A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks

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 × 8 pixel (i.e. 96 000 pixel interactions) training pairs are provided. Jupyter notebooks for various steps in process are also provided.

Data and Resources

Field Value
accessLevel public
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identifier ark:/88434/mds2-2950
issued 2023-07-20
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 National Institute of Standards and Technology
references {https://doi.org/10.1002/smll.202301987}
resource-type Dataset
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theme {"Mathematics and Statistics:Statistical analysis",Materials:Polymers,"Manufacturing:Additive manufacturing"}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • 3d-printing
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • additive-manufacturing
  • generative-adversarial-network
  • machine-learning
  • photopolymer
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Jason Killgore
maintainer_email jason.killgore@nist.gov
metadata_created 2025-09-24T00:08:02.384317
metadata_modified 2025-09-24T00:08:02.384326
notes 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 × 8 pixel (i.e. 96 000 pixel interactions) training pairs are provided. Jupyter notebooks for various steps in process are also provided.
num_resources 6
num_tags 13
title A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks