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.

翻译：本研究提出了一种新颖的基于群体的方法，用于生成针对三维打印应用优化的应力对齐轨迹。该方法利用群体动力学模拟虚拟智能体沿负载部件内部应力方向的运动，并将智能体的轨迹作为打印轨迹。通过这一方法，复杂的全局轨迹生成问题被分解为一系列有序且计算高效的二次规划问题。通过仿真与实验的综合评估，我们将所提出的方法与现有最优方法进行了比较。结果显示，计算效率显著提升，相比现有方法实现了115倍的加速。这种效率优势，结合可根据沉积工艺约束调整轨迹间距的能力，使得我们的方法能够无缝集成到现有三维打印流程中。此外，在常规熔融沉积成型设备上采用本算法制造的开口拉伸试样，相比现有轨迹优化方法，其比模量实现了约10%的显著提升。