the features or variables used in the ldf anaylsis required extensive testing until a match between predicted results and experimental casting results was obtained such testing involved computer model experiments using designed full tutorial orthogonal arrays to correlate the variables or features to actual casting results as a fine tune to the predictive capability of pattern recognition analysis the resultant ldf equation from the aforementioned training data of table iii used for the accurate prediction of fig4 is displayed in table vi wherein the coefficients and appropirate constants are given for each category and wherein a column in the table represents an entire equation appendix a a 14 for that category table vi______________________________________linear discriminant functions of grain categories obtained from the10730 node training data set using three input variables valuesrepresent least square coefficients for the variable values a column represents an entire equation for the category s xtal ds grains equiaxed grains freckle defects______________________________________r 0 0519 0 127 0 0396 0 0356g r wd 0 0611 0 0339 0 0396 0 0669gs lat 0 0633 0 00871 0 0429 0 0296constant 0 290 0 498 0 340 0 867______________________________________ in an attempt to improve the casting results i e eliminate the boundary condition a higher g and r combination was sought with a higher rate of mold withdrawal in additional casting trials to force the process parameters into what the defect map predicts is a more favorable region the results are presented in fig4 b although.