Date of Award
3-22-2019
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Department of Electrical and Computer Engineering
First Advisor
Gilbert L. Peterson, PhD
Abstract
Additive manufacturing is a dynamic technology with a compelling potential to advance the manufacturing industry. Despite its capacity to produce intricate designs in an efficient manner, industry still has not widely adopted additive manufacturing since its commercialization as a result of its many challenges related to quality control. The Air Force Research Laboratory (AFRL), Materials and Manufacturing Directorate, Functional Materials Division, Soft Matter Materials Branch (RXAS) requires a practical and reliable method for maintaining quality control for the production of printed flexible electronics. Height estimation is a crucial component for maintaining quality control in Material Extrusion Additive Manufacturing (MEAM), as the fundamental process for constructing any structure relies on the consecutive layering of precise extrusions. This work presents a computer vision solution to the problem of height estimation using monocular imagery as applicable to MEAM.
AFIT Designator
AFIT-ENG-MS-19-M-029
DTIC Accession Number
AD1075127
Recommended Citation
Gorospe, Andrew C., "Non-Contact Height Estimation for Material Extrusion Additive Systems via Monocular Imagery" (2019). Theses and Dissertations. 2259.
https://scholar.afit.edu/etd/2259