Abstract
Real-time nature, uncertainty handling and learning ability are essential requirements for knowledge representation and processing techniques to be applied at lower levels of intelligent manufacturing systems. The paper demonstrates and compares the applicability of neural networks and neuro fuzzy techniques for monitoring of milling tools. Learning and classification performances of back propagation (BP) networks and the neuro-fuzzy approach using different learning techniques are compared. The possible role of such a hybrid solution in an intelligent manufacturing environment is investigated.
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