Density-based topology optimization methods such as SIMP enable efficient topological exploration but produce diffuse material boundaries that require interpretation before manufacturing. Level-set methods maintain sharp interfaces but are sensitive to the initial design. This paper presents a sequential framework that addresses these complementary limitations through a signed distance function (SDF)-based geometry transfer, formulated for three-dimensional meshes. The SIMP density distribution is converted into an SDF that initializes subsequent level-set boundary refinement. From the level-set perspective, the SIMP-derived initialization mitigates sensitivity to the initial design. From the SIMP perspective, the level-set stage acts as optimization-driven post-processing that produces manufacturing-ready boundaries. Validation on three-dimensional cantilever and MBB benchmarks demonstrates compliance comparable to standalone level-set optimization, with up to 4.6x wall-clock speedup on the cantilever case. The full implementation is released under an open-source license to support reproducibility.

翻译：基于密度的拓扑优化方法（如SIMP）能够实现高效的拓扑探索，但会产生模糊的材料边界，在制造前需要解释。水平集方法保持清晰的界面，但对初始设计敏感。本文提出了一种顺序框架，通过基于符号距离函数（SDF）的几何传递来解决这些互补限制，该框架针对三维网格进行公式化。SIMP密度分布被转换为SDF，用于初始化后续的水平集边界细化。从水平集的角度来看，SIMP导出的初始化缓解了对初始设计的敏感性。从SIMP的角度来看，水平集阶段充当优化驱动的后处理，产生可制造的边界。在三维悬臂梁和MBB基准上的验证表明，其合规性与独立水平集优化相当，在悬臂梁案例中实现了高达4.6倍的时钟加速。完整的实现已在开源许可下发布，以支持可重复性。