Abstract The fuzzy-nets in-process (FNIP) system is proposed for monitoring tool breakage in end-milling operations. The FNIP system consists of two components: (1) the fuzzy search classifier (FSQ, which maps a state vector into a recommended action using fuzzy pattern recognition; and (2) the fuzzy adaptive controller (FAQ, which maps a state vector and a failure signal into a scalar grade that indicates state integrity. The FAC also produces the output action value, p , to upgrade FSC mapping according to the variation of the input state. By coupling fuzzy logic control systems and neural networks into the fuzzy-nets system, a self-learning capability (ability to generate rule bases and to fine-tune the membership functions of each linguist variable to the appropriate level of granularity) was developed. With this on-line learning capability, the fuzzy rule bases of FSC and FAC are established by fine-tuning the parameters in the FNIP system. After establishing all the fuzzy rule bases, the performance of the FNIP system is examined for an end-milling operation. Experiments have shown that the FNIP system is able to detect tool breakage in the end-milling operation “on-line”, approaching a real-time basis.
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