data are included in the \"readme.txt\" file appearing at the top-level directory of this dataset. Further \"readme.txt\" files are included in sub-directories to help orient the data user as to the contents and use of each component of the set. Download instructions are given in the readme, but generally, all the data files except for the raw data can be downloaded manually from the data listing. For bulk downloads and downloading the raw data, the included Python script, pdrdownload.py, can be used as described in the readme file. The full data set, including raw data, is 6.7 TB. Excluding raw data, it is roughly 148 GB.",
    "General structure and files:",
    "The dataset is structured with general information in the top-level directory, and different measurements within sub-directories, within each subdirectory the data are organized into folders for data itself or the various codes use to process the data. With the data folders, things are labeled by specimen ID and/or experiment number, which are traceable via the provided lab notebooks to all other data.",
    "1. The two Excel sheets at the top level are lab notebooks used during dataset creation. They described all the data captured in the dataset and may also include certain notes or comments recorded during data collection to provide added context.",
    "2. The DIC folder contains processed digital image correlation results, i.e., full-field strain maps collected during each test.",
    "3. The Raw_data folder contains ALL raw data collected (warning: it is a lot of data), and generally we recommend looking into this only after understanding the rest of the data provided.",
    "4. The Stress_strain_data folder contains load-displacement data collected during testing, and Matlab scripts used to process this data and generate stress-strain or load-displacement curves (and the single-point values such as Young\u2019s Modulus or Ultimate Tensile Strength from those curves).",
    "5. Finally, the XrayCT folder contains 3D images, and processing scripts for these images, from in-situ X-ray Computed Tomography, including five specimens imaged at several stress levels each (the names of each can be understood with the help of the lab notebooks \u2013 note that Exp numbers are repeated between Summer and Fall collection cycles, so be sure select the correct sheet for the given specimen ID within the Excel books).",
    "This dataset is intended to be understood with the aid of the related article, Kafka et al. 2022"
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