One of the main failure phenomena in composite materials is delamination, which consists of the progressive separation of plies. An augmented extended Kalman filter (KF) approach for joint input-parameter estimation is proposed and demonstrated in this work for delamination growth monitoring. In particular, a novel recursive estimation scheme for parameter identification is proposed within the joint input-parameter scheme which allows for a substantial reduction of the number of required sensors, enabling the use of the proposed KF approach. The proposed methodology also allows for the full-field response reconstruction, interface stiffness degradation monitoring and critical load identification. The methodology is validated on both simulated as well experimental data of a carbon fiber double Cantilever beam specimen, showcasing good accuracy for both cases.
OstachowiczWSomanRMalinowskiP.Optimization of sensor placement for structural health monitoring: a review. Struct Health Monit2019; 18(3): 963–988.
2.
SunHBüyüköztürkO.Optimal sensor placement in structural health monitoring using discrete optimization. Smart Mater Struct2015; 24(12): 125034.
3.
FlynnEBToddMD.A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing. Mech Syst Signal Proc2010; 24(4): 891–903.
4.
ZouYTongLPSGStevenGP.Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures—a review. J Sound Vibrat2000; 230(2): 357–378.
5.
MoorthyVMarappanK.Experimental study on delamination identification in tapered laminated composite plates using damage detection models. Compos Struct2023; 323: 117494.
6.
KralovecCSchagerlM.Review of structural health monitoring methods regarding a multi-sensor approach for damage assessment of metal and composite structures. Sensors2020; 20(3): 826.
7.
TuloupCHariziWAboura, et al. On the use of in-situ piezoelectric sensors for the manufacturing and structural health monitoring of polymer-matrix composites: a literature review. Compos Struct2019; 215: 127–149.
8.
JuMDouZLiJW, et al. Piezoelectric materials and sensors for structural health monitoring: fundamental aspects, current status, and future perspectives. Sensors2023; 23(1): 543.
9.
AabidAParveezBRahemanMA, et al. A review of piezoelectric material-based structural control and health monitoring techniques for engineering structures: challenges and opportunities. Actuators2021; 10(5): 101.
10.
TakedaSMinakuchiSOkabeY, et al. Delamination monitoring of laminated composites subjected to low-velocity impact using small-diameter FBG sensors. Compos Part A Appl Sci Manuf2005; 36(7): 903–908.
11.
PanopoulouALoutasTRouliasD, et al. Dynamic fiber Bragg gratings based health monitoring system of composite aerospace structures. Acta Astron2011; 69(7–8): 445–457.
12.
KuangKSCCantwellWJ. Use of conventional optical fibers and fiber Bragg gratings for damage detection in advanced composite structures: a review. Appl Mech Rev2003; 56(5): 493–513.
13.
GholizadehS.A review of non-destructive testing methods of composite materials. Proc Struct Integr2016; 1: 50–57.
14.
CristianiDFalcetelliFYueN, et al. Strain-based delamination prediction in fatigue loaded CFRP coupon specimens by deep learning and static loading data. Compos Part B Eng2022; 241: 110020.
15.
TidririKChattiNVerronS, et al. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges. Ann Rev Control2016; 42: 63–81.
16.
HeMWangYRamakrishnanKR, et al. A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams. Struct Health Monit2021; 20(4): 1997–2012.
17.
HeMRamakrishnanKRWangY, et al. A combined global-local approach for delamination assessment of composites using vibrational frequencies and FBGs. Mech Syst Signal Proc2022; 167: 108577.
18.
CumboRTamarozziTJanssensK, et al. Kalman-based load identification and full-field estimation analysis on industrial test case. Mech Syst Signal Proc2019; 117: 771–785.
19.
RisalitiETamarozziTVermautM, et al. Multibody model based estimation of multiple loads and strain field on a vehicle suspension system. Mech Syst Signal Process2019; 123: 1–25.
20.
CapalboCEDe GregoriisDTamarozziT, et al. Parameter, input and state estimation for linear structural dynamics using parametric model order reduction and augmented Kalman filtering. Mech Syst Signal Proc2023; 185: 109799.
CumboRMazzantiLTamarozziT, et al. Advanced optimal sensor placement for Kalman-based multiple-input estimation. Mech Syst Signal Proc2021; 160: 107830.
23.
HongSKEpureanuBICastanierMP.Next-generation parametric reduced-order models. Mech Syst Signal Process2013; 37(1–2): 403–421.
24.
AmsallemDFarhatC.An online method for interpolating linear parametric reduced-order models. SIAM J Sci Comput2011; 33(5): 2169–2198.
25.
ZahrMJFarhatC.Progressive construction of a parametric reduced-order model for PDE-constrained optimization. Int J Numer Methods Eng2011; 102(5): 1111–1135.
26.
BennerPGugercinSWillcoxK.A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Rev2015; 57(4): 483–531.
27.
CSN EN 6033. Carbon fibre reinforced plastics—test method—determination of interlaminar fracture toughness energy—mode I—GIC. Aerospace Series, 2015.
28.
ParkKPaulinoGH.Cohesive zone models: a critical review of traction-separation relationships across fracture surfaces. Appl Mech Rev2011; 64(6): 060802.
29.
DimitriRCornettiPMantičV, et al. Mode-I debonding of a double cantilever beam: a comparison between cohesive crack modeling and finite fracture mechanics. Int J Solids Struct2017; 124: 57–72.
SimonD.Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Hoboken, NJ: John Wiley and Sons, 2006.
32.
AzamSEChatziEPapadimitriouC.A dual Kalman filter approach for state estimation via output-only acceleration measurements. Mech Syst Signal Proc2015; 60: 866–886.
33.
LourensEReyndersEDe RoeckG, et al. An augmented Kalman filter for force identification in structural dynamics. Mech Syst Signal Proc2012; 27: 446–460.
34.
TamarozziTRisalitiERottiersW, et al. Noise, ill-conditioning and sensor placement analysis for force estimation through virtual sensing. In: International conference on noise and vibration engineering (ISMA2016), pp. 1741–1756. Belgium: Katholieke Univ Leuven, Department Werktuigkunde, 2016.
35.
McElroyM. An enriched shell element for delamination simulation in composite laminates. In: American Society for Composites technical conference (No. NF1676L–20621), East Lansing, MI, USA, 28–30 September 2015.
36.
CraigRRJr.A review of time-domain and frequency-domain component mode synthesis method. Int J Analyt Exp Modal Anal1987; 2(2): 59–72.
37.
SkecLAlfanoGJelenicG.Complete analytical solutions for double cantilever beam specimens with bi-linear quasi-brittle and brittle interfaces. Int J Fract2019; 215: 1–37.
38.
BerryJP.Determination of fracture surface energies by the cleavage technique. J Appl Phys1963; 34(1): 62–68.
39.
WilliamsJG.The fracture mechanics of delamination tests. J Strain Anal Eng Design1989; 24(4): 207–214.
40.
ASTM D5528/D5528M-21. Standard test method for mode I interlaminar fracture toughness of unidirectional fiber-reinforced polymer matrix composites. West Conshohocken, PA: ASTM International, 2022.
41.
FanteriaDLazzeriLPanettieriE, et al. Experimental characterization of the interlaminar fracture toughness of a woven and a unidirectional carbon/epoxy composite. Compos Sci Tech2017; 142: 20–29.
