Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.
CawleyPAlleyneD. The use of lamb waves for the long range inspection of large structures. Ultrasonics1996; 34(2–5): 287–290.
2.
SuZYeLLuY. Guided lamb waves for identification of damage in composite structures: a review. JSound Vibration2006; 295(3–5): 753–780.
3.
RoseJL. Ultrasonic guided waves in solid media. Cambridge, UK: Cambridge University Press, 2014. DOI: 10.1017/CBO9781107273610.
4.
AryanPKotousovANgCT, et al.A model-based method for damage detection with guided waves. Struct Control Health Monit2017; 24(3): e1884.
5.
Sankar PandaRRajagopalPKrishnanB. Rapid guided wave inspection of complex stiffened composite structural components using non-contact air-coupled ultrasound. Compos Structures2018; 206: 247–260.
6.
RayPYuXFanZ, et al.Fiber bragg grating based detection of part-thickness cracks in bent composite laminates using feature-guided waves. Smart Mater Stru2019; 28(8): 085026.
7.
IhnJ-BChangF-K. Pitch-catch active sensing methods in structural health monitoring for aircraft structures. Struct Health Monit2008; 7(1): 5–19. DOI: 10.1177/1475921707081979.
8.
RayPRajagopalPSrinivasanB, et al.Feature-guided wave-based health monitoring of bent plates using fiber bragg gratings. J Intell Mater Syst Structures2017; 28(9): 1211–1220.
9.
TabjulaJLKanakambaranSKalyaniS, et al.Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring. Structural Control and Health Monitoring2021: e2690.
YeagerMGregoryBKeyC, et al.On using robust mahalanobis distance estimations for feature discrimination in a damage detection scenario. Struct Health Monit2019; 18(1): 245–253.
12.
HaSLonkarKMittalA, et al.Adhesive layer effects on pzt-induced lamb waves at elevated temperatures. Struct Health Monit2010; 9(3): 247–256. DOI: 10.1177/1475921710365267.
13.
DonskoyDZagraiAAlexanderC, et al.Nonlinear vibro-acoustic modulation technique for life prediction of aging aircraft components. Proc Third Eur Workshop Struct Health Monit2006; 251–258.
14.
KeithWFarrarCRMansonG, et al.The fundamental axioms of structural health monitoring. Proc R Soc A: Math Phys Eng Sci2007; 463: 1639–1664.
15.
ZhangZLiuMQianL, et al.Baseline-free defect evaluation of complex-microstructure composites using frequency-dependent ultrasound reflections. Composites A: Appl Sci Manufacturing2020; 139: 106090.
16.
HeTXingGLiY, et al.A novel baseline-free defect detection and localization method of welded steel plate based on reciprocity loss. Measurement Science and Technology2020. Available at: http://iopscience.iop.org/10.1088/1361-6501/abb5db.
17.
JiaHLiuHZhangZ, et al.A baseline-free approach of locating defect based on mode conversion and the reciprocity principle of lamb waves. Ultrasonics2020; 102: 106063, ISSN 0041-624X, DOI: 10.1016/j.ultras.2020.106063, Available at: http://www.sciencedirect.com/science/article/pii/S0041624X20300020.
18.
JoostSHedayatrasaSPoelmanG, et al.Backside delamination detection in composites through local defect resonance induced nonlinear source behavior. J Sound Vibration2020; 479: 115360, ISSN 0022-460X, DOI: 10.1016/j.jsv.2020.115360, Available at: https://www.sciencedirect.com/science/article/pii/S0022460X20301917.
19.
BrotherhoodCJDrinkwaterBWDixonS. The detectability of kissing bonds in adhesive joints using ultrasonic techniques. Ultrasonics2003; 41(7): 521–529.
20.
SinghAKChenB-YTanVBC, et al.Finite element modeling of nonlinear acoustics/ultrasonics for the detection of closed delaminations in composites. Ultrasonics2017; 74: 89–98.
21.
SamaitisVMažeikaLRekuvienėR. Assessment of the length and depth of delamination-type defects using ultrasonic guided waves. Appl Sci2020; 10(15): 5236.
22.
MeiHGiurgiutiuV. Characterization of multilayer delaminations in composites using wavenumber analysis: numerical and experimental studies. Struct Health Monit2021; 20(3): 1004–1029. DOI: 10.1177/1475921720939616.
23.
GaoTWangYQingX. A new laser ultrasonic inspection method for the detection of multiple delamination defects. Materials1944; 14(9): 2021. DOI: 10.3390/ma14092424, Available at: https://www.mdpi.com/1996-1944/14/9/2424.
24.
MeiHMigotAFaisal HaiderM, et al.Vibration-based in-situ detection and quantification of delamination in composite plates. Sensors2019; 19(7), ISSN 1424-8220. DOI: 10.3390/s19071734, Available at: https://www.mdpi.com/1424-8220/19/7/1734.
