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首页> 外文会议>International Conference on Industrial Engineering, Applications and Manufacturing >Tool Condition Monitoring in Micro-drilling Using Vibration Signals and Artificial Neural Network: Subtitle: TCM in micro-drilling using vibration signals
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Tool Condition Monitoring in Micro-drilling Using Vibration Signals and Artificial Neural Network: Subtitle: TCM in micro-drilling using vibration signals

机译：使用振动信号和人工神经网络进行微钻孔的工具状态监测：字幕：使用振动信号微钻的TCM

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摘要

Tool condition monitoring is one of the key issues in mechanical micromachining for efficient manufacturing of the micro-parts in several industries. In the present study, a tool condition monitoring system for micro-drilling is developed using a tri-axial accelerometer, a data acquisition and signal processing module and an artificial neural network. Micro-drilling experiments were carried out on an austenitic stainless steel ((X5CrNi 18-10) workpiece with the 500 μm diameter micro-drill. A three-axis accelerometer was installed on a sensor plate attached to the workpiece to collect vibration signals in three directions during drilling. The time domain "root mean square" feature representing changes in tool wear was estimated for vibration signals of all three directions. The variations of the rms micro-drilling vibrations were investigated with the increasing number of holes under different cutting conditions. An artificial neural network (ANN) model was developed to fuse the rms values of all three directional vibration signals, the spindle speed and feed parameters to predict the drilled hole number. The predicted drilled hole number obtained with the ANN model is in good agreement with the experimentally obtained drilled hole number. It has been also shown that the error of hole number prediction obtained by the neural network model is less than that obtained by using the regression model.
机译：工具状况监控是机械微机械线的关键问题之一，用于高效制造几个行业的微零件。在本研究中，使用三轴加速度计，数据采集和信号处理模块和人工神经网络开发用于微钻孔的工具状况监测系统。在奥氏体不锈钢（（X5Crni 18-10）工件上进行微钻探实验，具有500μm直径微钻。安装在连接到工件上的传感器板上的三轴加速度计，以便在三个中收集振动信号钻井期间的方向。估计代表工具磨损变化的时域“均方方”特征估计了所有三个方向的振动信号。利用不同切割条件下越来越多的孔来研究RMS微钻振动的变化。开发了一种人工神经网络（ANN）模型以使所有三个方向振动信号，主轴速度和馈电参数的RMS值熔化，以预测钻孔孔数。用ANN模型获得的预测钻孔号是良好的协议实验获得的钻孔孔数。还显示了神经网络获得的孔数预测误差模型小于通过使用回归模型获得的模型。

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