通过实验识别核函数，联合优化光散射与断层重建模糊，提升轴向光刻精度。
Co-optimization of Diffusive and Tomographic Blur in Computed Axial Lithography via Experimental Kernel Identification
- 从标准打印件中提取扩散核函数，统一建模光散射效应。
- 联合优化光散射与断层重建模糊，显著提升复杂结构还原精度。
- 适合做高精度三维光固化制造的研究者与工程师参考。
计算轴向光刻是一种体素化增材制造方法，通过光图案的三维叠加选择性固化光敏树脂，相较于逐层工艺具有打印速度快、层间伪影少、兼容高粘度材料等优势。然而，自由基淬灭剂（如氧气）引起的扩散效应会模糊已固化与未固化区域的边界，限制分辨率并难以再现锐利的高频特征。通过将微CT数据与卷积不同扩散核的计算剂量模型对比，我们建立了一种从任意标准未校正打印件中提取单一扩散核的框架，以解释所有观测到的与目标的偏差。本文通过联合优化扩散模糊与计算断层成像固有模糊，实现了对前驱去卷积方法的性能超越，显著提升了形貌保真度。
原文摘要 · Abstract (English)
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.
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