Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing
CoRR(2024)
Abstract
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 produced in a loaded part. 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 with state-of-the-art approaches. Our
results highlight a remarkable improvement in computational efficiency,
achieving a 115x faster computation speed than existing methods. This
efficiency, combined with the possibility to tune the trajectories 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 set-up with our algorithm achieve a notable 10
specific modulus compared to existing trajectory optimization methods.
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