Term of Award

Fall 2021

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Manufacturing Engineering

Committee Chair

Dean Snelling

Committee Member 1

Kamran Kardel

Committee Member 2

Haijun Gong

Abstract

Additive manufacturing processes allow for a great degree of flexibility in terms of part production. The process is autonomous once the part has started printing in that the operator generally does not need to intervene until the part is finished. One issue that this introduces, however, is an inability to determine part quality during the printing process. Once a part has started printing, the operator must either wait until the part is finished or regularly check on the part during the print to determine the part quality. Using data gathered from multiple sensors, a quality score can be used to estimate the part quality at any point during the printing process. The development of the score also observed several of the largest contributing ambient factors to both the surface roughness and the part porosity. The largest contributors to quality were the chamber temperature and the oxygen content for the surface roughness and porosity, respectively. Each build characteristic was plotted, and the best fit equations created the quality score. The score generated a zero to one hundred scale that can be easily viewed without intimate knowledge of the process.

OCLC Number

1295611593

Research Data and Supplementary Material

No

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