An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning

Abstract

Ultra-precision turning is an important operation used to generate a high surface finish in precision component and its input–output relationships are highly non-linear. Surface finish of a turned surface depends on the selection of cutting parameters, such as cutting speed, feed and depth of cut. But, it is difficult to obtain the exact mathematical expression for surface finish in terms of the input variables. Realizing the fact that fuzzy logic controller (FLC) is a potential tool for dealing with impression and uncertainty, an attempt was made by the authors previously to model the grinding operation using a combined GA–fuzzy approach. In that approach, an optimal FLC was developed using a GA-based tuning. But, the main drawback of that approach lies in the fact that it does not keep track on the number of times a particular rule is getting fired during training. Thus, the resulting optimized rule base (RB) of an FLC may contain some redundant rules. To overcome this, an expert system is developed in this paper, based on the fuzzy basis function network (FBFN) to predict surface finish in ultra-precision turning. An approach for automatic design of RB and the weight factors (WFs) for different rules is developed using a GA, based on error reduction measures. Results of the proposed expert system have been compared to the experimental results available in the literature.