变厚度CFRP超声缺陷自动成像的相关性算法研究

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摘要针对变厚度碳纤维增强聚合物复合材料（carbon fiber reinforced polymer,CFRP）超声缺陷成像工序繁琐的问题，提出了一种基于相关性的自动成像算法。实验预埋夹杂缺陷，使用超声相控阵检测获取数据，首先依据离散序列互相关理论，对不同厚度CFRP的超声A扫描信号进行底波时移处理，之后利用自相关理论从无缺陷区域生成必要的参考信号。通过相关计算结果，利用欧氏距离区分缺陷信号和非缺陷信号，并依据欧式距离进行颜色编码绘图。最后通过基于机器视觉以及Hough圆变换的边缘检测算法来对缺陷尺寸进行统计测量，总体均值误差率小于7%，最大误差率为16.25%，最小误差率为0.25%。结果表明该算法可广泛应用于变厚度CFRP自动化超声检测。

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	王涛1
	邓婉欣2
	王海军3
	俞慈君1

关键词 ：变厚度复合材料, 超声检测, 时移, 自相关

Abstract：A correlation-based automatic imaging algorithm is proposed to address the labor-intensive process of variable thickness CFRP ultrasonic defect imaging. Embedded artificial defects are prepared in the specimens, and ultrasonic phased array detection is used to acquire data. Firstly, based on the theory of discrete sequence correlation, time-shift processing is applied to the ultrasonic A-scan signals of different thickness CFRP specimens to align the baseline. Subsequently, the necessary reference signals are generated from defect-free regions using autocorrelation theory. By analyzing the correlation results and utilizing the Euclidean distance, defect signals are distinguished from non-defect signals, and a color-coded image is generated based on the Euclidean distances. Ultimately, the defect size was statistically measured using an edge detection algorithm based on machine vision and the Hough circle transform. The overall mean error rate was less than 7%, with a maximum error rate of 16.25% and a minimum error rate of 0.25%. The results demonstrate that this algorithm can be extensively applied to the automated ultrasonic testing of variable thickness CFRP.

Key words： Variable thickness composites Ultrasonic testing Time-shifting Autocorrelation

收稿日期: 2023-09-19 出版日期: 2024-06-28

引用本文:

王涛1，邓婉欣2，王海军3，俞慈君1. 变厚度CFRP超声缺陷自动成像的相关性算法研究[J]. 振动与冲击, 2024, 43(12): 232-240.
WANG Tao1,DENG Wanxin2,WANG Haijun3,YU Cijun1. A study on correlation-based algorithms for automatic imaging of variable thickness CFRP ultrasonic defects. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(12): 232-240.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I12/232

