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Title of Journal: Int J Adv Manuf Technol

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Abbravation: The International Journal of Advanced Manufacturing Technology

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Springer-Verlag

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DOI

10.1016/0148-9062(79)90101-3

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1433-3015

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Analysis and predictive modeling of performance pa

Authors: Saeed Zare Chavoshi
Publish Date: 2010/09/01
Volume: 53, Issue: 9-12, Pages: 1081-1101
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Abstract

In this paper the effect of feed rate voltage and flow rate of electrolyte on some performance parameters such as surface roughness material removal rate and overcut of SAEXEVF valvesteel during electrochemical drilling in NaCl and NaNo3 electrolytic solutions have been studied using the main effect plot the interaction plot and the ANOVA analysis In continuation in this case which the training dataset was small an investigation has been done on the capability of the optimum presented regression analysis RA artificial neural network ANN and coactive neurofuzzy inference system CANFIS to predict the surface roughness material removal rate and overcut The predicted parameters by the employed models have been compared with the experimental data The comparison of results indicated that in electrochemical drilling using different electrolytic solutions CANFIS gives the best results to predict the surface roughness and overcut as well while ANN is the best for predicting the material removal rate

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