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Using response surface methodology to investigate bone china characteristics: (I) optical characteristics

Using response surface methodology to investigate bone china characteristics: (I) optical... This study deals with investigating the effect of feed composition on bone china optical characteristics using response surface methodology (RSM) by MATLAB software. In this regard, based on batch calculation diagram, 13 feed compositions were chosen and investigated according to RSM methodology. The optical characteristics that were studied are L*, a*, and b* which are representative of brightness, blue, and red colors. Results showed that brightness L* can be modeled by feed composition but a* and b* are functions of impurities and firing environment and cannot be modeled just by considering feed composition. There is also another parameter known as ΔE that represents the difference between the color of sample and a reference color. Since the lower amount of ΔE is the best, a minimization by Levenberg-Markared method was done and the minimum ΔE figured out to be at 58% bone ash, 21% feldspar, and 21% kaolin. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Australian Ceramic Society Springer Journals

Using response surface methodology to investigate bone china characteristics: (I) optical characteristics

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Australian Ceramic Society
Subject
Materials Science; Ceramics, Glass, Composites, Natural Materials; Materials Engineering; Inorganic Chemistry
ISSN
2510-1560
eISSN
2510-1579
DOI
10.1007/s41779-016-0019-1
Publisher site
See Article on Publisher Site

Abstract

This study deals with investigating the effect of feed composition on bone china optical characteristics using response surface methodology (RSM) by MATLAB software. In this regard, based on batch calculation diagram, 13 feed compositions were chosen and investigated according to RSM methodology. The optical characteristics that were studied are L*, a*, and b* which are representative of brightness, blue, and red colors. Results showed that brightness L* can be modeled by feed composition but a* and b* are functions of impurities and firing environment and cannot be modeled just by considering feed composition. There is also another parameter known as ΔE that represents the difference between the color of sample and a reference color. Since the lower amount of ΔE is the best, a minimization by Levenberg-Markared method was done and the minimum ΔE figured out to be at 58% bone ash, 21% feldspar, and 21% kaolin.

Journal

Journal of the Australian Ceramic SocietySpringer Journals

Published: Jan 18, 2017

References