CUTTING STATE MONITORING IN MILLING BY A NEURAL-NETWORK

The application of a neural network to cutting state monitoring in fac e milling was introduced and evaluated on multiple sensor data such as cutting forces and vibrations. This monitoring system consists of a s tatistically based adaptive preprocessor (autoregressive (AR) time ser ies modeling) for generating features from each sensor, followed by a highly parallel neural network for associating the preprocessor output s (sensor fusion) with the appropriate decisions. AR model parameters were used as features, and the cutting states (normal, unstable and to ol life end) were successfully detected by monitoring the evolution of model parameters during face milling. The proposed system offers fast operation through recursive preprocessing and highly parallel associa tion, and a data-driven training scheme without explicit rules or a pr iori statistics. It appears proven on limited experimental data.