Mandal, Nilrudra and Mondal, B. and Doloi, B. (2015) Application of Back Propagation Neural Network Model for Predicting Flank Wear of Yttria Based Zirconia Toughened Alumina (ZTA) Ceramic Inserts. Transactions of the Indian Institute of Metals, 68 (5). pp. 783-789.

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Abstract

A back propagation neural network model has been adopted for the flank wear prediction of zirconia toughened alumina (ZTA) insert in turning operation. The experiments are performed on AISI 4340 steel using developed yttria based ZTA inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Machining conditions such as cutting speed, feed rate and depth of cut are selected as input to the neural network model and flank wear of the inserts corresponding to these conditions has been chosen as the output of the network. The experimentally measured values are used to train the feed forward back propagation artificial neural network for prediction of those conditions. The convergence of the mean square error both in training and testing come out very well. The performance of the trained neural network has been validated with experimental data. The results demonstrate that the machining model is suitable and the optimization strategy satisfies practical requirements.

Item Type:	Article
Subjects:	Ceramics
Depositing User:	Dr. Sarita Ghosh
Date Deposited:	08 Jul 2016 10:19
Last Modified:	08 Jul 2016 10:19
URI:	http://cmeri.csircentral.net/id/eprint/185

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