Object-space optimization of tomographic reconstructions for additive manufacturing

Journal Article · 03 October 2021 · Additive Manufacturing

DOI: https://doi.org/10.1016/j.addma.2021.102367 · OSTI ID:1886128

Rackson, Charles M. ^[1]; Champley, Kyle M. ^[2]; Toombs, Joseph T. ^[3]; Fong, Erika J. ^[2]; Bansal, Vishal ^[3]; Taylor, Hayden K. ^[3]; Shusteff, Maxim ^[2]; McLeod, Robert R. ^[1]

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1. Univ. of Colorado, Boulder, CO (United States)
2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
3. Univ. of California, Berkeley, CA (United States)

Volumetric 3D printing motivated by computed axial lithography enables rapid printing of homogeneous parts but requires a high dimensionality gradient-descent optimization to calculate image sets. Here we introduce a new, simpler approach to image-computation that algebraically optimizes a model of the printed object, significantly improving print accuracy of complex parts under imperfect material and optical precision by improving optical dose contrast between the target and surrounding regions. Quality metrics for volumetric printing are defined and shown to be significantly improved by the new algorithm. The approach is extended beyond binary printing to grayscale control of conversion to enable functionally graded materials. The flexibility of the technique is digitally demonstrated with realistic projector point spread functions, printing around occluding structures, printing with restricted angular range, and incorporation of materials chemistry such as inhibition. Finally, simulations show that the method facilitates new printing modalities such as printing into flat, rather than cylindrical packages to extend the applications of volumetric printing.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1886128
• Report Number(s): LLNL-JRNL-820693; 1032010
• Journal Information: Additive Manufacturing, Journal Name: Additive Manufacturing Journal Issue: N/A Vol. 48; ISSN 2214-8604
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English