2.2 Design of experiment and objective

Five different set of fractional factorial (26–<sup>2</sup> = 16) experimental design have been selected at two levels, so that 80 rows of experimental data can be observed at three level of replication on D2 using WEDM. In this study the main aim to minimize the surface roughness of D2 on best possible maximum MRR during WEDM (Table 2).

#### 2.3 ANN architecture and training

The hit and trail method based on literature have been adapted to find 7 and 10 neurons in primary and secondary hidden layers respectively, which effects on the R-square statistics for best prediction modeling. Tan sigmoid activation (squashing) function used as the (infinite input to finite output range) learning capability by the


Table 2. Factors for screening test.

presented here for achieving the aimed to optimization of influencing process

of individual input parameters will be observed on the Ra (Tables 3–6).

Predictions against observations of Ra for model-D2, S2, 7 N (validation dataset).

Predictions against observations of Ra for model-D2, S2, 7 N (training dataset).

Optimization of Surface Roughness of D2 Steels in WEDM using ANN Technique

DOI: http://dx.doi.org/10.5772/intechopen.81816

possible square of residuals are available, to draw the Figure 6(a–f).

OPTIMIZATION OF PROCESS PARAMETER Ra: D2, 7 Neurons in hidden layer. The best model needs to be predicted among Model-S1 and S2, in D2 steel. Effect

It is evident from Table 3, that each independent influencing input parameter has corresponding values of their square of residuals at each three levels. Two values at each level (2 3 = 6 rows) has been taken for each inputs, where lowest

Figure 6(a–f) shows the relations between individual influencing parameters (Vg, Fr, Ton, Toff, Wf and Wt) to their optimized response, surface roughness (Ra) with corresponding values of MRR. Table 5 also indicates that unique values of each influencing parameters (corresponding to its serial numbers of Table 5) gives

parameters (Figures 4 and 5).

5. Result

115

Figure 4.

Figure 5.

4. Optimization of process parameters

optimum responses, which has been highlighted.

Figure 3. Artificial neural network approach.

controllable instructed programme in MATLAB 2010a. Steepest descent problem used for the training algorithm to train the multilayer network, where the values of gradient was smallest because of the small changes in weight and biases. p1, p2, p3, p4, p5 and p6 are the six input layer neurons and Oi is the single neurons in output layer, whereas I11-I17 and I21-I29 (7 neurons present in primary and 10 in secondary hidden layers) are the hidden layers (Figure 3).