25.
Hoon SohnDDYangJ-YParkH-J, et al.Delamination detection in composites through guided wave field image processing. Composites Science Technology2011a; 71(9): 1250–1256.
26.
Hoon SohnDDYangJYDeSimioM, et al.Automated detection of delamination and disbond from wavefield images obtained using a scanning laser vibrometer. Smart Mater Structures2011b; 20(4): 045017.
27.
GlushkovEGlushkovaNMikhailGolubV, et al.Wave energy trapping and localization in a plate with a delamination. Smart Mater Struc2012; 21(12): 125001.
28.
GuptaSRajagopalP. Effect of ply orientation and through-thickness position of delamination on the reflection of fundamental symmetric s0 lamb mode in gfrp composite plate structures. Ultrasonics2018; 90: 109–119.
29.
HayashiTKawashimaK. Multiple reflections of lamb waves at a delamination. Ultrasonics2002; 40(1–8): 193–197.
30.
CawleyPLoweMJSAlleyneDN, et al.Practical long range guided wave inspection-applications to pipes and rail. Mater Eval2003; 61(1): 66–74.
31.
CaraLeckeyACSeeboJP. Guided wave energy trapping to detect hidden multilayer delamination damage. AIP Conference Proceedings, 1650. American Institute of Physics, 2015, pp. 1162–1169.
YuXRatasseppMFanZ. Damage detection in quasi-isotropic composite bends using ultrasonic feature guided waves. Composites Sci Techn2017; 141: 120–129.
34.
Akbar AliMSSRajagopalP. Probability of detection (pod) curves based on weibull statistics. J Nondestructive Eval2018; 37(2): 20.
35.
DworakowskiZKohutPGallinaA, et al.Vision-based algorithms for damage detection and localization in structural health monitoring. Struct Control Health Monit2016; 23(1): 35–50.
36.
BerensAP. Nde reliability data analysis. ASM Handbook1989; 17: 689–701.
37.
BridsonR. Fast poisson disk sampling in arbitrary dimensions. SIGGRAPH Sketches2007; 10: 1278780–1278807.
38.
VelichkoAWilcoxPD. Quantitave characterisation of complex defects using two-dimensional ultrasonic arrays. Proc ECNDT2010; 1(18)
39.
ManasGMithunrajKKJohnsonBL, et al.Poisson-disk sparse stmr array based structural health monitoring for plate-like structures using ultrasonic guided waves. J ISSS2016; 5(1): 31–47.
40.
PandaRSRajagopalPKrishnanB. Characterization of delamination-type damages in composite laminates using guided wave visualization and air-coupled ultrasound. Struct Health Monit2017; 16(2): 142–152.
41.
PavlakovicBLoweMAlleyneD, et al.Disperse: a general purpose program for creating dispersion curves. In: Review of progress in quantitative nondestructive evaluation. Berlin, Germany: Springer; 1997, pp. 185–192.
42.
SmithM. Abaqus/standard user’s manual. version 6.9. Providence, RI: Simulia, 2009.
43.
XuRWunschD. Survey of clustering algorithms. IEEE Trans Neural Networks2005; 16(3): 645–678.
44.
MenonVMuthukrishnanGKalyaniS. Subspace clustering without knowing the number of clusters: A parameter free approach. IEEE Trans Signal Process2020; 68: 5047–5062.
45.
EsterMKriegelH-PterSanderJ, et al.A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd1996; 96: 226–231.
46.
MaiSTAssentIMartinS. Anydbc: an efficient anytime density-based clustering algorithm for very large complex datasets. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, 13–17 August 2016; 2016, pp. 1025–1034.
47.
Sullivan PepeM. Receiver operating characteristic methodology. J Am Stat Assoc2000; 95(449): 308–311.
48.
AcharHarishVRayPRajagopalP, et al.Detection of fundamental shear horizontal mode in plates using fibre bragg gratings. J Intell Mater Syst Struc2016; 27(16): 2229–2236.
49.
RayPSrinivasanBKrishnanB, et al.Monitoring pipe wall integrity using fiber bragg grating-based sensing of low-frequency guided ultrasonic waves. Ultrasonics2018; 90: 120–124.
50.
JagadeeshwarTLRajagopalPSrinivasanB. Detection of guided waves in a composite plate using surface bonded fiber bragg gratings sensor. IEEE Sensors2018; 1–4. DOI: 10.1109/ICSENS.2018.8589511.
51.
JagadeeshwarTLSrijithKRajagopalPrabhu, et al.Wave visualization of ultrasonic guided waves in a metallic structure using fiber bragg gratings. Optical Fiber Sensors Conference 2020 Special Edition, W4.32. Optical Society of America, 2020. DOI: 10.1364/OFS.2020.W4.32, http://www.osapublishing.org/abstract.cfm?URI=OFS-2020-W4.32.
