Abstract
This paper describes a new approach, the fuzzy-nets system, for monitoring tool breakage in end-milling operations. The fuzzy-nets tool-breakage detection (FNTBD) system has a self-learning capability to generate rule bases and to fine tune the term sets of each linguistic variable to the appropriate level of granularity. A self-learning algorithm for developing the FNTBD system consists of five steps: 1. Divide the input space into fuzzy regions. 2. Generate fuzzy rules from given data pairs through experimentation. 3. Avoid conflicting rules based on top-down or bottom-up methodologies. 4. Develop a combined fuzzy rule base. 5. Determine a mapping system based on the fuzzy rule base.
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