Projection optimization for computed axial lithography
In computed axial lithography, a rotating vial of photoresin is illuminated by a sequence of computed images, and the object forms wherever the accumulated light dose crosses the material’s gelation threshold. Choosing those images is an inverse problem: light can only be added, never subtracted, and the material responds nonlinearly to dose. Straightforward filtered back-projection therefore produces poor parts, and the quality of a print is set as much by the optimization as by the hardware.
We have developed a sequence of optimization methods for this problem. Early work introduced high-fidelity projection generation that accounts for the material response. In collaboration with the University of Colorado and Lawrence Livermore National Laboratory we developed Object Space Model Optimization (OSMO). Most recently, a band-constrained Lp-norm (BCLP) formulation treats the target as a tolerance band rather than a binary image. That generalization handles grayscale responses, spatially varying tolerances and weights, and places earlier algorithms within a common framework. Current work addresses the co-optimization of optical and diffusive blur, and scale-invariant formulations for large prints.
Banner: simulated dose in one slice of a tomographic print as the projections are optimized, from unoptimized projections (left) to 10 and 300 iterations of a band-constrained gradient method (center and right). The cyan contour marks where the dose crosses the gelation threshold; after optimization it follows the target’s fine bars, holes and star closely. Illustration generated for this page.
Key publications
[Pre12] S. Woo, S. Lee, and H. Taylor, “Scale-invariant projection optimization in tomographic volumetric additive manufacturing,” arXiv:2604.08997, Apr. 2026. doi:10.48550/arXiv.2604.08997
[Pre13] J. Ye, A.A. Khan, and H. Taylor, “Co-optimization of diffusive and tomographic blur in computed axial lithography via experimental kernel identification,” arXiv:2606.04174, Jun. 2026. doi:10.48550/arXiv.2606.04174
[J58] C.C. Li, J. Toombs, H. Taylor, and T. Wallin, “Tomographic projection optimization for volumetric additive manufacturing with general band constraint Lp-norm minimization,” Additive Manufacturing, 104447, 2024. doi:10.1016/j.addma.2024.104447
[J41] C.M. Rackson, K.M. Champley, J.T. Toombs, E.J. Fong, V. Bansal, H.K. Taylor, M. Shusteff, and R.R. McLeod, “Object-space optimization of tomographic reconstructions for additive manufacturing,” Additive Manufacturing, vol. 48, part A, 102367, 2021. doi:10.1016/j.addma.2021.102367
[J40] I. Bhattacharya, J. Toombs, and H. Taylor, “High fidelity volumetric additive manufacturing,” Additive Manufacturing, vol. 47, 102299, 2021. doi:10.1016/j.addma.2021.102299