Manufacturing process performance prediction by integrating crisp and granular information

Abstract

This study deals with the integration of crisp and granular information for predicting the performance of a manufacturing process. Supporting and computing a set of two If-Then rules is considered the central idea for this integration. In these rules, the antecedent part deals with the recommended ranges of the control variables of the process, while the consequent part deals with the acceptable ranges of the performance measures of the process. The rules specify that if the control variables are kept within their recommended ranges, then it is likely or unlikely to get the performance measures within their acceptable ranges. The rules are supported by using the following conditional probabilities: the probability of getting the performance measures acceptable given that the control variables are within their recommended ranges (which should be likely), and the probability of getting performance measures acceptable given that the control variables are not within their recommended ranges (which should be unlikely). The remarkable thing is that both acceptable ranges and recommended ranges are subjectively defined concepts. So are likelihood perceptions such as “likely” and “unlikely.” Therefore, all of them can be defined by using some kind of fuzzy-granular information. The usefulness of this new approach is demonstrated by solving a machining decision-making problem (select cutting conditions and inserts satisfying subjectively defined surface finish requirement in terms of roughness and fractal dimension of machined surface). Further study should be directed toward understanding these rules in the context of predictive process planning.