Article

Journal of Intelligent Material Systemsand Structures22(17) 2061–2067� The Author(s) 2011Reprints and permissions:sagepub.co.uk/journalsPermissions.navDOI: 10.1177/1045389X11424214jim.sagepub.com

A strain amplitude-based algorithm forimpact localization on compositelaminates

Cristobal Hiche, Clyde K. Coelho and Aditi Chattopadhyay

AbstractAutomated detection of damage due to low energy impacts in composite structures is very important for aerospace struc-tural health monitoring applications. Low-velocity impact creates subsurface damage that can significantly reduce the stiffnessof a component, yet show barely visible damage. This article proposes a novel methodology for impact localization based onthe maximum strain amplitude measured by fiber Bragg grating (FBG) sensors during an impact event. The approach corre-lates the strain amplitude of each sensor pair to find the location of highest strain corresponding to the impact location.This approach requires minimal knowledge of the structure and fewer number of sensors as opposed to current localizationmethods. Both simulation and experimental data are used as proof of concept. Since FBG sensors measure strain in onlyone direction, the effect of sensor orientation on the performance of the algorithm is also studied. The algorithm is testedon graphite/epoxy composite plates and shows good localization results in all impact cases considered.

Keywordsimpact localization, strain amplitude, woven composites, fiber Bragg gratings

Introduction

As the aerospace industry switches from schedule-basedto condition-based maintenance (CBM), there is anincreased need to accurately detect, localize, and esti-mate damage in a given component. Additional com-plexities arise due to anisotropy when compositematerials are involved. Low-velocity impacts in compo-sites can result in damage that cannot be detected usingvisual inspection, even though they result in stiffnessdegradation and a significant loss in structural integrity.Currently, detection of this type of damage requires theuse of specialized nondestructive evaluation (NDE)approaches such as acoustic emission (Yu et al., 2006),thermography (Genest et al., 2009), eddy currentmethod (Sximsxir and Ankara, 2007), and ultrasonic scan-ning (Aymerich and Meili, 2000). While use of certainNDE techniques can provide accurate estimates of thedamage, they all require the structure to be taken out ofservice and in some cases disassembled for inspection.For CBM to be viable, a structural health management(SHM) approach must be employed. A robust SHMframework requires the installation of a distributed sen-sor network so that damage measurements can be madequickly and frequently without significant effort orexpense. Several types of sensor networks are beinginvestigated, including strain gauges (Chan et al., 2001),

piezo transducers (Coelho et al., 2007), and fiber opticsensors (Hiche et al., 2008). Using these various typesof sensors, active and passive detection techniques havebeen proposed with some degree of success in metallicstructures (Chattopadhyay et al., 2010). In compositestructures, the use of wave-based techniques poses con-siderable difficulties. The traveling waves, which getreflected and refracted by in situ damage, are directiondependent due to the inherent anisotropy of the system.Therefore it is difficult to isolate the changes in wavesignature due to damage alone, making detection incomposites a more complex problem compared tometals. Further difficulties are introduced due to thepresence of different failure modes in a composite struc-ture, and its propensity to be damaged at the subsurfacelevel under low energy impacts (Cantwell and Morton,1989; Kim and Sham, 2000; Naik and Chandra Sekher,1998; Siow and Shim, 1998).

Damage localization in metallic structures is gener-ally based on the concept of time-of-flight of ultrasonicwaves, employing a simple triangulation algorithm to

Arizona State University, Tempe, AZ, USA

Corresponding author:

