ANN MODELLING OF SMALL HOLE DRILLING ON MONEL METAL BY USING ELECTRICAL DISCHARGE MACHINING
Journal Title: JOURNAL OF ADVANCES IN CHEMISTRY - Year 2016, Vol 12, Issue 25
Abstract
The selection of best combination of the process parameters in small hole drilling by Electrical Discharge Machining for an optimum material removal rate with a reduced tool wear rate can reduce machining time and yield better performances. Artificial Neural Network (ANN) has emerged as a powerful tool for modelling complex processes is used for achieving better performance parameter. Artificial Neural Network (ANN) with back propagation algorithm have been used for optimizing and modelling process. The experiments have been designed according to Taguchi L9 orthogonal array. The input parameters were considered for conducting experimentation are namely Discharge Current, Pulse off time and Pulse on time respectively. The performance measures were Material Removal Rate (MRR) and Tool Wear Rate (TWR). ANN models have been developed with varying number of neurons in the hidden layer from 5 to 10. It was found that one hidden layer with 9 neurons predicted the best results. The predicted values were compared with actual experimental results and the predicted values were almost equal to the expected with very less error.Â
Authors and Affiliations
M. S. Vijayanand, M. Ilangkumaran
Keywords
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- EP ID EP653022
- DOI 10.24297/jac.v12i25.1841
- Views 158
- Downloads 0
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