厚截面碳纤维复合材料远表面微缺陷超声检测

为了识别厚截面碳纤维复合材料(CFRP) 远表面的微缺陷, 使用递归分析方法对超声检测信号进行分析。首先在厚截面CFRP材料上打孔以模拟微缺陷, 采用水浸超声脉冲反射法对不同大小的模拟缺陷进行检测。然后选取缺陷位置附近信号段, 确定嵌入维数m、延迟时间τ、阈值ε等参数, 对各信号段进行递归分析, 得到递归图及递归定量分析结果。比较无缺陷信号和有缺陷信号的递归图, 从宏观上定性确定微缺陷对超声信号的影响; 比较无缺陷信号和有缺陷信号的递归定量分析结果, 根据每个递归定量参数的物理意义, 对缺陷产生的影响作出合理的解释。最后, 使用不同中心频率探头进行实验, 确定合适的探头参数。分析结果表明, 使用7.5 MHz高分辨率超声探头时检测效果最好; 当嵌入维数为7、延迟时间为2、阈值为2时, 递归图中出现异常白色区域、递归点增多且对角线结构变长, 同时所选取的递归定量参数随缺陷增大而上升, 表明厚截面CFRP远表面超声信号可能存在混沌结构, 而微缺陷的存在会改变原有信号结构。所研究内容为实际微缺陷的定量识别及分类打下基础。

Abstract

To detect micro defects in thick-section carbon fiber reinforced composite (CFRP), the recurrence analysis method was used to analyze ultrasonic signals of tested CFRP. First, small holes were made to simulate micro defects, and an ultrasonic pulse echo method was adopted to test these simulated defects of different sizes. Then, the signal segments around the defect position were selected, and recurrence analysis was performed after proper parameters like embedding dimension(m), time delay(τ), and threshold(ε) were chosen. The recurrence plots (RPs) of defect-free signals were compared with those of defective ones and, according to the physical meanings of recurrence quantification analysis (RQA) variables, the changes that appeared in RPs were explained. Finally, ultrasonic transducers with different frequencies were evaluated to determine which one has the best performance. The results show that a 7.5 MHz resolution series transducer is the best choice in our experiment, and while m=7, τ=2, and ε=2 , the defects may cause dark areas, white bands, and longer diagnosis structure in RPs and correspondingly larger RQA variables. More concretely, the ultrasonic signal of defect-free thick-section CFRP appears in a chaotic state, while defects may break this state and lead to another one. The results will lay a foundation for the quantitative identification and classification of real micro defects.

参考文献

[1]

SMITH R A, NELSON L J, MIENCZAKOWSKI M J, et al. Automated analysis and advanced defect characterisation from ultrasonic scans of composites ［J］. Insight-Non-Destructive Testing and Condition Monitoring , 2009, 51(2): 82-87.

[2] 郭治文, 施晓春, 沈洋, 等. 固化炉成型复合材料孔隙率超声检测方法研究［J］. 航空制造技术, 2017, 524(5): 49-53.

GUO Z W, SHI X CH, SHEN Y, et al.. Ultrasonic testing study for autoclave cured composite material porosity ［J］. Aeronautical Manufacturing Technology, 2017, 524(5): 49-53. (in Chinese)

[3] 陈越超.基于超声背散射信号处理的碳纤维复合材料孔隙检测研究［D］.浙江: 浙江大学, 2016.

CHEN Y CH. Research on Voids Testing for Carbon Fiber Reinforced Plastics Based on Ultrasonic Backscattered Signal Processing［D］. Zhejiang: Zhejiang University, 2016.(in Chinese)

[4] 杨辰龙, 陈越超, 叶钱, 等. 金属材料小缺陷超声反射信号建模及识别［J］. 光学精密工程, 2015, 23(9): 2635-2644.

YANG CH L, CHEN Y CH, YE Q, et al.. Ultrasonic echo signal modeling and identification for minor defects in metallic materials ［J］. Opt. Precision Eng., 2015, 23(9): 2635-2644 (in Chinese)

[5] ECKMANN J P, KAMPHORST S O, RUELLE D. Recurrence plots of dynamical systems［J］. Europhys Lett, 2007, 4(9): 973-977.

[6] ZBILUT J P, Jr C L W. Embeddings and delays as derived from quantification of recurrence plots［J］. Physics Letters A, 1992, 171(3-4): 199-203.

[7] ANGELA D. Characterization of Dynamic Phenomena Based on The Signal Analysis in Phase Diagram Representation Domain［D］.Universite Grenoble Alpes, 2017.

[8] TAKENS F. Detecting strange attractors in turbulence［J］. Lecture notes in mathematics, 1981, 898(1): 366-381.

[9] MARWAN N, WESSEL N, MEYERFELDT U. Recurrence plot based measures of complexity and their applications to heart rate variability data［J］.Physical Review E, 2002, 66 (7): 70-76.

[10] 杨栋, 任新伟．基于递归分析的振动信号非平稳性评价［J］.振动与冲击, 2011, 30(12): 39-43．

YANG D, REN X W. Non-stationarity evaluating for vibration signals using recurrence plot ［J］. Journal of Vibration and Shock, 2011, 30(12): 39-43．(in Chinese)

[11] Jr C L W, IOANA C, MARWAN N. Recurrence Plots and Their Quantifications: Expanding Horizons［M］. Springer International Publishing, 2016.

[12] BRANDT C. Recurrence Quantification Analysis as an Approach for Ultrasonic Testing of Porous Carbon Fibre Reinforced Polymers［M］.Recurrence Plots and Their Quantifications: Expanding Horizons. Springer International Publishing, 2016.

[13] 何晓晨, 金士杰, 林莉. 超声背散射信号递归定量分析无损表征CFRP孔隙分布仿真［J］. 复合材料学报, 2018, 1-6.

HE X CH, JIN SH J, LIN L. Simulation on non-destructive evaluation of CFRP void distribution with recurrence quantification analysis of ultrasonic back-scatter signals［J］. Acta Materiae Compositae Sinica, 2018, 1-6.(in Chinese)

[14] 滕云浩. 碳纤维复合材料构件微缺陷超声评价系统研究［D］.浙江大学, 2017.

TENG Y H. A Research on Ultrasonic Evaluation System of Material Defects in Carbon Fiber Reinforced Polymer ［D］. Zhejiang: Zhejiang University, 2017.(in Chinese)

[15] 胡勃. 基于递归分析的碳纤维复合材料微缺陷超声检测技术研究［D］.浙江大学, 2018.

HU B. A Research on Ultrasonic Testing Technology based on Recurrence Analysis for Defects Detection in Carbon Fiber Reinforced Plastics［D］. Zhejiang: Zhejiang University, 2018.(in Chinese)

滕国阳, 周晓军, 杨辰龙, 曾祥. 厚截面碳纤维复合材料远表面微缺陷超声检测[J]. 光学精密工程, 2018, 26(12): 3108. TENG Guo-yang, ZHOU Xiao-jun, YANG Chen-long, ZENG Xiang. Ultrasonic detection method of micro defects in thick-section CFRP[J]. Optics and Precision Engineering, 2018, 26(12): 3108.

厚截面碳纤维复合材料远表面微缺陷超声检测

关于本站 Cookie 的使用提示

全站搜索

厚截面碳纤维复合材料远表面微缺陷超声检测

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索