Scale-invariant projection optimization in tomographic volumetric additive manufacturing
2026-04-10 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors developed a new method to improve 3D printing that uses light patterns to create detailed shapes while avoiding unwanted exposure outside the target area. Their approach, called SiPO, separates the shape of the light projection from how intense it is, making it easier to control. They turn this problem into a special type of math optimization that finds the best balance between accurately creating the shape and preventing extra light exposure. The method can handle large-scale problems efficiently and works well even when accounting for blur effects in 3D printing.
Tomographic volumetric additive manufacturingProjection optimizationLinear-fractional programmingCharnes-Cooper transformationNormalized conformityDose spillagePrimal-dual hybrid gradient solver3D blurProcess control
Authors
Seungpyo Woo, Sangyup Lee, Hayden K. Taylor
Abstract
Tomographic volumetric additive manufacturing (TVAM) requires projection patterns that achieve high in-part fidelity while suppressing unintended exposure outside the target. We present a scale-invariant projection optimization framework (SiPO) that decouples projection shape from absolute dose scaling. The method formulates projection design as a linear-fractional program based on normalized conformity and spillage metrics, which is converted into a linear program via the Charnes-Cooper transformation. Two practical deterministic cases are introduced for process control: minimizing dose spillage under strict material tolerances and maximizing target conformity under hard inhibition constraints. A matrix-free primal-dual hybrid gradient solver enables large-scale implementation. Numerical results demonstrate that the framework provides a clear trade-off between target fidelity and process separation and remains effective under 3D blur-aware forward models.