基于数学形态学的超声信号盲区内缺陷特征提取方法

利用高频聚焦超声技术检测金属材料内部缺陷时,由于超声探头存在盲区,无法对近表面的缺陷进行准确定位,从而难以对材料的性能进行有效评估。因此,提出基于数学形态学的超声信号盲区内缺陷特征提取方法。采用扁平结构元素对盲区信号进行形态滤波,在提取出缺陷的特征信号后,通过计算特征信号的累积能量,由此可以定位出缺陷的深度位置。在实测中,利用100 MHz的高频超声探头对冷轧镀锌板进行检测分析。以长度为30的扁平结构元素对超声信号做形态滤波,对距离冷轧镀锌板上表面288.5μm的缺陷进行定位,得到缺陷位置为距离上表面275.6μm,相对误差为4.5%。为进一步验证方法的有效性,与小波包分解重构方法进行比较,结果表明:利用数学形态学方法对缺陷的定位误差更小,同时还改善了超声B扫成像的效果,使得缺陷特征更加凸显。

Defect Feature Extraction in Ultrasonic Blind Zone Based on Mathematical Morphology

When the high frequency focused ultrasound technology is used to detect internal defects in metal materials, the defects near the surface can’t be accurately located due to the blind zone. Thus it is difficult to evaluate the performance of the material. Therefore, the method of the defect feature extraction in ultrasonic blind zone based on mathematical morphology is proposed. Firstly, signal of the blind zone is processed by morphological filter via the flat structure element, so the characteristic signal of the defect is extracted. After that, the accumulated energy of the characteristic signal is calculated, which can be used to locate the depth of the defect. In the experiment, the cold rolled galvanized sheet is detected using a high frequency focused ultrasonic transducer of 100 MHz. The ultrasonic signal is processed with morphological filter via the flat structuring elements with length of 30. The distance between the defect and the surface of the material is 288.5μm, and the position of the defect is 275.6μm determined by the proposed method, so the relative error of the defect position is 4.5%. In order to verify the effectiveness of the method, wavelet packet method is compared. The results show that the method of mathematical morphology has smaller error and improves the B-scan imaging effect. So the defect feature can be more obvious.