氧化铝陶瓷磨削金刚石砂轮磨损的声发射监测

阐述了声发射监测工程陶瓷磨削的研究进展,发现目前对金刚石砂轮磨损监测研究基本上是选取声发射信号均方根(即有效值)进行分析,且金刚石砂轮磨损状态的声发射监测准确率不高.为提高金刚石砂轮磨损状态的声发射监测准确率,设计了氧化铝陶瓷磨削声发射实验,并采用支持向量机建立金刚石砂轮磨损状态的分类模型.分析发现氧化铝陶瓷精密磨削中声发射信号最强频谱能量在30~40kHz频段.金刚石砂轮轻度磨损、严重磨损钝化和修锐之后的磨削声发射信号频谱有明显不同;而且磨削声发射信号小波分解系数的方差值能够很好地反映金刚石砂轮磨损状态.结果表明采用磨削声发射信号的小波分解系数方差作为支持向量机判别金刚石砂轮磨损状态的输入特征,金刚石砂轮磨损状态分类测试的准确率达100%.

Acoustic Emission Monitoring of Diamond Wheel Wearwith Grinding Alumina Ceramics Grinding

Research progress of monitoring engineering ceramics grinding by acoustic emission (AE) is firstly described. It is found that the study of grinding diamond wheel wear monitoring is normally based on the analysis of the root mean square of acoustic emission signal, and the accuracy of acoustic emission monitoring is not high. In order to promote the accuracy of acoustic emission monitoring for wear states of grinding diamond wheel, support vector machine is applied to establish the classification model of grinding diamond wheel wear states. Through the analysis of acoustic emission signals, it is found that the strongest spectral energy of alumina ceramic acoustic emission signal under precision grinding is in 30~40 kHz band. The AE signal spectrum of the diamond grinding wheel wear states including mild wear, serious wear and after dressing are significantly different. The wavelet decomposition coefficient variances of the grinding acoustic emission signal can well reflect the wear states of the grinding diamond wheel. Therefore, the wavelet decomposition coefficient variances of grinding acoustic emission signal is applied as the input characteristics of support vector machine to identify grinding diamond wheel wear states, and the accuracy of classification test is 100%.