基于最小熵解卷积的汽车点焊质量超声评价

利用超声信号的时域信息，可以对焊点质量进行评价.白车身焊点超声检测的过程中，由于噪声信号的模糊作用，反映焊点质量的有用信息被掩盖，导致焊点质量评价结果不准确.超声探头接收到的超声信号可视为原始超声信号与噪声信号两种信号的叠加，基于超声信号的卷积模型及稀疏特性，可利用最小熵盲反卷积（MED）对超声信号进行解卷积处理，分离重叠的焊点超声信号，恢复信号反射系数，获取准确的回波个数.通过仿真与试验，结果表明，最小熵解卷积对焊点重叠超声信号分离的有效性.

Ultrasonic evaluation of automobile spot welding based on minimum entropy deconvolution

Using time domain information of the ultrasonic signal can evaluate the quality of spot welding. In the BIW ultrasonic testing process, because of the fuzzy function of the noise signal, the useful information of the welding spot quality is covered up, which leads to the inaccurate result of the spot welding quality evaluation. The ultrasonic signal received by ultrasonic probe can be regarded as the superposition of original ultrasonic signal and noise signal, based on convolution model of the ultrasonic signal and sparse features, combined with minimum entropy deconvolution blind ultrasonic signal convolution, separating overlapping spot welding ultrasonic echo signal and restore the reflection coefficient, to obtain an accurate number of echo. After simulation and experiments, the result proved the effectiveness of the minimum entropy deconvolution on the separation of the overlapped ultrasonic signals.