Hybrid data mining and type II fuzzy system approach for surface finish from the perspective of E-manufacturing

Abstract

The current trends in industry include integration of an information and knowledge base network with a manufacturing system, which coined a new term, E-Manufacturing. From the perspective of E-Manufacturing, any production equipment and its control functions do not exist alone, but become a part of the holistic operation system with distant monitoring and fault diagnostic capabilities. The key to this new paradigm is the accessibility to a remotely located system and having the means of responding to a changing environment. In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.