Application of data fusion for the monitoring of the state of cutting-tools

Based on multi-sensor data fusion a method of analysis is presented that fuses together original statistical data obtained from means and variances of multi-channel signals and indentify the cutting tool states by means of their higher-order terms and artificial neural network and fault tree theory. Simulations show that the method is quite effective in identifying different cutting-tool wear levels. Experiments of monitoring boring breakage on FMC show that it is a new practical and feasible method to monitor cutting-tool states by the use of multi-sensor data sampled from a new type flow acoustic emission sensor and accelerator sensor.