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.
Resources
6 resources available
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2950_README
TEXT/PLAIN -
Jupyter_notebooks
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modified_pix2pix
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photomasks
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raw_print_data
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training_pairs
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Complete Metadata
| @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 × 8 pixel (i.e. 96 000 pixel interactions) training pairs are provided. Jupyter notebooks for various steps in process are also provided. |
| distribution |
[ { "title": "2950_README", "mediaType": "text/plain", "downloadURL": "https://data.nist.gov/od/ds/mds2-2950/2950_README.txt" }, { "title": "Jupyter_notebooks", "mediaType": "application/zip", "downloadURL": "https://data.nist.gov/od/ds/mds2-2950/Jupyter_notebooks.zip" }, { "title": "modified_pix2pix", "mediaType": "application/zip", "downloadURL": "https://data.nist.gov/od/ds/mds2-2950/modified_pix2pix.zip" }, { "title": "photomasks", "mediaType": "application/zip", "downloadURL": "https://data.nist.gov/od/ds/mds2-2950/photomasks.zip" }, { "title": "raw_print_data", "mediaType": "application/zip", "downloadURL": "https://data.nist.gov/od/ds/mds2-2950/raw_print_data.zip" }, { "title": "training_pairs", "mediaType": "application/zip", "downloadURL": "https://data.nist.gov/od/ds/mds2-2950/training_pairs.zip" } ] |
| identifier | ark:/88434/mds2-2950 |
| issued | 2023-07-20 |
| keyword |
[ "3D Printing", "Additive Manufacturing", "Generative Adversarial Network", "Machine Learning", "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 |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| references |
[ "https://doi.org/10.1002/smll.202301987" ] |
| theme |
[ "Manufacturing:Additive manufacturing", "Materials:Polymers", "Mathematics and Statistics:Statistical analysis" ] |
| title | A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks |
