Date of Award
9-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Department of Electrical and Computer Engineering
First Advisor
Gilbert L. Peterson, PhD
Abstract
Additive Manufacturing (AM), also known as 3D printing, has emerged as a key component of Industry 4.0, enabling reduced cost, quick production, greater sustainability, and increased design complexity compared to its traditional manufacturing counterpart. Currently, Fused Deposition Modeling (FDM) technology dominates the AM market with respect to the number of 3D printers in use. However, the FDM process is sensitive to changes in system configuration, especially the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters, acting as a barrier to the technology.
To enable greater accessibility for non-expert users, this research explores techniques in autonomous experimentation to calibrate FDM 3D printers in both single and multi-objective settings without human intervention. A camera-based computer vision framework is developed and validated as a low-cost, easily deployable alternative to traditional human-in-the-loop methods for characterizing print quality. The framework is combined with combinatorial and surrogate optimization techniques to search for optimal process parameter configurations within a limited experimental budget. The results demonstrate that for the single-objective problem of optimizing print quality, process parameters can be autonomously calibrated to produce parts with greater geometric accuracy than manufacturer specified tolerances. Additionally, when optimizing for both print quality and completion time, the results show that process parameters can be calibrated to significantly outperform manufacturer defaults, achieving an average improvement of 32.2% in quality and a 31.2% reduction in completion time with respect to a popular benchmarking protocol.
AFIT Designator
AFIT-ENG-DS-24-S-020
Recommended Citation
Ganitano, Graig S., "Autonomous Experimentation for Accelerated Calibration of Fused Deposition Modeling 3D Printers" (2024). Theses and Dissertations. 7995.
https://scholar.afit.edu/etd/7995
SF298 for dissertation AFIT-ENG-DS-24-S-020, G. Ganitano
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.
The SF298 form for this work is attached separately in the Additional Files section.