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

TL;DR

A novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications using swarming dynamics to simulate the motion of virtual agents along the stress field of a part under loading conditions is presented.

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.

Figures (9)

• Figure 1: Notation of the agents locations and distance vectors utilized in the definition of the environment potential $P_e$ and of the aggregation potential $P_a$
• Figure 2: Initial swarm of equally spaced agents $x_{1,\ldots,N}$ and boundary agents $x_0$ and $x_{N+1}$. The displacement of the boundary agents is one dimensional, as they are constrained to follow the part boundary.
• Figure 3: Internal boundary agents $x_{i+2}$ and $x_{i+3}$ added to the swarm when encountering a hole in the part
• Figure 4: Representation of the constraints used in the optimization problem \ref{['eq:opt_problem']} to limit the displacement of the agents around their ideal location $t_i(k+1)$. The two constraints form the box depicted in magenta, which constrains $x_1(k+1)$.
• Figure 5: Open-hole tensile specimen according to american2023standard. The dimensions are $p = 36mm$ and $q = 150mm$, and the thickness is 2mm. The hole has a diameter of 6mm. The specimen is evaluated in a tensile strength test, with tension applied at the two narrow extremities.