Scale-invariant projection optimization in tomographic volumetric additive manufacturing

TL;DR

This paper introduces SiPO, a scale-invariant projection optimization framework for TVAM that improves target fidelity and process separation by decoupling projection shape from dose scaling.

Contribution

It formulates projection design as a linear-fractional program, enabling large-scale, efficient optimization with practical control strategies.

Findings

SiPO achieves a clear trade-off between target fidelity and process separation.

The framework remains effective under 3D blur-aware models.

Numerical results validate the effectiveness of the optimization approach.

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.