Nesting of 3D irregular shaped objects applied to powder-based additive manufacturing

This paper proposes a 3D nesting algorithm which combines the flower pollination algorithm (FPA) with the oriented bounding box (OBB) collision detection to solve the 3D packing of irregular shaped objects for powder-based additive manufacturing. In the powder-based 3D printing process, stacking multiple models most compactly in the build volume is an important task because no support structure is needed. Given a fixed printing area, the post-nesting build height directly impacts the printing cost and efficiency. To reduce the printing cost, 3D models must be packed as closely as possible and the build height must be minimized. This has been found to be a combinatorial optimization and an NP-hard problem. We propose to use the most recent and effective meta-heuristic optimization algorithm FPA, combined with the collision detection of printed objects set up as the optimization constraint using the OBB tree, to find the near-optimal 3D nesting solution. In FPA optimization, a significant amount of time is spent on collision detection. This paper uses the safety clearance distance to adaptively reduce the OBB tree subdivision, hence significantly reducing the computation of collision detection. As a result, the proposed method finds the model positions and rotations of the global solution, and generates the near-optimal solution in reasonable time. Finally, our method is compared with state-of-the-art commercial softwares. The results show that the proposed method produces lower build height with better efficiency. For real-world complex engine parts (30 different STL models and a total of 192,018 triangles), the computation time is only 160 s, and the build height is 12.4% and 13% better than the results from Netfabb and Magics, respectively.

Automated summary

This paper proposes a 3D nesting algorithm that combines FPA and OBB collision detection for packing irregular shaped objects in powder-based additive manufacturing. The method finds near-optimal solutions by reducing the computation time of collision detection.