Table 1. Some studies in the field of diamond wire saw. ‘igure 1. Schematic presentation of a diamond wire-cutting layout and cross section of a diamond bead. Table 2. Operational parameters of wire saw machine. Table 3. Factors affecting cutting efficiency in the diamond wire saw method (Ozcelik, 1999). Figure 3. Production versus quartz content. Table 4. The results of performance studies and laboratory tests. Figure 5. Production versus uniaxial compression strength. independent variables, and b;, bo, b, are the regression coefficients of X,, Xz, X;. functional form. A Euclidean distance (D?) is estimated between an input vector and the weights, which are then rescaled by the spreading factor. The radial basis output is then the exponential of the negatively weighted distance. The GRNN equation is: Table 6. The results of laboratory tests and production rate for test data. Figure 6. The estimated production values versus the observed production values for test data (MnLR). Figure 7. The estimated production values versus the observed production values for test data (MLR). Table 7. The estimated and observed production values for all data. Figure 8. The estimated production values versus the observed production values (MnLR). Figure 9. The estimated production values versus the observed production values (MLR). Table 8. Correlation coefficient values and means relative error with different smooth factors in train and test data-set. Figure 10. Architecture of GRNN (Demuth and Beale, 2002). Note: R, no. of elements in input vector; Q, no. of neurons in layer 1; Q, no. of neurons in layer 2; Q, no. of input/target pairs. a} is ith element of a, where IW, , is a vector made of the ith row of IW, 4. Table 9. A comparison of the results of ANN, MnLR, and MLR methods. Figure 12. The estimated production values versus the observed production values for test data (ANN). Figure 11. The estimated production values versus the observed production values (ANN).
