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Application of Artificial neural network and wavelet packet transform for vibration signal based monitoring in mechanical micro drilling

机译:人工神经网络和小波包变换在振动信号监测中的应用

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In order to achieve high quality and productivity in microdrilling, monitoring of the prefailure phase and detection of tool breakage is very important. In the present work, vibration signals have been studied during micro drilling operations to monitor the prefailure phase of the micro-drills. These signals have been processed in time domain and time-frequency domain to extract tool wear sensitive features. An Artificial neural network (ANN) has been developed from time domain feature and wavelet packet features of vibration signals to predict the hole number of the micro-drilling at different spindle speed and feed. The prediction of drilled hole number using ANN model is in good agreement to experimentally obtained drilled hole number. It has been found that wavelet packet feature based ANN model outperforms the time domain feature based ANN model.
机译:为了在微钻孔中实现高质量和高生产率,监视故障前阶段和检测工具破损非常重要。在当前的工作中,已经在微钻操作期间研究了振动信号以监测微钻的故障前阶段。这些信号已在时域和时频域中进行处理,以提取工具磨损敏感特征。从振动信号的时域特征和小波包特征发展了一种人工神经网络(ANN),以预测在不同主轴转速和进给率下的微钻的孔数。使用人工神经网络模型对钻孔数的预测与实验获得的钻孔数非常吻合。已经发现,基于小波包特征的ANN模型优于基于时域特征的ANN模型。

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