基于印刷感应层电学稀疏成像的蜂窝夹层结构冲击损伤识别

doi:10.19936/j.cnki.2096-8000.20211128.031

复合材料科学与工程 ›› 2022, Vol. 0 ›› Issue (9): 5-10.DOI: 10.19936/j.cnki.2096-8000.20211128.031

• 基础研究 • 下一篇

基于印刷感应层电学稀疏成像的蜂窝夹层结构冲击损伤识别

周登, 严刚, 郭树祥^*, 束嘉俊

南京航空航天大学航空学院机械结构力学及控制国家重点实验室,南京 210016

收稿日期:2021-09-03 出版日期:2022-09-28 发布日期:2022-09-27
通讯作者: 郭树祥(1976-),男,博士,副教授,主要从事工程问题建模仿真和结构完整性评定方面的研究,nuaagsx@nuaa.edu.cn。
作者简介:周登(1996-),男,硕士研究生,主要从事复合材料结构健康监测方面的研究。
基金资助:
国家自然科学基金项目(11602104);航空科学基金(2017ZA52005)

Impact damage identification for honeycomb sandwich panel using printing sensing layer and electrical sparse tomography

ZHOU Deng, YAN Gang, GUO Shu-xiang^*, SHU Jia-jun

State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2021-09-03 Online:2022-09-28 Published:2022-09-27

摘要/Abstract

摘要： 蜂窝夹层结构在航空航天领域具有广泛的应用,但其在使用过程中不可避免地会遭受损伤,特别是冲击造成的损伤对其强度有很大的影响。本文结合现代电子印刷技术,采用石墨烯导电碳油墨和银浆油墨在蜂窝夹层结构表面制备智能感应层,结合电学成像技术对低速冲击损伤进行识别。使用落锤冲击试验装置对蜂窝夹层结构进行了低速冲击,并于冲击前后在印刷感应层中注入微小电流获取边界电压变化。对电压数据进行分析,基于SpaRSA稀疏正则化算法重建感应层电导率变化的图像,将损伤信息可视化。实验结果表明,所提出方法能有效地识别出蜂窝夹层结构中冲击损伤的个数、位置和大致尺寸,与传统基于Tikhonov正则化的算法相比,稀疏成像算法对损伤尺寸的识别效果更好,为蜂窝夹层结构冲击损伤在线识别提供了一种新途径。

关键词: 蜂窝夹层结构, 冲击损伤识别, 印刷感应层, 电学成像, 稀疏正则化算法, 复合材料

Abstract: Honeycomb sandwiched structures are widely used in aerospace field, but during service it is inevitable for them to encounter damage, especially impact damage that can significantly reduce their strength. Combined with modern electronic printing technology, this study directly fabricates intelligent sensing layers on the surface of honeycomb sandwiched structure with conductive graphene-doped carbon ink and silver ink through screen printing, and identifies low-velocity impact damage with electrical tomography. Drop-weight device is used to impact the structure with low velocity, and the boundary voltage change of the sensing layer before and after impact is gathered by the electrical test system through injecting a tiny current into it. By analyzing the voltage data, sparse regularization algorithm, SpaRSA, is employed to reconstruct the image of conductivity change of the sensing layer to visualize damage information. Experimental results have demonstrated that, the proposed method can effectively identify the number, locations and approximate sizes of impact damage. And compared with traditional Tikhonov regularization-based algorithm, the sparse tomography algorithm can achieve better identification accuracy for damage sizes, providing a novel way of online impact damage identification for honeycomb sandwiched structures.

Key words: honeycomb sandwich structure, impact damage identification, printed sensing layer, electrical tomography, sparse regularization algorithm, composites

中图分类号:

TB332

周登, 严刚, 郭树祥, 束嘉俊. 基于印刷感应层电学稀疏成像的蜂窝夹层结构冲击损伤识别[J]. 复合材料科学与工程, 2022, 0(9): 5-10.

