New technique co-optimizes blur in volumetric 3D printing for sharper prints
Researchers fix diffusion and tomographic blur simultaneously using a single kernel extraction method.
Computed Axial Lithography (CAL) is a volumetric additive manufacturing technique that cures photosensitive resin using 3D light projections, offering faster print times and smoother surfaces than layer-by-layer methods. However, diffusive blur from free-radical quenchers (like oxygen) limits resolution by smearing the boundary between cured and uncured regions. Previous attempts to correct this blur relied on deconvolution pre-compensation, but they struggled to account for both diffusive and inherent tomographic reconstruction blur together.
The team now presents a framework that extracts a single experimental diffusion kernel from any standard uncorrected CAL print by aligning micro-CT scans with computational dose models. They then co-optimize for both diffusive and tomographic blur, yielding better fidelity than prior deconvolution approaches. This method is compatible with existing CAL hardware and requires no additional calibration steps — simply using a single test print to derive the kernel. The result is volumetric prints with sharper edges and finer spatial detail, pushing the resolution limits of high-viscosity resin 3D printing.
- Framework extracts a single diffusion kernel from micro-CT data of any standard uncorrected print.
- Co-optimizes diffusive blur (from oxygen quenchers) and tomographic reconstruction blur simultaneously.
- Achieves higher fidelity than previous deconvolution-only pre-compensation for high-spatial-frequency features.
Why It Matters
Sharper, faster volumetric 3D prints for high-resolution prototypes, medical devices, and microfluidics without hardware changes.