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Deconvolution volumetric additive manufacturing
Volumetric additive manufacturing techniques are a promising pathway to ultra-rapid light-based 3D fabrication. Their widespread adoption, however, demands significant improvement in print fidelity. Currently, volumetric additive manufacturing prints suffer from systematic undercuring of fine featur...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362001/ https://www.ncbi.nlm.nih.gov/pubmed/37479831 http://dx.doi.org/10.1038/s41467-023-39886-4 |
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author | Orth, Antony Webber, Daniel Zhang, Yujie Sampson, Kathleen L. de Haan, Hendrick W. Lacelle, Thomas Lam, Rene Solis, Daphene Dayanandan, Shyamaleeswari Waddell, Taylor Lewis, Tasha Taylor, Hayden K. Boisvert, Jonathan Paquet, Chantal |
author_facet | Orth, Antony Webber, Daniel Zhang, Yujie Sampson, Kathleen L. de Haan, Hendrick W. Lacelle, Thomas Lam, Rene Solis, Daphene Dayanandan, Shyamaleeswari Waddell, Taylor Lewis, Tasha Taylor, Hayden K. Boisvert, Jonathan Paquet, Chantal |
author_sort | Orth, Antony |
collection | PubMed |
description | Volumetric additive manufacturing techniques are a promising pathway to ultra-rapid light-based 3D fabrication. Their widespread adoption, however, demands significant improvement in print fidelity. Currently, volumetric additive manufacturing prints suffer from systematic undercuring of fine features, making it impossible to print objects containing a wide range of feature sizes, precluding effective adoption in many applications. Here, we uncover the reason for this limitation: light dose spread in the resin due to chemical diffusion and optical blurring, which becomes significant for features ⪅0.5 mm. We develop a model that quantitatively predicts the variation of print time with feature size and demonstrate a deconvolution method to correct for this error. This enables prints previously beyond the capabilities of volumetric additive manufacturing, such as a complex gyroid structure with variable thickness and a fine-toothed gear. These results position volumetric additive manufacturing as a mature 3D printing method, all but eliminating the gap to industry-standard print fidelity. |
format | Online Article Text |
id | pubmed-10362001 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103620012023-07-23 Deconvolution volumetric additive manufacturing Orth, Antony Webber, Daniel Zhang, Yujie Sampson, Kathleen L. de Haan, Hendrick W. Lacelle, Thomas Lam, Rene Solis, Daphene Dayanandan, Shyamaleeswari Waddell, Taylor Lewis, Tasha Taylor, Hayden K. Boisvert, Jonathan Paquet, Chantal Nat Commun Article Volumetric additive manufacturing techniques are a promising pathway to ultra-rapid light-based 3D fabrication. Their widespread adoption, however, demands significant improvement in print fidelity. Currently, volumetric additive manufacturing prints suffer from systematic undercuring of fine features, making it impossible to print objects containing a wide range of feature sizes, precluding effective adoption in many applications. Here, we uncover the reason for this limitation: light dose spread in the resin due to chemical diffusion and optical blurring, which becomes significant for features ⪅0.5 mm. We develop a model that quantitatively predicts the variation of print time with feature size and demonstrate a deconvolution method to correct for this error. This enables prints previously beyond the capabilities of volumetric additive manufacturing, such as a complex gyroid structure with variable thickness and a fine-toothed gear. These results position volumetric additive manufacturing as a mature 3D printing method, all but eliminating the gap to industry-standard print fidelity. Nature Publishing Group UK 2023-07-21 /pmc/articles/PMC10362001/ /pubmed/37479831 http://dx.doi.org/10.1038/s41467-023-39886-4 Text en © Crown 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Orth, Antony Webber, Daniel Zhang, Yujie Sampson, Kathleen L. de Haan, Hendrick W. Lacelle, Thomas Lam, Rene Solis, Daphene Dayanandan, Shyamaleeswari Waddell, Taylor Lewis, Tasha Taylor, Hayden K. Boisvert, Jonathan Paquet, Chantal Deconvolution volumetric additive manufacturing |
title | Deconvolution volumetric additive manufacturing |
title_full | Deconvolution volumetric additive manufacturing |
title_fullStr | Deconvolution volumetric additive manufacturing |
title_full_unstemmed | Deconvolution volumetric additive manufacturing |
title_short | Deconvolution volumetric additive manufacturing |
title_sort | deconvolution volumetric additive manufacturing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362001/ https://www.ncbi.nlm.nih.gov/pubmed/37479831 http://dx.doi.org/10.1038/s41467-023-39886-4 |
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