Data Transformations with a Full 2^6 Experimental Design—A Metal-Cutting Case Study

Abstract

Abstract: The Box-Cox transformation was evaluated with reference to a six-factor full factorial (2^6) data set with 64 runs. The data were used to determine the optimal operating conditions for a milling machine with respect to surface finish. A suitable transformation was determined by minimizing the mean square errors, evaluating the size of the effect significances, the normal probability plots of the estimated effects, Shapiro-Wilk test and the model residuals. The achievement of both normality with constant variance and a simple model came about as a result of a trade-off between several different criteria.