OCTOBER 19-20, 2012 - YMCA University of Science & Technology

Proceedings of the National Conference on
Trends and Advances in Mechanical Engineering,
YMCA University of Science & Technology, Faridabad, Haryana, Oct 19-20, 2012
5.8
Best Linear Fit: A = (1.2) T + (-0.386)
Best Linear Fit: A = (0.296) T + (0.00579)
5.6
R = .979
R = 1
Pred Ra (A)
5.4
5.2
5
4.8
4.6
Data Points
Best Linear Fit
A = T
Pred MRR (A)
9
8.5
8
9.5 x 10-3 Expt MRR (T)
Data Points
Best Linear Fit
A = T
4.4
7.5
4.2
7
4
3.8
3.8 4 4.2 4.4 4.6 4.8 5
Expt Ra (T)
6.5
6.5 7 7.5 8 8.5 9 9.5
x 10 -3
Figure 7 Correlation of the testing patterns for MRR and R a
5. Conclusion
The training and testing data set was taken from experiments on self developed setup on EDM, based on L 9 OA.
The ANN back propagation algorithm with four inputs, two outputs, and one hidden layer with 23 neurons has
been employed to establish the process model. The model after proper training is capable of predicting the
response parameter. The number of hidden layer neurons and the learning factors employed are found to be
suitable for the present investigation. There exist highly non-linear relationships between MRR, R a and the
machining conditions. This justifies the use of ANN to develop the model.
6. References
1.Aguair P. R., Dotto F. R. L. and Bianch E. C. (2005), “Study of Thresholds to Burning in Surface Grinding
Process,” Journal of the Brazillian Society of Mechanical Sciences and Engineering, Vol. 27(2), pp 150-156.
2.Erden A., and Kaftanoglu B. (1981), “Heat Transfer Modeling of Electric Discharge Machining, in: Proc. 21st
Int. Mach. Tool Des. Research Conf., London.
3.Schalkoff, R.B. (1997) “Artificial Neural Networks” McGraw-Hill International Ed.
549