基于空频去混叠的CFRP材料纤维纹路涡流成像

Eddy Current Imaging of Fiber Textures in CFRP Composite Based on Spatial Frequency Domain De-aliasing Method

空间分辨率是图像质量评价的一项关键性指标,在涡流成像技术的应用中非常重要。针对碳纤维增强树脂基复合材料(Carbon fiber reinforced polymer,CFRP)电涡流成像中欠采样频谱混叠引起的纤维纹路粗大和模糊难以辨别等问题,研究基于空频域去混叠的涡流图像增强方法,显著提高图像的空间分辨率和对纤维排布以及缺陷的检测精度。建立CFRP层合板的细观电磁仿真模型,揭示涡流纹路成像机理和周期性结构信号的混叠规律;接下来对涡流探头的点扩散函数进行研究和估计,结合信号波形特征提取,评价不同结构尺寸发射-接收(Transmitter-Receiver,T-R)型探头图像的空间分辨率;提出基于点扩散函数和傅里叶变换的逆卷积纹路细化聚焦方法,以实现对CFRP层合板中纤维方向和铺层缺陷的高精度成像,并达到图像反映复合材料缺陷真实形状的目的。

The spatial resolution is a key indicator of image quality and is of great importance in the application of eddy current imaging technology. Concerning the aliasing problems including coarse and fuzzy fiber textures in eddy current imaging of carbon fiber reinforced polymer(CFRP) composites, the spatial-frequency domain image enhancement method is studied to significantly improve the detection accuracy of meso-structures and defects such as the fiber direction, in-plane waviness, wrinkles, and gaps in CFRP laminates. In this paper, the mesoscopic electromagnetic simulation model of CFRP laminate is firstly established to reveal the eddy current imaging mechanism and the aliasing law of periodic structure signals. Then the point spread function(PSF) of the eddy current probe is studied and estimated, and the signal feature extraction technology is used to evaluate the spatial resolution of the T-R probes with different sizes. Finally, a texture thinning and focusing method based on PSF and fast Fourier transform(FFT)de-convolution is developed to realize high-precision imaging of fiber direction and layup defects in CFRP laminates, and to achieve the purpose of reflecting the true shape of defects in composite structures.