• Accelerated Multiobjective Cal...

*Result*: Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision

Title:
Accelerated Multiobjective Calibration of Fused Deposition Modeling 3D Printers Using Multitask Bayesian Optimization and Computer Vision
Authors:
Ganitano, Craig S., Maruyama, Benji, Peterson, Gilbert L.
Source:
Faculty Publications
Publisher Information:
AFIT Scholar
Publication Year:
2025
Collection:
AFTI Scholar (Air Force Institute of Technology)
Subject Terms:
3D printing, Autonomous experimentation, Computer vision, Artificial Intelligence and Robotics
Document Type:
*Academic Journal* text
File Description:
application/pdf
Language:
unknown
Relation:
https://scholar.afit.edu/facpub/1840; https://scholar.afit.edu/context/facpub/article/2866/viewcontent/Advanced_Intelligent_Systems___2025___Ganitano___Accelerated_Multiobjective_Calibration_of_Fused_Deposition_Modeling_3D.pdf
DOI:
10.1002/aisy.202400523;
Availability:
https://scholar.afit.edu/facpub/1840
https://doi.org/10.1002/aisy.202400523;
https://scholar.afit.edu/context/facpub/article/2866/viewcontent/Advanced_Intelligent_Systems___2025___Ganitano___Accelerated_Multiobjective_Calibration_of_Fused_Deposition_Modeling_3D.pdf
Accession Number:
edsbas.F76512B3
Database:
BASE

*Further Information*

*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.*