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Artificial Neural Network Modeling for Electrical Discharge Machining Parameters

Artificial Neural Network Modeling for Electrical Discharge Machining Parameters

Raja Das, M. K. Pradhan
Copyright: © 2014 |Pages: 22
ISBN13: 9781466649408|ISBN10: 1466649402|EISBN13: 9781466649415
DOI: 10.4018/978-1-4666-4940-8.ch014
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MLA

Das, Raja, and M. K. Pradhan. "Artificial Neural Network Modeling for Electrical Discharge Machining Parameters." Advances in Secure Computing, Internet Services, and Applications, edited by B.K. Tripathy and D. P. Acharjya, IGI Global, 2014, pp. 281-302. https://doi.org/10.4018/978-1-4666-4940-8.ch014

APA

Das, R. & Pradhan, M. K. (2014). Artificial Neural Network Modeling for Electrical Discharge Machining Parameters. In B. Tripathy & D. Acharjya (Eds.), Advances in Secure Computing, Internet Services, and Applications (pp. 281-302). IGI Global. https://doi.org/10.4018/978-1-4666-4940-8.ch014

Chicago

Das, Raja, and M. K. Pradhan. "Artificial Neural Network Modeling for Electrical Discharge Machining Parameters." In Advances in Secure Computing, Internet Services, and Applications, edited by B.K. Tripathy and D. P. Acharjya, 281-302. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4940-8.ch014

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Abstract

The objective of the chapter is to present the application of Artificial Neural Network (ANN) modelling of the Electrical Discharge Machining (EDM) process. It establishes the best ANN model by comparing the prediction from different models under the effect of process parameters. In EDM, the motivation is frequently to get better Material Removal Rate (MRR) with fulfilling better surface quality of machined components. The vital requirements are as small a radial overcut with minimal tool wear rate. The quality of a machined surface is very important to fulfilling the growing demands of higher component performance, durability, and reliability. To improve the reliability of the machine component, it is necessary to have in depth knowledge of the effect of parameters on the aforesaid responses of the components. An extensive chain of experiments has been conducted over a wide range of input parameters, using the full factorial design. More than 150 experiments have been conducted on AISI D2 work piece materials using copper electrodes to get the data for training and testing. The additional experiments were obtained to validate the model predictions. The performance of three neural network models is discussed in the evaluation of the generalization ability of the trained neural network. It was observed that the artificial neural network models could predict the process performance with reasonable accuracy, under varying machining conditions.

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