厚截面CFRP孔隙超声脉冲反射检测方法

目前对碳纤维复合材料(carbon fiber reinforced plastic,简称CFRP)孔隙缺陷超声检测识别的研究主要以薄板型CFRP为对象,针对厚截面CFRP孔隙缺陷检测的需要,提出了基于超声脉冲反射背散射信号处理的孔隙缺陷识别方法。背散射信号特征分析结果表明,背散射信号由材料近表面共振结构噪声、信号指数型衰减成分、孔隙的反射和散射信号以及随机噪声组成。为得到孔隙的反射和散射信号,首先,利用提升小波变换良好的去噪能力除去背散射信号中的随机噪声;其次,设计低通滤波和自适应滤波分别除去信号中的共振结构噪声和衰减成分。对实验信号的处理结果表明,上述处理方法可以有效去除相应信号成分。在此基础上进一步提出了背散射信号幅值C扫描成像方法,将该成像方法应用于厚截面CFRP孔隙缺陷识别,可以有效识别试块中的含孔隙区域。

Study of Ultrasonic Pulse Echo Method for Voids Test in Thick-Section CFRP

Current research of void defects testing and identification for carbon fiber reinforced plastic (CFRP) mainly focuses on sheet-shaped CFRP, not thick-section CFRP. To fill this need, this paper proposes a void defects identification method for thick-section CFRP based on ultrasonic pulse echo backscattered signal processing. The analysis results of backscattered signal characteristics show that the backscattered signal is composed of resonance structure noise, the exponential decay signal, the reflection and scattering signal caused by voids, and stochastic noise. First, in order to obtain the reflection and scattering signal caused by voids, the stochastic noise is removed by lifting wavelet transform, which has good denoising ability. Then, the low pass filter and adaptive filter are designed to remove the influence caused by, respectively, resonance structure noise and the decay signal. The processing results of the experimental signal show that the above processing method can effectively remove corresponding signal components. Based on these signal processing methods, the C-scan imaging of the ultrasonic backscattered signal amplitude is designed. The experimental results show that regions containing voids in the CFRP workpiece can be determined effectively when the voids recognition method is used to test a thick-section CFRP workpiece.