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Correcting ray distortion in tomographic additive manufacturing.

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    Computational correction eliminates hardware needs for tomographic 3D printing distortion. This advance simplifies 3D printing by computationally fixing optical aberrations, enabling more flexible and accessible additive manufacturing.

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    Area of Science:

    • Additive Manufacturing
    • Optical Engineering
    • Computational Imaging

    Background:

    • Light-based additive manufacturing (3D printing) builds objects layer-by-layer via photopolymerization.
    • Tomographic dose patterning creates 3D light dose distributions within a resin vial.
    • Current methods require hardware (index matching baths or lenses) to correct for lensing distortion in cylindrical vials.

    Purpose of the Study:

    • To demonstrate that hardware-based distortion correction is unnecessary in tomographic 3D printing.
    • To introduce a computational method for correcting optical distortions.
    • To enable simpler and more flexible tomographic 3D printer designs.

    Main Methods:

    • Developed a computational approach to correct for lensing distortion by resampling the parallel-beam radon transform into an aberrated geometry.
    • Applied the computational method to correct for non-telecentricity in optical projection systems.

    Main Results:

    • Successfully demonstrated that lensing distortion in tomographic 3D printing can be computationally corrected.
    • Showcased the general applicability of the computational approach for correcting optical projection system aberrations.
    • Eliminated the need for external hardware solutions like index matching baths or cylindrical lenses.

    Conclusions:

    • Computational correction of optical distortions is a viable and effective alternative to hardware-based solutions.
    • This approach simplifies the design and operation of tomographic 3D printers.
    • The findings pave the way for a new generation of flexible and accessible tomographic additive manufacturing systems.