[1] Mohammadkhani R, Fragonara L Z, Padiyar J M, et al. Ultrasonic Phased Array Imaging Technology for the Inspection of Aerospace Composite Structures[C]. 2019 IEEE 6th International Workshop On Metrology For Aerospace. New York: IEEE, 2019: 203-208. [2] Lee J-C, Park D-H, Jung H-S, Lee S H, et al. Design for Carbon Fiber Lamination of PMI Foam Cored CFRP Sandwich Composite Applied to Automotive Rear Spoiler[J]. Fibers and Polymers, 2020, 21(1): 156-161. [3] Zhang X, Yang H, Zhang H and Wang C. A Carbon Fiber Reinforced Nylon 6 (CFRPA6) Composite Specialized for Military Field Cooking Task[C]. Industrial Design And Mechanical Power. Durnten-Zurich: Trans Tech Publications Ltd, 2012: 199-203. [4] Slonski M, Schabowicz K and Krawczyk E. Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks[J]. Materials, 2020, 13(7): 1557. [5] Liu Y, Li X, Zhang G, Zhang S, et al. Characterizing Microstructural Evolution of TP304 Stainless Steel Using a Pulse-Echo Nonlinear Method[J]. Materials, 2020, 13(6): 1395. [6] Ryuzono K, Yashiro S, Nagai H, et al. Topology Optimization-Based Damage Identification Using Visualized Ultrasonic Wave Propagation[J]. Materials, 2020, 13(1): 33. [7] 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. [8] 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. [9] 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. [10] Patil S and Reddy D M. Impact damage assessment in carbon fiber reinforced composite using vibration-based new damage index and ultrasonic C-scanning method[J]. Structures, 2020, 28: 638–650. [11] Dong J, Xu G, Wei L, Fan G, et al. Ultrasonic C-scan detection research for effective connection area of arc-stud-weld joints[J]. International Journal Of Advanced Manufacturing Technology, 2019, 104(9-12): 4007–4021. [12] Bariant J-F and Milschewski T. Tracking Objects with Multiple Reflections based on Ultrasonic Transducer Raw Data Using a PDAF[C]. 2017 20th International Conference On Information Fusion (Fusion). New York: IEEE, 2017: 828-835. [13] Li S, Poudel A and Chu T P. Ultrasonic Defect Mapping Using Signal Correlation for Nondestructive Evaluation (nde)[J]. Research In Nondestructive Evaluation, 2015, 26(2): 90–106. [14] Ma M, Cao H, Jiang M, et al. High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm[J]. Materials, 2020, 13(17): 3840 [15] Vandendriessche J, Orta A H, Verboven E, et al. Probabilistic ultrasound C-scan imaging of barely visible impact damage in CFRP laminates[J]. Composite Structures, 2022, 284: 115209. [16] Li X, Wang Y, Ni P, et al. Flaw sizing using ultrasonic C-scan imaging with dynamic thresholds[J]. Insight, 2017, 59(11): 603-608. [17] Martinez A, Gueemes A, Perales J M, et al. Variable Thickness in Plates-A Solution for SHM Based on the Topological Derivative[J]. Sensors, 2020, 20(9): 2529. [18] Wang Y Y. Research on sensitivity automatic compensation technique for ultrasonic C-scan of variable thickness part[C]. 2005: 6th International Symposium on Test and Measurement, Vols 1-9, Conference Proceedings. Hong Kong: International Academic Publishers Ltd, 2005: 60-63. [19] Zheng H F, Lv J M, Yang N, et al. Research on Automatic Ultrasonic Waveform Tracking for Curved Surface Composite Parts with Variable Thickness[J]. Acta Metrologica Sinica, 2012, 33(5): 400-404. [20] 吴思源,周晓军,李雄兵等.变厚度航空锻件超声C扫描技术研究[J]. 传感技术学报, 2006(04): 1052-1055. WU Si-yuan, ZHOU Xiao-jun, LI Xiong-bing, et al. Study on Ultrasonic C-Scan Technique for Variation Thickness Aviation Forged Piece[J]. Chinese Journal Of Sensors And Actuators, 2006(4): 1052-1055. [21] 吴玄,周世圆,张翰明等.变壁厚回转体零件超声检测动态闸门技术研究[J]. 中国测试, 2018, 44(05): 103-107. WU Xuan, ZHOU Shi-yuan, ZHANG Han-ming, et al. Research on ultrasonic dynamic gate technology for inspection of rotational parts with variable thickness[J]. China Measurement & Test, 2018, 44(5): 103-107. [22] Hasiotis T, Badogiannis E, Tsouvalis N G. Application of Ultrasonic C-Scan Techniques for Tracing Defects in Laminated Composite Materials[J]. Strojniski Vestnik-Journal of Mechanical Engineering, 2011, 57(3): 192-203. [23] Wronkowicz A, Dragan K, Lis K. Assessment of uncertainty in damage evaluation by ultrasonic testing of composite structures[J]. Composite Structures, 2018, 203: 71-84. [24] 杨辰龙. 曲面变厚度工件超声检测中的波形自动跟踪技术研究[C]. 中国自动化学会控制理论专业委员会.中国自动化学会控制理论专业委员会C卷, 2011: 4. YANG Chen-long. Automatic Waveforms Track Research on Ultrasonic Inspection of Curved Composite Surface[C]. Proceedings of the 30th Chinese Control Conference. Yantai: [s. n.], 2011: 1274-1277. [25] Ding W, Gu J, Tang S, et al. Development of a calibrating algorithm for Delta Robot’s visual positioning based on artifificial neural network[J]. Optik-International Journal for Light and Electron Optics, 2016, 127(20):9095-9104.