Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2020-09-03 , DOI: 10.1007/s10845-020-01635-5 Siti Nurfadilah Binti Jaini , Deug-Woo Lee , Seung-Jun Lee , Mi-Ru Kim , Gil-Ho Son
In this study, an indirect tool monitoring was developed based on the installation of a gap sensor in measuring the signal related to the tool behavior during the drilling process. Eleven types of twist drills with different tool conditions were utilized to differentiate the sensorial signals based on the tool states. A statistical analysis was conducted in the signal processing, by extracting the gap sensor signal associates from each tool condition, using the skewness and kurtosis features. Multi-class classification was conducted using the multilayer perceptron (MLP) feed forward neural network (FF-NN) model to classify and predict the tool condition based on the skewness and kurtosis data. The architectures of the MLP FF-NN models were varied to optimize the classification accuracy. This study found that the tool condition was correlated to the displacement of the drill machine spindle because the runout occurred when the sensor signal displayed fluctuation and irregularity trends. The peak intensity of the gap sensor signals increased with increasing wear severity of the twist drill. An ideal MLP FF-NN structure was achieved when the classification performance was optimized to be consistent with the learning curve.
中文翻译:
基于间隙传感器信号和多层感知器前馈神经网络的钻井间接刀具监测
在这项研究中,基于间隙传感器的安装开发了一种间接工具监控,该传感器用于测量与钻孔过程中工具行为有关的信号。利用11种类型的具有不同工具条件的麻花钻,根据工具状态区分感官信号。通过使用偏斜度和峰度特征从每个工具条件中提取间隙传感器信号关联,在信号处理中进行了统计分析。使用多层感知器(MLP)前馈神经网络(FF-NN)模型进行多类分类,以基于偏度和峰度数据对工具条件进行分类和预测。改变了MLP FF-NN模型的体系结构以优化分类准确性。这项研究发现,刀具状态与钻机主轴的位移相关,因为当传感器信号显示波动和不规则趋势时发生跳动。间隙传感器信号的峰值强度随着麻花钻磨损严重程度的增加而增加。当优化分类性能以使其与学习曲线一致时,可获得理想的MLP FF-NN结构。