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

TL;DR

This paper introduces a swarm-based method for generating stress-aligned 3D printing trajectories that significantly improves computational efficiency and material properties compared to existing methods.

Contribution

The paper presents a novel swarm-based approach that efficiently generates stress-aligned trajectories for 3D printing, outperforming current methods in speed and material performance.

Findings

115x faster computation than existing methods

~10% improvement in specific modulus of printed specimens

Seamless integration potential with current 3D printing processes

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% improvement in specific modulus compared to existing trajectory optimization methods.