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
4. Artificial neural network testing
The trained ANN was initially tested by presenting 9 input patterns which were employed for the training
purpose for each input pattern, the predicted value of MRR and R a are compared with the respective measured
average values and the absolute percentage error is computed which is given as:
Table IV
Machining conditions for testing patterns of ANN
MSE
10 -1
10 -2
10 -3
Performance is 9.97179e-006, Goal is 1e-005
Test no.
Wheel
speed
(RPM)
Current
(A)
Pulse
ontime
(µs)
Duty
factor
1. 700 7 100 0.578
2. 1200 9 200 0.697
10 0 Epochs
10 -4
10 -5
10 -6
0 200 400 600 800 1000 1200 1400
Figure 5 The variation of mean squared error (MSE) with number of epochs.
where is the measured value (average) and is the ANN predicted value of the response for th trial.
It was found that the predicted and experimental values were very quite close to each other Regression analysis
between the network response and the corresponding targets has been performed for measuring the performance
of a trained network. This was carried out using the postreg in the NN toolbox. The graphical outputs of postreg
are presented in Fig.6 for the training data sets. The correlation coefficient (R value) between the outputs and
targets is a measure of how well the variation in the output is explained by the targets. If R value is 1 then it
indicates perfect correlation between targets (T) and predicted outputs (A). In the present case the R value is 1
and 0.999 for MRR and R a , indicating a very good relation.
For the validation purpose, 2 new trials were tested, which do not belong to training data set. Table IV gives the
chosen machining conditions used for the confirmation tests. For these validation data set, the MRR and R a
values are predicted using the ANN model and then compared with the measured values. It was also found that
the maximum absolute percentage error was around 28.11 and 16.77 for MRR and R a respectively. The graphical
output of postreg is presented in Fig.7. for the validation datasets and R values are 0.979 and 1 for the outputs
MRR and R a respectively.
Best Linear Fit: A = (0.995) T + (4.49e-005)
7.5
Best Linear Fit: A = (1.01) T + (-0.0396)
Pred MRR (A)
11
10.5
10
9.5
9
8.5
R = 1
11.5 x 10-3 Expt MRR (T)
Data Points
Best Linear Fit
A = T
Pred Ra (A)
7
R = 0.998
Data Points
Best Linear Fit
A = T
8
6.5
7.5
7
6.5
6 7 8 9 10 11 12
x 10 -3
6
6 6.5 7 7.5
Expt Ra (T)
Figure 6 Correlation of the training patterns for MRR and R a
548