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.

翻译：体层体积增材制造（TVAM）需设计投影图案，以实现目标内部高保真度的同时，抑制目标外部的非预期曝光。本文提出一种尺度不变投影优化框架（SiPO），将投影形状与绝对剂量标度解耦。该方法基于归一化符合度与泄漏度量，将投影设计问题表述为线性分式规划，并通过Charnes-Cooper变换转化为线性规划。针对过程控制，引入两种实用的确定性情形：在严格材料公差下最小化剂量泄漏，以及在硬性抑制约束下最大化目标符合度。采用无矩阵原对偶混合梯度求解器，可支持大规模实现。数值结果表明，该框架在目标保真度与过程分离度之间提供清晰权衡，且在三维模糊感知前向模型下仍保持有效性。