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 traj...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838569/ |
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| author | Xavier Guidetti Efe C. Balta and John Lygeros |
| author_facet | Xavier Guidetti Efe C. Balta and John Lygeros |
| author_sort | Xavier Guidetti |
| collection | DOAJ |
| description | 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> <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> <tex-math notation="LaTeX">$\approx 10\%$ </tex-math></inline-formula> improvement in specific modulus compared to existing trajectory optimization methods. |
| format | Article |
| id | doaj-art-401e3e0718d64c5d97a4b7d9fef177fe |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-401e3e0718d64c5d97a4b7d9fef177fe2025-01-21T00:01:18ZengIEEEIEEE Access2169-35362025-01-0113107351074510.1109/ACCESS.2025.352853110838569Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D PrintingXavier Guidetti0https://orcid.org/0000-0002-5639-1093Efe C. Balta1https://orcid.org/0000-0001-8596-8739and John Lygeros2https://orcid.org/0000-0002-6159-1962Automatic Control Laboratory, ETH Zürich, Zürich, SwitzerlandAutomatic Control Laboratory, ETH Zürich, Zürich, SwitzerlandAutomatic Control Laboratory, ETH Zürich, Zürich, SwitzerlandIn 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> <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> <tex-math notation="LaTeX">$\approx 10\%$ </tex-math></inline-formula> improvement in specific modulus compared to existing trajectory optimization methods.https://ieeexplore.ieee.org/document/10838569/Additive manufacturingfused filament fabricationstress-aligned printingswarmingtrajectory optimization |
| spellingShingle | Xavier Guidetti Efe C. Balta and John Lygeros Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing IEEE Access Additive manufacturing fused filament fabrication stress-aligned printing swarming trajectory optimization |
| title | Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing |
| title_full | Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing |
| title_fullStr | Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing |
| title_full_unstemmed | Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing |
| title_short | Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing |
| title_sort | swarm based trajectory generation and optimization for stress aligned 3d printing |
| topic | Additive manufacturing fused filament fabrication stress-aligned printing swarming trajectory optimization |
| url | https://ieeexplore.ieee.org/document/10838569/ |
| work_keys_str_mv | AT xavierguidetti swarmbasedtrajectorygenerationandoptimizationforstressaligned3dprinting AT efecbalta swarmbasedtrajectorygenerationandoptimizationforstressaligned3dprinting AT andjohnlygeros swarmbasedtrajectorygenerationandoptimizationforstressaligned3dprinting |