复合材料缺陷面积准确量化是缺陷自动识别、自动分析的重要环节，对研究复材制造工艺性能具有指导作用。本研究基于CFRP的超声相控阵无损检测数据开展研究，分析比较了迭代阈值法、大津法两种图像分割方法对缺陷面积量化结果，结合超声A扫描信号与C扫描图像，提出一种半波高度四邻域缺陷量化算法，定位连通缺陷区域的超声信号最大幅值点，以最大幅值点处一半幅值为临界判断，扩展四邻域缺陷边界搜索，确定缺陷边界。与图像分割方法相比，精度有较大提高，针对于较小缺陷，量化的缺陷面积与实际缺陷面积的偏差为13.2%，其余缺陷面积量化偏差均小于2%。
Abstract
The accurate area quantification of composite defects is an important part of automatic defect identification and analysis, which has a guiding role in the study of composite manufacturing process performance. In this study, based on the ultrasonic phased array nondestructive testing data of CFRP, the results of the defect area quantification were analyzed and compared by the iterative threshold method and the Otsu method, and a four-neighborhood defect quantification algorithm was proposed by combining the ultrasonic A-scan signal and the C-scan image, which located the ultrasonic signal of the connected defect area, and took half of the amplitude at the maximum amplitude of the largest amplitude as the critical judgment to expand the four-neighborhood defect boundary search and determine the defect boundary. Compared with the image segmentation method, the accuracy is greatly improved, and for small defects, the deviation between the quantized defect area and the actual defect area is 13.2%, and the quantization deviation of the remaining defect area is less than 2%.
关键词
复合材料 /
缺陷量化 /
图像分割 /
半波高度法
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Key words
composites /
defect quantification /
image segmentation /
half-wave height method
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参考文献
[1] Santos M, Santos J, Reis P, et al. Ultrasonic C-scan techniques for the evaluation of impact damage in CFRP[J]. Materials Testing, 2021, 63(2): 131–137.
[2] Ilangovan S, Kumaran S S, Naresh K, et al. Studies on glass/epoxy and basalt/epoxy thin-walled pressure vessels subjected to internal pressure using ultrasonic “C” scan technique[J]. Thin-Walled Structures, 2023, 182: 110160.
[3] 李雄兵，周晓军，吴思源，等．超声C扫描图像缺陷标记及边缘跟踪的研究[J].传感技术学报，2006(66):2690-2693．
LI Xiong-bing, ZHOU Xiao-jun, WU Si-yuan, et al. Research on defect labeling and edge tracking of ultrasound C-scan images[J]. Journal of Sensing Technology, 2006(66):2690-2693．
[4] 陈怀东，曹宗杰，张柯柯，等．基于遗传算法的超声检测图像分割识别方法[J].西安交通大学学报，2003(01):22-25.
CHEN Huai-dong, CAO Zong-jie, ZHANG Ke-ke, et al. An image segmentation and recognition method for ultrasonic testing based on genetic algorithm[J]. Journal of Xi'an Jiaotong University, 2003(01):22-25.
[5] 刘继忠，朱根兴，周晓军，等．基于Haralick算法的超声图像边缘特征提取[J].无损检测，2005(05):228-230.
LIU Ji-zhong, ZHU Gen-xing, ZHOU Xiao-jun, et al. Edge feature extraction of ultrasound images based on Haralick algorithm[J]. Non-destructive testing, 2005(05):228-230.
[6] Song W, Ren J, He P, Sun J, et al. Quantitative determination of the defects in TC4 diffusion bonded joints via ultrasonic C-scan[J]. Journal Of Manufacturing Processes, 2021, 64: 1476–1483.
[7] 李万达. 基于超声波的新型轻质陶瓷基复合材料粘接缺陷检测研究[D]. 秦皇岛：燕山大学, 2020.
LI Wan-da. Research on the detection of bonding defects of new lightweight ceramic matrix composites based on ultrasound[D]. Qinhuangdao: Yanshan University,2020.
[8] 吕洪涛, 李 锋, 刘志毅等.基于超声C扫描数字图像处理的缺陷面积分析[J].无损检测,2022,44(12):37-41.
LU Hong-tao, LI Feng, LIU Zhi-yi, et al. Defect Area Analysis Based on Ultrasonic C-Scan Digital Image Processing [J]. Nondestructive Testing, 2022, 44(12): 37-41.
[9] FANG Y, CHEN Z M, YANG X H, et al. Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing[J]. NDT & E International,2024,141:102975.
[10] Sanjeevareddy K, Christian H, Jens P, et al. Quantitative evaluation of ultrasonic C-scan image in acoustically homogeneous and layered anisotropic materials using three dimensional ray tracing method, [J]. Ultrasonics,2014,54(2):551-562.
[11] 王秀菲. 基于特征加权支持向量机的复合材料粘接缺陷量化识别研究[D]. 内蒙古：内蒙古大学, 2011.
WANG Xiu-fei. Research on Quantitative Identification of Composite Bonding Defects Based on Feature-weighted Support Vector Machine[D]. Inner Mongolia:Inner Mongolia University, 2011.
[12] Mathew J, Raghu V P. Quantification of fatigue damage in carbon fiber composite laminates through image processing[J]. Materials Today: Proceedings,2018,5(9):16995-17005.
[13] MING H, ZHU M, Michael D T, et al. Uncertainty quantification for acoustic nonlinearity parameter in Lamb wave-based prediction of barely visible impact damage in composites[J]. Mechanical Systems and Signal Processing,2017,82:448-460.
[14] Neerukatti R K, Rajadas A, Borkowski L, et al. A hybrid method for damage detection and quantification in advanced X-COR composite structures[J]. Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems,2016,9803:980326.
[15] Gaétan P, Saeid H, Joost S, et al. Adaptive spectral band integration in flash thermography: Enhanced defect detectability and quantification in composites[J]. Composites Part B: Engineering,2020,285:108305.
[16] Michael D.C.Moles, Colin R. Bird, Pamela Herzog et al. Introduction to Phased Array Ultrasonic Technology Applications [M]. America, 2004: 177.
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