For evaluating the performance of MGGP-based method, the MGGP model is firstly obtained using the experimental data proposed by Chandrasekharan et al.
The parameter setting is very important for the generalization ability of the MGGP model; the parameters selected by trial-and-error approach are shown in Table 4.
The best fitness function is given at generation 145 and the predicted function based on MGGP for the thrust force is as follows:
Figure 9 shows the results of the thrust force predicted by Chandrasekharan and MGGP model, respectively; it is clearly seen that the latter is closer to the ideal line than the former.
The values of the mare shown in Table 5; the results show significantly that MGGP method has better performance.
In order to make the model of drill point angles for minimizing drilling forces, in this paper, MGGP method is adopted using the measurement data of Table 1 as training data.
Equation (6) shows the formulated model to predict FM as function of drill point relief angle ([x.sub.1]) and chisel edge angle ([x.sub.2]) based on MGGP. The predicted and actual values of FM on data point are shown in Figure 11; it is clearly seen that the predicted values agree very well with the actual measurement ones.
Using these input and output values as the training data, the model for predicting thrust forces and torque was proposed based on MGGP to obtain the optimal drill point angles for minimizing the thrust forces and torque.
Caption: FIGURE 8: Thrust forces predicted by MGGP model.
Caption: FIGURE 9: Comparison of thrust forces predicted by Chandrasekharan and MGGP model.