Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites
ANFIS based tool wear/condition monitoring is demonstrated for end milling GFRP composites. Grid portioning and subtractive clustering are used to partition the experimental data. Using feed force produces better prediction capability compared to cutting force. Statistical analyses are used to evaluate the predictive capability.
tool wear machining gfrp composites subtractive clustering anfis modelling statistical performance grid partitioning