An effective fuzzy-nets training scheme for monitoring tool breakage

Recent research results show that fuzzy logic and the neural networks systems are very effective in detecting the breakage of cutting tools during machining processes. In the present study, a fuzzy-nets training procedure was developed to build the rule banks to meet the dynamic requirements of machining processes. The system is capable of responding and adapting in "real-time" to either shut down the machine when a tool fracture occurs or tune the process parameters "on-line". The training procedure was validated in a back-up truck problem. Furthermore, two fuzzy-nets systems were combined to serve as a tool breakage detection system for an end milling operation. When the system was evaluated for end milling, the adaptive capability (of the fuzzy-nets system) was shown to enable detection of tool breakage to occur "on-line", approaching a real-time base.