Date of Award

2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering

Abstract

Additive manufacturing (AM) technology is transforming the future of design and manufacturing. The additive manufacturing industry has witnessed strong growth in sales of desktop 3D printers. Companies are using desktop 3D printers as a low-priced solution for creating form, fit, and function prototype models or end use parts and assemblies. Students are being inspired by desktop 3D printing as they design, make, and experience physical creations. 3D printing offers many advantages, but the selection of a 3D printer or 3D printing technology can be overwhelming and complex. The goal of this study was to bring forth quantitative data on the deviation of 3D printed features produced from material extrusion machines. This study involved 3D printing a total of 30 parts, 10 parts from each of three desktop 3D printers. 3D printed parts were laser scanned using a Faro Edge ScanArm ES to collect data regarding deviation of feature size. A total of 2,940 deviations, 98 from each 3D printed part, were analyzed using Multivariate Analysis of Covariance (MANCOVA) in IBM SPSS 21 statistical software. Pillais trace reveals that, based on mean deviation of 3D printed features, there is a significant effect of mean deviation (V=1.861, F(24,28) = 15.635, p < .05) on the Afinia H480, a MakerBot Replicator 2X, and a Stratasys Mojo. Regarding mean deviation, this study revealed that there is a statistically significant difference ( p = .000) between the Afinia H480, a MakerBot Replicator 2X, and a Stratasys Mojo.

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