SFCDecomp: Multicriteria Optimized Tool Path Planning in 3D Printing using Space-Filling Curve Based Domain Decomposition

TL;DR

This paper introduces SFCDecomp, a space-filling curve based framework for efficient multicriteria optimization of 3D printing toolpaths, addressing NP-hard problems and demonstrating improved mechanical properties in printed models.

Contribution

The paper presents a novel space-filling curve based decomposition method for large-scale 3D printing optimization and a multicriteria approach for toolpath planning.

Findings

Successfully optimized toolpaths for large models with hundreds of thousands of nodes.

Maximized or minimized layer edge overlaps significantly affected tensile strength.

Framework effectively solves NP-hard 3D printing optimization problems.

Abstract

We explore efficient optimization of toolpaths based on multiple criteria for large instances of 3D printing problems. We first show that the minimum turn cost 3D printing problem is NP-hard, even when the region is a simple polygon. We develop SFCDecomp, a space filling curve based decomposition framework to solve large instances of 3D printing problems efficiently by solving these optimization subproblems independently. For the Buddha model, our framework builds toolpaths over a total of 799,716 nodes across 169 layers, and for the Bunny model it builds toolpaths over 812,733 nodes across 360 layers. Building on SFCDecomp, we develop a multicriteria optimization approach for toolpath planning. We demonstrate the utility of our framework by maximizing or minimizing tool path edge overlap between adjacent layers, while jointly minimizing turn costs. Strength testing of a tensile test specimen printed with tool paths that maximize or minimize adjacent layer edge overlaps reveal significant differences in tensile strength between the two classes of prints.