Hybrid self-organizing fuzzy and radial basis-function neural-network controller for constant cutting force in turning

Abstract

A useful method for increasing the metal removal rate and the tool life of turning systems is by controlling a constant cutting force. This study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC) for turning systems to maintain constant cutting force operation. The HSFRBNC uses a radial basis function neural-network to regulate both the learning rate and the weighting distribution of a self-organizing fuzzy controller (SOFC) in real time to appropriate values, instead of obtaining these values by trial and error. It not only eliminates the difficulties of finding appropriate membership functions and fuzzy rules in the design of the fuzzy logic controller but also solves the problem of determining suitable parameters of the SOFC. The HSFRBNC has better control performance than the SOFC for manipulating constant cutting force in the turning system, as shown in the simulation results.