Figure 8 depicts the experimental values of surface roughness and the corresponding calculated values of surface roughness by the RBF network for the test data used.
In Fig. 5 the experimental values of surface roughness and the corresponding calculated values of surface roughness by the neural network for the test data are shown.
Multiple regression equations are formulated for estimating predicted values of surface roughness and cutting forces.
In the section considering case studies, the values of surface chloride concentration are detailed.
The calculated values of surface roughness and the grain sizes are summarized in Table?1.
The measured values of surface roughness and computed MRR are listed in Table 3.
Moreover, the proposed model demonstrates that these oscillations may occur for realistic values of surface heterogeneity.
In this paper values of surface enthalpies and surface Gibbs free energies are given in order to characterize subsurface minerals.
The crossover, which is later for larger values of surface potential, appears in the evolution curve of the wetting layer.
In addition, the proposed method is applied to analyze the critical values of surface deflection and the influence of blank holder force on surface deflection.
