Date of Award
12-1-2025
Degree Name
Master of Science
Department
Mechanical Engineering
First Advisor
Jung, Sangjin
Abstract
The progress in metal AM technology has enabled printing parts with thin features and intricate geometries. Predicting and reducing metal AM part geometrical deviations to narrow down the difference between printed and designed structure still poses a problem. This work explores changes in laser speed, laser power, and hatch spacing affecting geometrical deviations in parts made using laser powder bed fusion (L-PBF) and places an emphasis on how to predict geometrical defects in the AM parts. Sliced images obtained from CAD designs and printed parts to capture the effect of various L-PBF process parameters are utilized to create a dataset. Conditional Generative Adversarial Networks (cGANs) are trained to predict accurate images that accurately resemble actual geometrical deviations. This facilitates early correction of geometrical deviations during the L-PBF process. A measure of how L-PBF process parameters affect geometric deviation and how a machine learning model can be employed to improve part predictability made using the L-PBF process is provided. In addition, existing measurement metrics for geometric deviations are combined to propose a comprehensive and novel quantification metric.
Access
This thesis is Open Access and may be downloaded by anyone.