Low-velocity impact damage characterization of CFRP composite based on infrared thermography
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摘要: 采用红外热成像检测技术对20 J、40 J冲击载荷下的碳纤维增强树脂基(CFRP)复合材料层合板表面与内部损伤进行识别。针对内部缺陷定量提取不准确的问题,通过分析红外图像空域特征,提出一种多尺度八方向边缘检测图像分割算法。首先依据敏感区域最大标准差法选取最优图像并采用模糊C均值聚类算法对红外缺陷图像进行预分割获取先验信息,然后构建圆形卷积模板对红外图像进行多尺度八方向卷积运算,引入OTSU算法分割梯度图像,结合形态学运算得到缺陷边缘图,对目标区域进行连通域分析,实现缺陷特征的定量提取。研究结果表明,本文算法提高了损伤区域弱边缘的检测能力,保证了缺陷边缘的完整性与连通性,相较于传统图像分割方法在缺陷面积、长径、短径的检测精度上分别提升了20.41%、5.61%、9.77%以上。Abstract: The surface and internal damage of carbon fiber reinforced polymer (CFRP) laminates under 20 J and 40 J impact loads were identified by infrared thermography. Aiming at the inaccuracy of quantitative defect extraction, a multi-scale eight-direction edge detection image segmentation algorithm was proposed by analyzing the spatial characteristics of infrared images. Firstly, the optimal image was selected according to the maximum standard deviation of the sensitive area, and the fuzzy C-means clustering algorithm was used to presegment the defect image to obtain prior information. Then a circular convolution template was constructed to perform a multi-scale eight-direction convolution operation on the infrared image. The OTSU algorithm was introduced to segment the gradient image, combined with morphological operations to obtain the defect edge map, and the connected domain of the target area was analyzed to realize the quantitative extraction of defect features. The results show that the proposed algorithm improves the detection ability of weak edges in the damaged area and ensures the integrity and connectivity of defect edges. Compared with the traditional image segmentation algorithm, the detection accuracy of the defect area, long diameter and short diameter obtained by the proposed algorithm are improved by more than 20.41%, 5.61% and 9.77%, respectively.
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表 1 CFRP缺陷特征定量提取结果
Table 1. Quantitative analysis results of defect characteristics of CFRP
Method Area/mm2 Long diameter/mm Short diameter/mm 20 J 40 J 20 J 40 J 20 J 40 J C-Scan 788.40 2356.23 43.70 83.60 23.94 40.30 OTSU 533.70 1657.92 38.14 73.94 19.36 31.69 Iterative threshold 530.63 1648.39 38.03 73.72 19.31 31.60 Genetic algorithm 539.19 1660.37 38.62 73.74 20.54 32.17 Region growing 492.31 1596.08 36.83 73.17 18.29 30.31 In this article 759.95 2141.47 41.07 79.20 22.88 37.55 表 2 不同算法处理结果的相对误差
Table 2. Relative errors of the results processed by different algorithms
Algorithms Relative error of area/% Relative error of long diameter/% Relative error of short diameter/% 20 J 40 J 20 J 40 J 20 J 40 J OTSU 32.31 29.64 12.72 11.56 19.13 21.36 Iterative threshold 32.70 30.04 12.97 11.82 19.34 21.59 Genetic algorithm 31.61 29.53 11.63 11.79 14.20 20.17 Region growing 37.56 32.26 15.72 12.48 23.60 24.79 In this article 3.61 9.11 6.02 5.26 4.43 6.82 -
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