Using Artificial Neural Network(ANN) Machinability Investigation Of Yttria Based Zirconia Toughness Alumina (Y-ZTA) Ceramic Insert
Ishani Bishnu1, Jyoti Vimal2, Neha Kumari3
1Ishani Bishnu, Information Technology, Bengal College of Engineering and Technology, Durgapur, India.
2Jyoti Vimal, Information Technology, Bengal College of Engineering and Technology, Durgapur, India.
3Neha Kumari,,Information Technology, Bengal College of Engineering, India.

Manuscript received on October 20, 2013. | Revised Manuscript received on November 01, 2013. | Manuscript published on November 05, 2013. | PP: 162-166 | Volume-3 Issue-5, November 2013 . | Retrieval Number: E1952113513 /2013©BEIESP
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Abstract: A back propagation neural network model has been developed for the machinability evaluation i.e flank wear, cutting force and surface roughness prediction of Zirconia Toughness Alumina(ZTA) inserting in turning process. Numerous experiments have been performed on AISI 4340 steel using developed yttria based ZTA inserts. These inserts are constructed through wet chemical co-precipitation route followed by powder metallurgy process. Process parametric conditions such as cutting speed, feed rate and depth of cut are nominated as input to the neural network model and flank wear, surface roughness and cutting force of the inserts corresponding to these conditions has been selected separately as the output of the network. The experimentally calculated values are used to train the feed forward back propagation artificial neural network(ANN) for forecasting. The mean square error both in training and testing results positively. The performance of the trained neural network has been confirmed with experimental data. The results reveal that the machining model is acceptable and the optimization technique satisfies practical prospects.
Keywords: Zirconia Toughness Alumina(ZTA), Artificial Neural Network(ANN), Flank Wear, Cutting Force, Surface Roughness, Back Propagation.