小孔隙率碳纤维复合材料的富树脂超声检测

曾祥; 杨辰龙; 周晓军; 滕国阳

doi:10.3788/OPE.20182611.2732

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小孔隙率碳纤维复合材料的富树脂超声检测

微纳技术与精密机械 | 更新时间：2020-07-05

- 小孔隙率碳纤维复合材料的富树脂超声检测
- Ultrasonic detection of rich-resin in low-porosity CFRP
- 光学精密工程 2018年26卷第11期页码：2732-2743
- 作者机构：
  
  1.浙江大学流体动力与机电系统国家重点实验室, 浙江杭州 310027
  2.中车株洲电力机车研究所有限公司, 湖南株洲 412001
- 作者简介：
  
  [ "曾祥(1989-), 男, 湖南株洲人, 博士研究生, 2012年于武汉大学获得学士学位, 主要从事超声检测及自动化方面的研究。E-mail:zzjjuu0104@163.com" ]
  [ "杨辰龙(1974-), 男, 山西运城人, 博士, 副教授, 1999年、2002年于太原科技大学分别获得学士、硕士学位, 2005年于浙江大学获得博士学位, 主要从事复合材料超声检测及成像方面的研究。E-mail:yclzju@163.com" ]
- 基金信息：
  
  浙江省自然科学基金资助项目(LY18E050002);中央高校基本科研基金资助项目(2018QNA4001)
- DOI：10.3788/OPE.20182611.2732
  中图分类号： TB553
- 收稿日期：2018-03-30，
  
  录用日期：2018-4-25，
  
  纸质出版日期：2018-11-25
- 稿件说明：
移动端阅览
曾祥, 杨辰龙, 周晓军, 等. 小孔隙率碳纤维复合材料的富树脂超声检测[J]. 光学精密工程, 2018,26(11):2732-2743.

Xiang ZENG, Chen-long YANG, Xiao-jun ZHOU, et al. Ultrasonic detection of rich-resin in low-porosity CFRP[J]. Optics and precision engineering, 2018, 26(11): 2732-2743.
曾祥, 杨辰龙, 周晓军, 等. 小孔隙率碳纤维复合材料的富树脂超声检测[J]. 光学精密工程, 2018,26(11):2732-2743. DOI： 10.3788/OPE.20182611.2732.

Xiang ZENG, Chen-long YANG, Xiao-jun ZHOU, et al. Ultrasonic detection of rich-resin in low-porosity CFRP[J]. Optics and precision engineering, 2018, 26(11): 2732-2743. DOI： 10.3788/OPE.20182611.2732.

摘要

针对孔隙率接近0的小孔隙率碳纤维复合材料（Carbon Fiber Reinforced Composite，CFRP）的富树脂检测需求，提出富树脂超声检测技术。对超声检测信号中的噪声消除方法、衰减抑制方法和富树脂检测的多视图成像技术进行研究，并开发小孔隙率CFRP富树脂超声检测软件。首先提出共振频率估计方法，通过低通滤波抑制高频随机噪声。其次根据频率差异，应用变分模态分解算法分离并消除共振结构噪声，提取低频成分。该低频成分包括表面回波、底面回波、富树脂反射信号和由层间反射信号、材料散射噪声等构成的相干噪声。再次，引入瞬时幅值比修正低频成分的幅值衰减并描述被检测小孔隙率CFRP的局部反射能力。最后，应用Otsu多阈值方法自适应获得富树脂识别的阈值，消除相干噪声的影响，完成富树脂识别。进一步对小孔隙率CFRP的超声检测结果进行多视图成像，在三维视图、C扫描视图和B扫描视图内识别富树脂。结果表明：变分模态分解的分量数为2，Otsu多阈值的类别数为3时，能够准确识别小孔隙率CFRP超声检测信号中的富树脂反射信号；采用0.15作为多视图成像的阈值，可简洁有效地描述富树脂在小孔隙率CFRP中的分布。

Abstract

To satisfy the demand of rich-resin defect detection in the so-called low-porosity carbon fiber reinforced composite (CFRP) with porosity close to zero

an ultrasonic testing methodology was proposed in this article. The denoising methods

attenuation suppression method

and 3D imaging technology for rich-resin identification are investigated

and low-porosity CFRP rich-resin detection software was developed. The rich resin was detected in four steps. First

the resonant frequency was estimated

and the high-frequency stochastic noise was suppressed. Second

variational mode decomposition (VMD) was used to separate the resonant structure noise and extract the low-frequency component. The low-frequency component consisted of the front-wall echo

back-wall echo

rich-resin reflection signal

and remaining coherent noise made up of the interlayer reflection signals and material scattering noise. Third

the instant amplitude ratio was introduced to correct the envelop attenuation of the low-frequency component and describe the local reflectivity of the low-porosity CFRP. Finally

the multi-threshold Otsu method was used to search the threshold of the rich-resin detection

resulting in the elimination of interference and finishing the detection of rich resin. Further

multi-view imaging was performed on the test results

and the rich resin was identified in the 3D

C-scan

and B-scan imaging processes. The experimental results show that when the VMD mode was set to two and the classes in the multi-threshold Otsu method are set to three

a rich-resin reflection signal can be detected. When the threshold in the multi-view imaging is set to 0.15

the rich resin can be effectively characterized.

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references

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