Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision
Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three‐dimensional (3D) printing, but is time‐consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users m...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400523 |
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| author | Graig S. Ganitano Benji Maruyama Gilbert L. Peterson |
| author_facet | Graig S. Ganitano Benji Maruyama Gilbert L. Peterson |
| author_sort | Graig S. Ganitano |
| collection | DOAJ |
| description | Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three‐dimensional (3D) printing, but is time‐consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera‐based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct 3D printers. The results show that the system significantly outperforms manufacturer calibration across the machine and material configurations, achieving an average improvement of 32.2% in quality and a 31.2% decrease in completion time with respect to a popular benchmark. |
| format | Article |
| id | doaj-art-47d28d1db6f04481b60f3049da2b9f56 |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-47d28d1db6f04481b60f3049da2b9f562025-08-20T02:11:37ZengWileyAdvanced Intelligent Systems2640-45672025-04-0174n/an/a10.1002/aisy.202400523Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer VisionGraig S. Ganitano0Benji Maruyama1Gilbert L. Peterson2Department of Electrical and Computer Engineering Air Force Institute of Technology Wright‐Patterson AFB OH 45433 USAMaterials and Manufacturing Directorate Air Force Research Laboratory Wright‐Patterson AFB OH 45433 USADepartment of Electrical and Computer Engineering Air Force Institute of Technology Wright‐Patterson AFB OH 45433 USAProper process parameter calibration is critical to the success of fused deposition modeling (FDM) three‐dimensional (3D) printing, but is time‐consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera‐based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct 3D printers. The results show that the system significantly outperforms manufacturer calibration across the machine and material configurations, achieving an average improvement of 32.2% in quality and a 31.2% decrease in completion time with respect to a popular benchmark.https://doi.org/10.1002/aisy.2024005233D printingautonomous experimentationcomputer vision |
| spellingShingle | Graig S. Ganitano Benji Maruyama Gilbert L. Peterson Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision Advanced Intelligent Systems 3D printing autonomous experimentation computer vision |
| title | Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision |
| title_full | Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision |
| title_fullStr | Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision |
| title_full_unstemmed | Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision |
| title_short | Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision |
| title_sort | accelerated multiobjective calibration of fused deposition modeling 3d printers using multitask bayesian optimization and computer vision |
| topic | 3D printing autonomous experimentation computer vision |
| url | https://doi.org/10.1002/aisy.202400523 |
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