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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.16470 |
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| _version_ | 1866916175081897984 |
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| author | Guidetti, Xavier Mukne, Ankita Rueppel, Marvin Nagel, Yannick Balta, Efe C. Lygeros, John |
| author_facet | Guidetti, Xavier Mukne, Ankita Rueppel, Marvin Nagel, Yannick Balta, Efe C. Lygeros, John |
| contents | The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods improve the accuracy and repeatability of 3D prints. However, optimal tuning of controllers for given process parameters and design geometry is often a challenge with manually tuned controllers resulting in inconsistent and suboptimal results. This work employs Bayesian optimization to identify the optimal controller parameters. Additionally, we explore transfer learning in the context of 3D printing by leveraging prior information from past trials. By integrating optimized extrusion force control and transfer learning, we provide a novel framework for closed-loop 3D printing and propose an automated calibration routine that produces high-quality prints for a desired combination of print settings, material, and shape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Extrusion Force Control Tuning for 3D Printing Guidetti, Xavier Mukne, Ankita Rueppel, Marvin Nagel, Yannick Balta, Efe C. Lygeros, John Optimization and Control Systems and Control The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods improve the accuracy and repeatability of 3D prints. However, optimal tuning of controllers for given process parameters and design geometry is often a challenge with manually tuned controllers resulting in inconsistent and suboptimal results. This work employs Bayesian optimization to identify the optimal controller parameters. Additionally, we explore transfer learning in the context of 3D printing by leveraging prior information from past trials. By integrating optimized extrusion force control and transfer learning, we provide a novel framework for closed-loop 3D printing and propose an automated calibration routine that produces high-quality prints for a desired combination of print settings, material, and shape. |
| title | Data-Driven Extrusion Force Control Tuning for 3D Printing |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2403.16470 |