Cristobal Hiche, Department of Mechanical and Aerospace Engineering,

Arizona State University, Tempe, AZ 85287-6106, USA

Email: chiche@asu.edu

localize the impact. Kundu et al. (2008) also implemen-ted an optimization algorithm to locate the point ofimpact based on the time-of-flight of ultrasonic signals.However, it is shown that significant errors are intro-duced if the wave speed is assumed constant on an ani-sotropic plate. To overcome this issue, experiments tomeasure the wave speed in different directions were per-formed, which may be impractical to do in some com-posite structures. Matt and di Scalea (2007) proposed apassive approach to localization using macro-fibercomposite (MFC) patches in a rosette to determine thedirection of an incoming wave. The main advantage ofthis method is that wave velocity is not required. Thismethod was implemented on both aluminum and com-posite plates and also on a curved honeycomb sandwichcomposite panel, and tested using lead breaks. Thisproof-of-concept methodology using two MFC rosettesshowed promising results in both the aluminum andcomposite plate. However, significant errors wereobserved in the honeycomb sandwich panel due to thesevere attenuation loss in the honeycomb core. Theaddition of a third rosette to minimize shadowing prob-lems, which occurs between two rosettes when the wavedirection obtained by each rosette is close to being par-allel, was suggested. Fiber Bragg grating (FBG) sensorsare showing promise in many applications since theyare low weight, require minimal space, immune to elec-tromagnetic interference, and can support multiple sen-sors in a single fiber. Betz et al. (2007) implemented anovel approach to accurately localize the presence of ahole on a metallic structure using two FBG rosettes tocapture the Lamb waves generated by piezoelectrictransducers (PZT). This method requires the use of anoptimization algorithm, which can be computationallyexpensive. Moreover, this method is difficult to imple-ment in composites because knowledge of the wavevelocity is required.

In this research, a novel method is proposed to loca-lize impact using distributed FBG sensors to measurethe maximum strain amplitude during impact. Theobjective of this work is to build a data-driven frame-work that can localize an impact even in the absence ofcomplete strain information. Arrays of sensor are sur-face mounted on both composite plates and compositewings and subjected to low-velocity impacts. A finiteelement (FE) model has been developed with virtualsensing capabilities to investigate different sensor con-figurations. Both simulations and experiments havebeen used to validate the approach.

Theoretical Formulation

The impact localization algorithm is based on the abso-lute maximum strain amplitude measured during animpact event using FBG sensors. The main idea behindthis novel localization algorithm is to locate damage

based on the fact that the maximum strain amplitudeincreases when an impact is located closer to a sensor.This algorithm only requires the relative position ofeach FBG sensor. The maximum strain observed dur-ing an impact for each sensor is extracted from the dataand normalized to compute the strain ratio betweeneach sensor pair, as expressed by the followingequation:

rij =�ej

�ei + �ej

, ð1Þ

where �ei and �ej are the normalized absolute maximumstrain of the ith and jth sensor, respectively. The ratioof the strains between each sensor pair is then corre-lated to the physical location of each sensor to find arelative location of damage between each sensor pair.

To compute the relative distance based on the ratioof the strains, the Euclidian distance between two sen-sors is computed, which can be expressed as follows:

uij = tan�1 yj � yi

xj � xi

� �, ð2Þ

dij =

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXj � Xi

� �2+ Yj � Yi

� �2,

qð3Þ

where (Xi, Yi) and (Xj, Yj) are the locations of the ithand jth sensor, respectively. uij and dij are the angle andEuclidian distance between each sensor, respectively.Then, the relative impact position can be computedusing the following expressions:

Iij

� �x

= Xi + rijdij cos uij, ð4aÞ

Iij

� �y

= Yi + rijdij sin uij ð4bÞ

Equations (4a) and (4b) represent the geometrical esti-mation of the relative impact location for each sensorpair in the x- and y-direction, respectively. The locationof these relative position points is calculated just as themultiplication of the strain ratio with the Euclidian dis-tance between each sensor pair. This concept is illu-strated in Figure 1, which shows the strain amplitude oftwo sensors during impact and the corresponding esti-mation of their relative location. Figure 1(a) shows thestrain profile of two FBG sensors during a low energyimpact. It is seen that sensor S1 measures a maximumstrain more than four times the strain measured at sen-sor S2 for the given impact event. According to thestrain ratio in Equation (1), and Equations (4a) and(4b), the relative location is estimated at about one-fourth the distance between sensors S1 and S2 from S1,as seen in Figure 1(b). This shows good estimation inone direction (x-direction) but not on the other direc-tion, as may be expected with only two sensors. With alarger sensor network, the relative location for eachsensor pair is calculated to obtain a grid of these rela-tive locations as seen in Figure 3(a).

