{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["006:55"], "contactPoint": {"fn": "Jason Killgore", "hasEmail": "mailto:jason.killgore@nist.gov"}, "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.", "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"}], "identifier": "ark:/88434/mds2-2950", "issued": "2023-07-20", "keyword": ["3D Printing", "Additive Manufacturing", "Machine Learning", "Generative Adversarial Network", "Photopolymer"], "landingPage": "https://data.nist.gov/od/id/mds2-2950", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2023-03-07 00:00:00", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "references": ["https://doi.org/10.1002/smll.202301987"], "theme": ["Mathematics and Statistics:Statistical analysis", "Materials:Polymers", "Manufacturing:Additive manufacturing"], "title": "A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks"}