AN ADAPTIVE NETWORK BASED FUZZY INFERENCE (ANFIS) MODEL TO PREDICT THE GLAZE COMPOSITIONS AND GLASSING VALUES IN CERAMIC GLAZE APPLICATIONS
Published: 31 May 2015
Abstract: In this study, it is aimed to predict the glazing and appropriate glaze composition with computer software to reduce of the product costs during prototype production in the ceramic industry. Hence, formulas were prepared to use washed Uşak Kaolin, Minium and Quartz. Samples were prepared using these formulas and fired at deformation temperatures 950 and 1150 0C. An adaptive network based fuzzy inference (ANFIS) model to predict the glaze compositions and glassing values were chosen as output values while temperature, surface tension and expansion coefficient were chosen as input values. To this end, MatLab 2010 Toolbox package program was used in this study. A comparative evaluation of the predicted and experimental results has shown that ANFIS model has a high accuracy and absolute relative error is less than 17.39%. As a result of training, high performance was obtained between regression for glaze components R2=0.5301and R2=0.9984. Likewise, according to test results, high performance was obtained between regression for glassing R2=0.8167 and regressions for glaze components R2=0.9996 and 0.9986. Moreover, the ANFIS model was an easy and practical method to predict the glaze compositions and glassing values.
Keywords: artificial neuronal network, an adaptive network based fuzzy inference, ceramic, chemical properties, glazing, glaze component
Download full text
Back to the contents of the volume
© 2018 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This permission does not cover any third party copyrighted material which may appear in the work requested.