Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing

In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to simulate the motion of virtual agents along the stress field of a part under loading conditions. The resulting agent trajectories are then used as print trajectories. With this approach, the complex global trajectory generation problem is subdivided into a set of sequential and computationally efficient quadratic programs. Through comprehensive evaluations in both simulation and experiments, we compare our method to state-of-the-art approaches. Our results demonstrate a remarkable improvement in computational efficiency, achieving a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$115\times $ </tex-math></inline-formula> faster computation speed than existing methods. This efficiency, combined with the possibility to tune the trajectory spacing to match the deposition process constraints, makes the potential integration of our approach into existing 3D printing processes seamless. Additionally, the open-hole tensile specimen produced on a conventional fused filament fabrication setup with our algorithm achieves a notable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 10\%$ </tex-math></inline-formula> improvement in specific modulus compared to existing trajectory optimization methods.