2062 Journal of Intelligent Material Systems and Structures 22(17)

As indicated before, the algorithm is based on theknowledge that the area of higher strain correspond tothe impact location. Sensors closer to the impact loca-tion will measure higher strains. The computed relativelocations of sensors closer to the impact should providea better prediction of the actual impact than computedrelative locations of sensors away from an impact.Therefore all computed relative locations between eachsensor pair need to be related to the area of higherstrain. A magnitude for these relative locations is com-puted as the sum of the absolute maximum strains mea-sured by the ith and jth sensors,

Mk = �ei + �ej ð5Þ

In order to accurately predict the location of impact,the relative location points with higher strain magni-tudes have to be selected. A selection process is formu-lated in which the minimum number of these relativelocation points with the highest strain magnitudes isselected. First, Mk is arrayed in descending order ofstrain amplitudes and then the optimal number ofpoints m is found such that

Pmk = 1 Mk � a3

Pnk = 1 Mk ,

where n is the total number of relative location points,and a is a tuning parameter which depends on both thenumber of sensors and also the geometry (curvature) ofthe given structure. A value of a = 0.6 has been used,implying the minimum number of intensity pointsselected need to contain at least 60% of the strainenergy. This value of the tuning parameter allows tocapture the magnitude of a sensor pair in which one ofthe sensors might be on a blind zone due to the inher-ent directionality of an FBG sensor. Figure 2 shows thesensing region of sensor 2 and the bilobed sensingregion due to the directionality of an FBG sensor. Itcan be seen that the two impacts near S1 are close toS2, but they cannot be sensed by S2 because they are

almost perpendicular to the direction of S2 and there-fore on its blind zone.

This selection process can be further clarified byFigure 3(b), which shows five relative location pointsrepresented by black triangles. These points correspondto the five relative location points with the largest com-puted strain magnitude from Figure 3a, represented bythe small dots/stars. The predicted impact location isthen estimated simply as the arithmetic mean of theselected location points as expressed by

Ximpact, Yimpact

� �=

Pmk = 1 Ik

ij

X

m,

Pmk = 1 Ik

ij

Y

m

24

35 ð6Þ

Figure 1. (a) Strain amplitude profile of two FBG sensors, (b) relative location obtained based on the ratio of the strain amplitudesbetween the sensors.

Figure 2. Visualization of the bilobed sensing region for sensor2. The impacts near S1, although close to S2, cannot be detecteddue to the bilobed nature of an FBG sensor and its dead zone.

C. Hiche et al. 2063

FE Model

An FE model of a twill weave laminate was developedusing ABAQUS/Explicit to test the algorithm. Four plysquare composite laminates of dimension 12 in. andfixed-fixed boundary conditions are used. An analysisshowed that using a four-ply laminate instead of a 12-ply laminate (which was used for the experimentalresults) for the simulation significantly reduced the com-putational time while the strain discrepancy was lessthan 2%. It is important to note that although the num-ber of plies was reduced, the overall thickness betweenexperimental and simulation laminates remained con-stant at 0.061 in. Ten different impact simulations areperformed at three different energy levels: 0.5, 12.5, and50 J (0.4, 1.9, and 3.8 m/s, respectively). Five FBG sen-sors are modeled by extracting the strain information atfive locations on the plate, as summarized in Table 1.The strain information, e22, is extracted for sensors S1,S2, and S4, while e11 is extracted from S3 and S5.

Experimental Setup

Woven carbon fiber composite plates were made usingprepreg Fibre Glast 3K 2 3 2 twill weave fabric. All

samples were 12-ply laminates with an overall thicknessof 6.10 3 1022 6 3.9 3 1023 in. The plate specimenswere clamped on all sides by the fixture shown inFigure 4 to impose a fixed-fixed boundary condition,exposing a square test area of 12 3 12 in. A five FBGsensor array was surface mounted in a pentagon shapeto capture strain in both x- and y-directions at differentlocations on the plate (Table 1). The Micron Opticsmodel os1200 FBG sensor arrays have a polyimide coat-ing that provides a greater strength to the optical fiberand improves strain transfer compared to the acrylate-coated fiber. The surfaces of the composite plates wereprepped for FBG sensor mounting by sanding and clean-ing to maximize adhesive bonding between the surfaceand the sensor, minimizing both air pockets and theadhesive layer thickness between the composite plate andthe FBG sensor. Loctite 401, a medium viscosity one-part cyanoacrylate adhesive, was used to mount theFBGs.

All impact testings were performed using a modifiedCharpy impactor with a hemispherical impact head;schematic is shown in Figure 5. The FBG sensors wereall interrogated with a custom-made interrogator devel-oped by Todd et al. (2001) at the Naval ResearchLaboratory. The system is based on the use of a tun-able optical filter and a Mach–Zehnder interferometer.This interrogation system allows for high-resolutionwavelength shift detection, and measures strain withhigh precision at high sampling frequencies. It is verysuitable for high strain rate applications, such as animpact event. For this study, the sampling frequency ofthe FBG response was 9765 Hz.

