{
    "identifier": "ark:\/88434\/mds2-2950",
    "accessLevel": "public",
    "references": [
        "https:\/\/doi.org\/10.1002\/smll.202301987"
    ],
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Jason Killgore"
    },
    "programCode": [
        "006:045"
    ],
    "@type": "dcat:Dataset",
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2950",
    "description": "Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. Subsequent layers are printed on to preceding layers to eventually form a desired 3 dimensional (3D) part. This data set characterizes the 3D geometry of a single layer of voxels (volume pixels) printed with photomasks assigned random intensity levels at every pixel. The masks are computer generated, then printed onto a glass cover slide. Geometry of the printed voxels is characterized by laser scanning confocal microscopy. The data were originally curated to train image-to-image U-net machine learning models to predict voxel scale geometry given arbitrary photomasks, as described in the publication \"A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks\". Data are provided in a raw (native microscope format and photomask image) and processed into aligned mask-print training pairs. A total of 1500 8 pixel \u00d7 8 pixel (i.e. 96 000 pixel interactions) training pairs are provided. Jupyter notebooks for various steps in process are also provided.",
    "language": [
        "en"
    ],
    "title": "A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks",
    "distribution": [
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2950\/Jupyter_notebooks.zip",
            "mediaType": "application\/zip",
            "title": "Jupyter_notebooks"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2950\/2950_README.txt",
            "mediaType": "text\/plain",
            "title": "2950_README"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2950\/raw_print_data.zip",
            "mediaType": "application\/zip",
            "title": "raw_print_data"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2950\/photomasks.zip",
            "mediaType": "application\/zip",
            "title": "photomasks"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2950\/modified_pix2pix.zip",
            "mediaType": "application\/zip",
            "title": "modified_pix2pix"
        },
        {
            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2950\/training_pairs.zip",
            "mediaType": "application\/zip",
            "title": "training_pairs"
        }
    ],
    "license": "https:\/\/www.nist.gov\/open\/license",
    "bureauCode": [
        "006:55"
    ],
    "modified": "2023-03-07 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "accrualPeriodicity": "irregular",
    "theme": [
        "Mathematics and Statistics:Statistical analysis",
        "Materials:Polymers",
        "Manufacturing:Additive manufacturing"
    ],
    "issued": "2023-07-20",
    "keyword": [
        "3D Printing",
        "Additive Manufacturing",
        "Machine Learning",
        "Generative Adversarial Network",
        "Photopolymer"
    ]
}