Voronoi Diagram-Based Volume Decomposition and Overhang Control in Topology Optimization for Multi-Axis Additive Manufacturing

Why It Matters

It provides a practical pathway for manufacturers to reduce support material and post‑processing costs while exploiting the flexibility of multi‑axis AM, accelerating the adoption of advanced design‑for‑manufacturing strategies.

Key Takeaways

• Voronoi‑based decomposition ensures full coverage for arbitrary geometries
• KS‑based distance constraint prevents diffuse partition interfaces
• Global overhang constraints are applied per partition using density gradients
• Simultaneous optimization aligns topology, partitions, and build orientations
• Numerical tests show reduced support structures and feasible designs

Pulse Analysis

Multi‑axis additive manufacturing (AM) promises unprecedented design freedom by allowing dynamic build orientation changes during fabrication. However, realizing this potential requires sophisticated design tools that can anticipate and mitigate overhang issues, which traditionally demand extensive support structures. The newly proposed Voronoi diagram‑based volume decomposition addresses this gap by partitioning the design domain into manageable regions using a minimal set of seed points. This implicit method, powered by Softmax and Heaviside projection, guarantees complete domain coverage and stable partitions regardless of geometric complexity, laying a solid foundation for downstream optimization.

The core innovation lies in integrating overhang control directly into the topology optimization loop. By coupling the spatial gradient of the material density field with partition‑specific overhang constraints, the algorithm can adjust each region’s build orientation in concert with its structural layout. A Kreisselmeier–Steinhauser aggregation constraint further refines seed point placement, eliminating unrealistic diffuse interfaces between partitions. The result is a self‑supported design that minimizes the need for additional support material, thereby cutting material waste, post‑processing time, and overall production costs—critical factors for competitive AM operations.

Extensive 2D and 3D case studies validate the method’s robustness. Sensitivity analyses reveal that the number of partitions and initial orientation guesses influence final geometry, yet the approach consistently yields feasible, manufacturable parts with controlled overhangs. For industry stakeholders, this translates into faster design cycles, lower inventory of support consumables, and a clearer path to scaling multi‑axis AM for high‑value components in aerospace, automotive, and medical sectors. The technique bridges the gap between theoretical optimization and practical, cost‑effective manufacturing.