超声检测中人工神经网络对缺陷定量评价

分析了均匀各向同性介质的超声检测中,耦合因素和仪器因素造成的脉冲回波幅度的差异,提出了误差校正方法。以校正后的缺陷回波和底面反射波的峰-峰值为特征量,利用人工神经网络进行缺陷类型识别和大小评价。
为模拟自然缺陷的二基本要素——光滑曲面和带棱边的平面,用有机玻璃制作了代表性的横穿孔和平底孔缺陷样品共18个.对18个缺陷样品的缺陷回波和底面反射波的峰-峰值测量了四次,并进行的校正.用人工神经网络对这组缺陷样品进行的处理结果表明:(1)设定的缺陷类型全部准确识别。(2)估计缺陷大小与标称孔径吻合较好。最后,对测量误差和缺陷大小估计误差进行了分析。

Ultrasonic quantitative flaw evaluation using neural networks

A correction method is presented to eliminate echo amplitude deviation from insufficient coupling andinstrument factor in ultrasonic evaluation of a homegeneous solid.The flaws are then be evaluated by neural networkson using the maximum peak-peak values of the flaw echoes and bottom echoes as characteristic features.
In order to simulate two essential elements of nature flaw-smooth surface and plane with sharp edge,18 representative flaw samples with traverse cylindrical cavity and flat-bottom hole respectively are made.The maximum peak-peakvalues of the echoes of the samples are measured four times,and corrections are made to the data.
The experiment results show that flawes can be classified correctly and sized well.Finally,the errors of themeasurement and the flaw size estimation are analysed.