Improving error compensation via a fuzzy-neural hybrid model

Factors that influence the accuracy of machining and in-cycle measuring processes are varied. It is very difficult or impossible to identify and fix each error by in-cycle measuring systems with touch trigger probes. Moreover, even where errors have been determined, the effects and relationships among them are very complicated, and there are no existing mathematical models to be applied to control or compensate the machining processes. This paper introduces a new in-cycle measuring and error compensation system based on a fuzzy controller combined with a supervised neural network. The fuzzy neural hybrid compensation model consists of a multilayer feed-forward neural network trained with the back propagation gradient descent algorithm. The fuzzy rules are implemented by the hidden layer of the network, and the fuzzy max-min operations are replaced by the feed-forward summation. The proposed system reveals that it is feasible to achieve an improved machining performance by adapting the fuzzy membership functions and generating linguistic control rules. A series of experiments is performed, and the characteristics of the system are evaluated and discussed.