数据融合法在监测刀具切削状态中的应用

从多传感器数据融合观点出发，提出利用多通道传感信号的均值和标准差为基本参数，由其高阶项和人工神经网络进行数据融合，并用故障树推理方法诊断刀具切削状态的分析方法；经计算机模拟，证实了神经网络对刀具不同磨损程度的识别能力；在立式加工中心上，利用新型非表面接触式声发射（ＡＥ）传感器和振动加速度传感器组成的多通道传感数据进行了镗刀破损状态的监测，证实了所述方法的正确性、可行性和实时性。

Application of Data Fusion for the Monitoring of the State of Cutting-Tools

Based on multi-sensor data fusion a method of analysis is presental that fuses together original statistical data obtained from means and variances of multi-channel signals and indentify the cutting tool stain 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 practival 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.