Abstract

Color 3D printing is a relatively young technology with several exciting applications and challenges yet to be explored. One of those challenges is the effect that three dimensional surface geometries have on appearance. The appearance of 3D objects is complex and can be affected by the interaction between several visual appearance parameters such as color, gloss and surface texture. Since traditional printing is only 2D, several of these challenges have either been solved or never needed to be addressed. Complicating matters further, different color 3D printing technologies and materials come with their own inherent material appearance properties, necessitating the study of these appearance parameters on an individual case by case basis.

Neural networks are powerful tools that are finding their way into just about every field imaginable, and the world of color science is no exception. A process described by previous researchers provides a method for picking out color sensitive neurons in a given layer of a convolutional neural network (CNN). Typically, CNNs are used for image classification but can also be used for image comparison. A siamese CNN was built and shown to be a good model for appearance differences using textured color patches designed to simulate the appearance of color 3D printed objects.

A direct scaling psychophysical experiment was done to create an interval scale of perceptual appearance between color 3D printed objects printed at different angles. The objects used for this experiment were printed with an HP® Jet Fusion 580 color 3D printer. The objects exhibit print angle dependent surface textures inherent to the layered printing process itself. The preliminary siamese CNN showed that perceptual differences in the prints were likely to exist and could be modeled using a neural network. However, the results of the psychophysical experiment indicated that CIELAB color differences were extremely strong predictors of observer perceptions, even with variable surface texture in uncontrolled lighting conditions.

Library of Congress Subject Headings

Three-dimensional printing--Quality control; Color printing--Quality control; Color perception; Neural networks (Computer science)

Publication Date

12-2020

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Nathan Cahill

Advisor/Committee Member

Susan P. Farnand

Advisor/Committee Member

Mark D. Fairchild

Campus

RIT – Main Campus

Plan Codes

IMGS-PHD

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