{"ID":3053222,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T19:35:40.366641076Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04174","arxiv_id":"2606.04174","title":"Co-optimization of Diffusive and Tomographic Blur in Computed Axial Lithography via Experimental Kernel Identification","abstract":"Computed Axial Lithography is a volumetric additive manufacturing method that selectively cures photosensitive resin through the 3D superposition of patterns of light, offering advantages over layer-based processes including rapid print times, reduced layer artifacts, and compatibility with high-viscosity materials. However, diffusive effects, primarily those of free-radical quenchers such as oxygen, blur the boundary between cured and uncured regions, limiting resolution and preventing the reproduction of sharp, high-spatial-frequency features. By comparing micro-CT data to computational dose models convolved with kernels across a range of diffusivities, we establish a framework for extracting a single diffusion kernel from any standard uncorrected print to account for all observed deviations from the target. In this work, we correct diffusion-induced blurring by co-optimizing for its effects alongside the inherent blur of the computed tomography reconstruction, demonstrating improved fidelity over previous approaches of pre-compensating the target geometry via deconvolution.","short_abstract":"Computed Axial Lithography is a volumetric additive manufacturing method that selectively cures photosensitive resin through the 3D superposition of patterns of light, offering advantages over layer-based processes including rapid print times, reduced layer artifacts, and compatibility with high-viscosity materials. Ho...","url_abs":"https://arxiv.org/abs/2606.04174","url_pdf":"https://arxiv.org/pdf/2606.04174v1","authors":"[\"Jennings Z. Ye\",\"Abrar Amin Khan\",\"Hayden K. Taylor\"]","published":"2026-06-02T19:38:31Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"physics.app-ph\"]","methods":"[\"Diffusion Model\"]","has_code":false}