刀具振动无线检测系统研究

Study on Wireless Detection System of Tool Vibration

针对现有刀具振动无线检测系统中传感装置的布置对刀柄改造较大、成本较高等问题,以传感装置低功耗、小型化为设计理念,采用低功耗蓝牙5.0、MEMS传感器及Python,设计了一种用于铣削加工中的刀具振动无线检测系统。传感装置测试及铣削实验结果:相较于已有同类传感装置,系统传感装置保证信号传输性能的同时,降低了功耗,实现了小型化;提出的基于融合指标的OVMD-双树复小波降噪方法的去噪效果优于小波阈值等其他常用降噪方法;与现有无线系统相比,设计的无线系统使用同一CNN模型进行刀具磨损状态识别的准确率更高。结果表明设计的无线系统更具实用性的同时,能有效地保留振动信号中的特征,为刀具磨损状态识别提供了一种获取振动信号的可靠方案。

Aiming at the problems that the layout of the sensor device in the existing wireless monitoring system for tool vibration has a large effect on the transformation of the tool holder and the high cost,a kind of wireless detection system of tool vibration used in milling based on the low power consumption and miniaturization of the sensor device is designed by adopting the lowpower Bluetooth 5.0,MEMS sensor and Python.Sensing device testing and milling Comparing with the existing similar sensing devices,the system sensing device ensured signal transmission performance,reduced power consumption,and achieved miniaturization;the denoising effect of the present variational model decomposition-dual tree complex wavelet denoising method based on the fusion index is better than other common denoising methods such as wavelet threshold;comparing with the existing wireless system,the designed wireless system used the same convolutional neural networks model to recognize the tool wear status with higher accuracy.The results showed that the designed wireless system was more practical and can effectively retain the characteristics of the vibration signal,provideing a reliable way to obtain vibration data for tool wear status recognition.