{
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  "ediid": "ark:/88434/mds2-2933",
  "version": "1.0.1",
  "doi": "doi:10.18434/mds2-2933",
  "title": "Additive Manufacturing Benchmark 2022 Schema ",
  "contactPoint": {
    "fn": "Lyle E. Levine",
    "hasEmail": "mailto:lyle.levine@nist.gov"
  },
  "modified": "2023-09-12",
  "status": "available",
  "landingPage": "https://data.nist.gov/od/id/mds2-2933",
  "description": [
    "This resource is the implementation in XML Schema [1] of a data model that describes the Additive Manufacturing Benchmark 2022 series data. \nIt provides a robust set of metadata for the build processes and their resulting specimens and for measurements made on these in the context of the AM Bench 2022 project.",
    "The schema was designed to support typical science questions which users of a database with metadata about the AM Bench results might wish to pose. The metadata include identifiers assigned to build products, derived specimens, and measurements; links to relevant journal publications, documents, and illustrations; provenance of specimens such as source materials and details of the build process; measurement geometry, instruments and other configurations used in measurements; and access information to raw and processed data as well as analysis descriptions of these datasets.",
    "This data model is an abstraction of these metadata, designed using the concepts of inheritance, normalization, and reusability of an object oriented language for ease of extensibility and maintenance. It is simple to incorporate new metadata as needed.",
    "A CDCS [2] database at NIST was filled with metadata provided by the contributors to the AM Bench project. They entered values for the metadata fields for an AM Bench measurement, specimen or build process in tabular spreadsheets. These entries were translated to XML documents compliant with the schema using a set of python scripts. The generated XML documents were loaded into the database with a persistent identifier (PID) assigned by the database.",
    "[1] https://www.w3.org/XML/Schema\n[2] https://www.nist.gov/itl/ssd/information-systems-group/configurable-data-curation-system-cdcs/about-cdcs"
  ],
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    "additive manufacturing",
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    "material composition",
    "laser absorptivity",
    "radiography",
    "mechanical testing"
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      "tag": "Materials: Polymers",
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      "tag": "Build Process: Machine performance",
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      "tag": "Build Process: Process mapping",
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      "tag": "Build Process: Powder bed fusion: Laser",
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      "tag": "Build Process: Vat photopolymerization",
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    {
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      "tag": "Post-build Process: Annealing",
      "@type": "Concept"
    },
    {
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      "tag": "Post-build Process: Curing",
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    },
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      "tag": "Microstructure: In-situ characterization",
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      "tag": "Microstructure: Ex-situ characterization",
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      "tag": "Microstructure: Microstructure analysis",
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      "tag": "Properties: Mechanical",
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      "tag": "Properties: Composition",
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      "tag": "Properties: Residual stress",
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      "tag": "Properties: Rheology",
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      "tag": "Properties: Thermal / kinetic",
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      "tag": "Properties: Surface topography",
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      "tag": "Properties: Surface roughness",
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      "tag": "Performance: Fracture",
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      "tag": "Performance: Geometric accuracy",
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      "tag": "Characterization Approach: In-situ: Build process",
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      "tag": "Characterization Approach: Microscopy: Optical",
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      "tag": "Characterization Approach: Small-angle scattering",
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      "tag": "Characterization Approach: Mechanical Testing",
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      "tag": "Characterization Approach: Serial sectioning",
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      "tag": "Characterization Approach: Dimensional metrology",
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      "tag": "Characterization Approach: Contour method",
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      "tag": "Characterization Approach: Profilometry",
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      "tag": "Characterization Approach: Rheometry",
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      "tag": "Characterization Approach: Machine vision",
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      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
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      "title": "Asynchronous AM Bench 2022 Challenge Data: Real-time, simultaneous absorptance and high-speed Xray imaging",
      "issued": "2022-01-10",
      "citation": "Simonds, B. J., Tanner, J., Artusio-Glimpse, A., Williams, P. A., Parab, N., Zhao, C., &amp; Sun, T. (2022). Asynchronous AM Bench 2022 Challenge Data: Real-time, simultaneous absorptance and high-speed Xray imaging (Version 1.3.1) [Data set]. National Institute of Standards and Technology. https://doi.org/10.18434/MDS2-2525"
    },
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      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
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      "title": "AM Bench 2022 challenge problem Subcontinuum Mesoscale Tensile Test (CHAL-AMB2022-04-MeTT)",
      "issued": "2022",
      "citation": "Benzing, J., Kafka, O., Moser, N., Weaver, J., Liew, L.-A., &amp; Hrabe, N. (2022). AM Bench 2022 challenge problem Subcontinuum Mesoscale Tensile Test (CHAL-AMB2022-04-MeTT) (Version 1.4.1) [Data set]. National Institute of Standards and Technology. https://doi.org/10.18434/MDS2-2587"
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      "_extensionSchemas": [
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      "title": "AM Bench 2022 challenge Macroscale Tensile Tests at Different Orientations (CHAL-AMB2022-04-MaTTO)",
      "issued": "2022-04-11",
      "citation": "Benzing, J., Moser, N., Kafka, O., Weaver, J., Derimow, N., &amp; Hrabe, N. (2022). AM Bench 2022 challenge Macroscale Tensile Tests at Different Orientations (CHAL-AMB2022-04-MaTTO) (Version 1.1.0) [Data set]. National Institute of Standards and Technology. https://doi.org/10.18434/MDS2-2588"
    },
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      "title": "AM Bench 2022 3D Build Modeling Challenge Description Data (AMB2022-01)",
      "issued": "2022",
      "citation": "Lane, B., Levine, L., Deisenroth, D., Yeung, H., Tondare, V., Mekhontsev, S., &amp; Neira, J. (2022). AM Bench 2022 3D Build Modeling Challenge Description Data (AMB2022-01) (Version 1.0.0) [Data set]. National Institute of Standards and Technology. https://doi.org/10.18434/MDS2-2607"
    },
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