Scale-invariant projection optimization in tomographic volumetric additive manufacturing
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914463224954880 |
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| author | Woo, Seungpyo Lee, Sangyup Taylor, Hayden K. |
| author_facet | Woo, Seungpyo Lee, Sangyup Taylor, Hayden K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08997 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scale-invariant projection optimization in tomographic volumetric additive manufacturing Woo, Seungpyo Lee, Sangyup Taylor, Hayden K. Computational Engineering, Finance, and Science Optimization and Control 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. |
| title | Scale-invariant projection optimization in tomographic volumetric additive manufacturing |
| topic | Computational Engineering, Finance, and Science Optimization and Control |
| url | https://arxiv.org/abs/2604.08997 |