In order to monitor the wear states of the tool during friction stir welding process online ,we designed a set of acoustic emission online monitoring system based on the virtual instrument technology . The system ,which combined with acoustic emission sensors ,data acquisition card and signal condition-er ,can collect acoustic emission signal during the friction stir welding process .In the experiment ,7075 aluminum alloy was welded by the needle stirring tool with screw thread ,and the acoustic emission sig-nal during welding was collected by the self-developed online monitoring system .Then the collected acoustic emission signals were processed by wavelet packet decomposition ,the percentage of each fre-quency band after decomposition was calculated ,and the energy distribution rule was extracted as the signal feature .The result shows that friction stir welding tools have different acoustic emission signal characteristics under different wear conditions .And the wavelet packet decomposition shows that when the wear of the mixing head is slight ,the proportion of energy in the low frequency band is high ;on the contrary ,the energy of the low frequency band accounts for a relatively low proportion when the wear of the mixing head is serious .%为在线监测搅拌摩擦焊接过程工具磨损状态,本文基于虚拟仪器技术设计了一套声发射在线监测系统,结合声发射传感器、数据采集卡及信号调理器实现了对搅拌摩擦焊接加工过程中声发射信号的采集.实验中采用带有螺纹的搅拌针工具来焊接7075铝合金,并利用自主开发的在线监测系统采集焊接过程中的声发射信号.然后对采集到的声发射信号进行小波包分解处理,计算分解后各频段能量所占百分比,并提取能量分布规律作为信号特征.研究表明:搅拌摩擦焊接工具在不同磨损情况下具有不同的声发射信号特征,小波包分解处理表明搅拌头磨损轻微时,低频段能量占比较高;相反,搅拌头磨损严重时,低频段能量占比较低.
展开▼
