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  "title": "A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks ",
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    "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."
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      "title": "A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-Nets and Generative Adversarial Networks",
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      "citation": "Killgore, J. P., Kolibaba, T. J., Caplins, B. W., Higgins, C. I., & Rezac, J. D. (2023). A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-Nets and Generative Adversarial Networks. Small. Portico. https://doi.org/10.1002/smll.202301987"
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