基于小波系数统计特征的光学元件粗糙度声发射监测研究

The development of acoustic emission monitoring system for the roughness of optical element based on statistical parameter of wavelet coefficients

表面粗糙度是光学元件表面质量的重要评价指标之一。传统的粗糙度检测方法大多采用离线方法,无法实现在线表征。为此讨论了一种基于声发射检测技术的表面粗糙度监测方法,利用改进的表面粗糙度检测装置,采集不同粗糙度下声发射信号;由于传统方法存在一定的局限性,因此提出了基于小波分解系数有效值统计特征的表面粗糙度监测方法,通过对摩擦抛光的声发射信号进行特征提取,来辨识粗糙度。研究结果表明,利用该方法所提取出的特征可以对表面粗糙度进行有效区分,验证了其是光学元件表面粗糙度声发射监测的有效方法。

Surface roughness is one of the important factors to affect the surface quality of optical elements. Currently,most of the traditional monitoring methods are performed as offline,which cannot characterize the surface roughness quickly. The paper proposes a surface roughness monitoring method based on acoustic emission detecting technique. Based on the statistical characteristics of the root mean square（ RMS） values of wavelet coefficients,it employs an enhanced surface roughness monitoring device to collect the acoustic emission signals from the surface of optical elements with the different roughness conditions,presents the surface roughness monitoring method to extract the features of the acoustic emission（ AE） signals and identify the surface roughness conditions. The results show that the features extracted by the proposed method can effectively distinguish the different surface roughness conditions and be superior to some conventional methods.