Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/6323
Title: Prediction of etching rate of alumino-silicate glass by RSM and ANN
Authors: Ting, H T
Abou-El-Hossein, K A
Chua, H B
Keywords: Alumino-silicate glass
ANN
Etching rate
RSM
Issue Date: Nov-2009
Publisher: CSIR
Abstract: In this study, response surface methodology (RSM) andartificial neural network (ANN) were applied to predict material removal rate in chemical etching process of alumino-silicate glass (SiO2 57/Al2O3 36/CaO/MgO/BaO). 2k Factorial design was performed to evaluate linearity condition among process parameters. Analysis of variance (ANOVA) was performed and quadratic model was found most significant for data values of process parameters. New models were able to predict etching rate of alumino-silicate glass, with a great confidence. Input parameters analyzed were temperature, etching period and type of setup with and without condensation.
Description: 920-924
URI: http://hdl.handle.net/123456789/6323
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.68(11) [November 2009]

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