ZHOU Deng, YAN Gang, GUO Shu-xiang, SHU Jia-jun. Impact damage identification for honeycomb sandwich panel using printing sensing layer and electrical sparse tomography[J]. COMPOSITES SCIENCE AND ENGINEERING, 2022, 0(9): 5-10.

参考文献

[1] 陈静, 邱启艳. 蜂窝夹层结构在飞机上的应用及发展[J]. 新材料产业, 2018(7): 63-67.
[2] KHODAEI Z, ROJAS-DIAZ R, ALIABADI M. Lamb-wave based technique for impact damage detection in composite stiffened panels[J]. Key Engineering Materials, 2012, 488-489: 5-8.
[3] MUSTAPHA S, YE L, DONG X, et al. Evaluation of barely visible indentation damage (BVID) in CF/EP sandwich composites using guided wave signals[J]. Mechanical Systems and Signal Processing, 2016, 76-77: 497-517.
[4] PEI H, WEI Y, CHEN Y, et al. Impact test of composite sandwich beam with built-in FBG sensor[J]. IOP Conference Series: Materials Science and Engineering, 2018, 397: 012028.
[5] GIROLAMO D, CHANG H Y, YUAN F G. Impact damage visualization in a honeycomb composite panel through laser inspection using zero-lag cross-correlation imaging condition[J]. Ultrasonics, 2018, 87: 152-165.
[6] RELLINGER T, UNDERHILL P R, KRAUSE T W, et al. Combining eddy current, thermography and laser scanning to characterize low-velocity impact damage in aerospace composite sandwich panels[J]. NDT and E International, 2021, 120: 102421.
[7] BEKAS D G, SHARIF-KHODAEI Z, BALTZIS D, et al. Quality assessment and damage detection in nanomodified adhesively-bonded composite joints using inkjet-printed interdigital sensors[J]. Composite Structures, 2019, 211: 557-563.
[8] ZHOU P, CAO W, LIAO Y, et al. Temperature effect on all-inkjet-printed nanocomposite piezoresistive sensors for ultrasonics-based health monitoring[J]. Composites Science and Technology, 2020, 197: 108273.
[9] LIAO Y, ZHOU P, PAN D, et al. An ultra-thin printable nanocomposite sensor network for structural health monitoring[J]. Structural Health Monitoring, 2021, 20(3): 894-903.
[10] TALLMAN T N, SMYL D. Structural health and condition monitoring via electrical impedance tomography in self-sensing materials: A review[J]. Smart Materials and Structures, 2020, 29(12).
[11] BALTOPOULOS A, POLYDORIDES N, PAMBAGUIAN L, et al. Exploiting carbon nanotube networks for damage assessment of fiber reinforced composites[J]. Composites Part B, 2015, 76: 149-158.
[12] YAN G, ZHENG Y. A printed electrical impedance tomography sensor for detection of damage in composites[J]. International Journal of Applied Electromagnetics and Mechanics, 2020, 64: 1011-1017.
[13] KIM S J, KOH K, LUSTIG M,et al. An interior-point method for large-scale 1-regularized least squares[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 606-617.
[14] BERG E, FRIEDLANDER M. Probing the Pareto frontier for basis pursuit solutions[J]. Siam Journalon Scientific Computing, 2008, 31(2): 890-912.
[15] WRIGHT S, NOWAK R, FIGUEIREDO M. Sparse reconstruction by separable approximation[J]. IEEE Transaction on Signal Processing, 2009, 57(7): 2479-2493.
[16] 谢志鹏. 压缩感知的稀疏重构算法研究[D]. 南京: 南京航空航天大学, 2012.
[17] 蔡杰松. 基于组稀疏压缩感知的穿墙雷达成像研究[D]. 南京: 南京理工大学, 2017.
[18] FIGUEIREDO M, NOWAK R, WRIGHT S. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
[19] BARZILAI J, BORWEIN J M. Two-point step size gradient methods[J]. IMA Journal of Numerical Analysis, 1988, 8(1): 141-148.

基于印刷感应层电学稀疏成像的蜂窝夹层结构冲击损伤识别

Impact damage identification for honeycomb sandwich panel using printing sensing layer and electrical sparse tomography

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