Analysis of model building techniques for the development of machinability database systems

Computerized machinability database systems are essential for the selection of optimum cutting conditions during process planning, and these form an important component in the implementation of computer integrated manufacturing (CIM) systems. This paper presents a comparative analysis of the different model building techniques available in commercial statistical packages, to select the most suitable technique for use in machinability database systems. The techniques analysed are; backward elimination, forward selection, stepwise regression, and all possible subset regression. Experimental machining response data for the surface roughness when turning grey cast iron (154 BHN) and steel (140 BHN) have been analysed to evaluate the four model building techniques. Second order polynomial model structures with logarithmic transformation of the variables has been used for building adequate models. The adequacy of the fitted regression equation is checked by analysing the residuals. Based on the results of this analysis, the advantages and limitations of the four different techniques for building machinability models are discussed. It has been found that the backward elimination and all possible subset regression are the best suited techniques for model building in machining database systems.