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Deconvolution volumetric additive manufacturing

Author

Listed:
  • Antony Orth

    (National Research Council of Canada)

  • Daniel Webber

    (National Research Council of Canada)

  • Yujie Zhang

    (National Research Council of Canada)

  • Kathleen L. Sampson

    (National Research Council of Canada)

  • Hendrick W. Haan

    (Ontario Tech University)

  • Thomas Lacelle

    (National Research Council of Canada)

  • Rene Lam

    (National Research Council of Canada)

  • Daphene Solis

    (National Research Council of Canada)

  • Shyamaleeswari Dayanandan

    (National Research Council of Canada)

  • Taylor Waddell

    (University of California Berkeley)

  • Tasha Lewis

    (University of California Berkeley)

  • Hayden K. Taylor

    (University of California Berkeley)

  • Jonathan Boisvert

    (National Research Council of Canada)

  • Chantal Paquet

    (National Research Council of Canada)

Abstract

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.

Suggested Citation

  • Antony Orth & Daniel Webber & Yujie Zhang & Kathleen L. Sampson & Hendrick W. Haan & Thomas Lacelle & Rene Lam & Daphene Solis & Shyamaleeswari Dayanandan & Taylor Waddell & Tasha Lewis & Hayden K. Ta, 2023. "Deconvolution volumetric additive manufacturing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39886-4
    DOI: 10.1038/s41467-023-39886-4
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    References listed on IDEAS

    as
    1. Jose L. Sanchez Noriega & Nicholas A. Chartrand & Jonard Corpuz Valdoz & Collin G. Cribbs & Dallin A. Jacobs & Daniel Poulson & Matthew S. Viglione & Adam T. Woolley & Pam M. Ry & Kenneth A. Christens, 2021. "Spatially and optically tailored 3D printing for highly miniaturized and integrated microfluidics," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Martin Regehly & Yves Garmshausen & Marcus Reuter & Niklas F. König & Eric Israel & Damien P. Kelly & Chun-Yu Chou & Klaas Koch & Baraa Asfari & Stefan Hecht, 2020. "Xolography for linear volumetric 3D printing," Nature, Nature, vol. 588(7839), pages 620-624, December.
    3. Bin Wang & Einstom Engay & Peter R. Stubbe & Saeed Z. Moghaddam & Esben Thormann & Kristoffer Almdal & Aminul Islam & Yi Yang, 2022. "Stiffness control in dual color tomographic volumetric 3D printing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Damien Loterie & Paul Delrot & Christophe Moser, 2020. "High-resolution tomographic volumetric additive manufacturing," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
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