基于声发射砂轮磨损监测系统的研究

磨削加工过程中砂轮出现磨损需要反复的修整,砂轮磨损状态的监测可以有效判别砂轮工作状态,减少砂轮修整次数。本文建立了一种基于声发射信号的砂轮磨损监测模型,提出了基于一种小波分解系数均方值统计分析的砂轮磨损状态特征提取方法。同时,采用BP神经网络对砂轮磨损状态进行识别,其输入为3种提取特征,输出为3种不同的砂轮磨损状态。通过磨削试验对监测系统进行评价。结果表明,所提出小波分解系数均方值统计分析的特征提取方法和砂轮磨损监测系统均具有良好的效果。

Research on Grinding Wheel Wear Monitoring System Based on Acoustic Emission

The wear of grinding wheel needs repeated dressing in the process of grinding. The monitoring of grinding wheel wear state can effectively distinguish the working state of grinding wheel and reduce the number of grinding wheel dressing. An monitoring model of grinding wheel wear based on acoustic emission signal is established, and a method of grinding wheel wear state feature extraction based on statistical analysis of wavelet decomposition coefficient mean square value is proposed. At the same time, BP neural network is used to identify the wear state of grinding wheel, which input is three kinds of extraction features and the output is three different grinding wheel wear states. The monitoring system is evaluated by grinding tests. The results show that the proposed acoustic emission signal feature extraction method based on the statistical analysis of average wavelet decomposition coefficient and the monitoring system of grinding wheel wear have good results.