GFRP层压板脱黏缺陷的红外脉冲热波成像检测

针对玻璃纤维增强塑料层压板脱黏缺陷的红外无损检测问题,首先制备了一种人工脱黏缺陷试样,采用红外脉冲热波成像检测技术对脱黏缺陷进行检测,分析了层压板脱黏区和非脱黏区的表面热信号瞬态响应过程,以图像信噪比和标准化对比度作为评价指标,定量对比了热信号重构、复调制Zoom-FFT、改进的独立分量分析和主分量分析4种热图重构算法在脱黏缺陷识别中的作用.在此基础上,提出基于热信号重构增强的主分量分析算法,并验证了该算法在脱黏缺陷识别中的作用.研究表明:4种热图重构算法均可提高层压板脱黏缺陷定量识别能力,其中以热信号重构对提高缺陷区与非缺陷区对比度最为显著,主分量分析对热图噪声的抑制能力最强,基于热信号重构增强的主分量分析能够显著提高深度分别为0.5mm、1.0mm、1.5 mm的脱黏缺陷定量识别能力.

Detection of Unbounded Defects in GFRP Laminates by Infrared Pulse Thermal Wave Imaging

In view of the problem of infrared non destructive testing for unbounded defects of glass fiber reinforced plastic laminates, a kind of artificial unbounded defect sample is prepared firstly. The unbounded defects are detected by infrared pulse thermal wave imaging technology, and the transient response process of surface thermal signals in unbounded area and non debonding area of laminates is analyzed. The image signal noise ratio and normalized contrast are used as evaluation indexes to quantitatively analyze the effects of four thermal image reconstruction algorithms, including thermography signal reconstruction, complex modulation Zoom FFT, improved independent component analysis (ICA) and principal component analysis (PCA), and the function of the unbounded defect recognition. On this basis, the PCA algorithm based on thermal signal reconstruction enhancement is proposed, and the effect of the algorithm in unbounded defect recognition is verified. The results show that the four thermal image reconstruction algorithms can improve the quantitative identification ability of unbounded defects, in which the thermal signal reconstruction is the most significant to improve the contrast between the defect area and the non defect area, and the principal component analysis has the strongest ability to suppress the thermal image noise; and the principal component analysis based on the enhancement of thermal signal reconstruction can significantly improve the quantitative identification ability of unbounded defects with the depth of 0.5 mm, 1.0 mm and 1.5 mm.