It is important to note that a scheme to measure andestablish the change in the Bragg wavelength due totemperature changes was not necessary in this research.

Figure 3. Visualization of localization algorithm based on (a) relative location points between each sensor pair and (b) the selectionof relative location points for impact location prediction.

Table 1. Location of FBG sensors on composite plate

Sensor x-Coordinate (in.) y-Coordinate (in.)

S1 3.875 2.750S2 8.125 2.750S3 9.375 6.750S4 6.000 9.250S5 2.625 6.750

2064 Journal of Intelligent Material Systems and Structures 22(17)

During testing, the system records the wavelength atthe beginning of each measurement and treats this asthe zero strain point. This reduces any errors intro-duced by temperature variations to only those thatoccur during the 5-s measurement duration, which isinsignificant compared with the dynamic strain due toimpact.

Results and Discussion

The localization results showing the distance errorbetween the predicted and actual impact location forthe 50-J impacts on a plate are summarized in Table 2.These results were selected since they are a good repre-sentation of the three different impact energy results.From the table it is seen that some locations are esti-mated within three-fourth of an inch while others havea 5- to 6-in. error. The high errors, especially at loca-tions (10,10) and (3,9), are due to the fact that theseimpacts are outside the sensing region formed by thesensors. This error is expected due to the formulationof the localization algorithm which calculates the rela-tive location between each sensor pair, and thus it islimited to localize impacts within the region defined by

the sensors. A better representation of the results inTable 2 is shown in Figure 6.

Figure 6 shows the difference between the actual(cross) and the predicted (circle) impact locations. Goodresults can be seen for most impact cases with an aver-age error of 0.95 in. for all impacts within the sensingregion. Only the impact location at (7,4.25) shows a highdistance error of 2.45 in. A possible explanation for theerror is due to the unusually high strain reading of S4which biases the prediction toward that sensor, thus theimpact prediction is closer to sensor S4 than to sensorS2. The algorithm cannot accurately predict impactsoutside the sensing region (Figure 6(b)) and highererrors were observed as the impact location approachedthe edge of the sensing region. This is also due to the factthat when damage is at the edge of the sensing region, asingle selected relative location point is sufficient for itsaccurate prediction; a combination of relative locationpoints bias the location toward the center.

Different sensor configurations have been investi-gated and simulated using four FBG sensors. Theseconfigurations varied not only in the orientation of theFBG sensors but also their location on the plate.Although the predicted location of a given impact var-ies based on the sensor configuration, the average errorof all the predicted impacts remains the same. Figure 7shows the results of the algorithm using one of the sen-sor array configurations explored. This suggests theorientation of the sensors is not a critical parameter foraccurate localization. The average distance error is1.42 in., showing a 50% error increase with the reduc-tion of one sensor, suggesting a minimum of five sen-sors are required for localization predictions within lessthan an inch of the actual impact on a plate. Furtherwork will be conducted to confirm these findings.

Experiments were conducted on twill weave compo-site plates to validate the model and to confirm the fea-sibility of the algorithm on an actual compositestructure. The plates were impacted at each locationthree times with different energies: 5, 10, and 15 J.Figure 8 displays a visual representation of the localiza-tion results taken as the average error of the three

Figure 5. Modified Charpy impactor setup.

Figure 4. Plate fixture and FBG sensor location.

Table 2. Simulation results on composite plate

Location (in.) Error (in.)

(7.00,4.25) 2.4516(6.00,6.00) 0.2045(7.50,6.75) 0.6271(8.00,3.00) 0.3167(4.00,3.00) 0.2885(4.00,4.00) 0.5671(7.50,8.00) 1.9830(5.50,7.50) 1.1570(10.00,10.00) 6.5373(3.00,9.00) 5.0528

C. Hiche et al. 2065

impacts, showing good results for all the three impactcases. The average error is 0.57 in., showing goodagreement with the simulation results shown in Figure6(a), for impacts within the sensing zone. It is importantto note that the experimental impact location at thecoordinate (7,4) is very accurate, which differs from thesimulation impact at (7,4.25), confirming an unusualhigh strain profile was measured by sensor S4 due tonumerical error in the simulation which prompted ahigh distance error.

Conclusion

A novel localization algorithm was developed, usingthe strain amplitude of FBG sensors to localize

low-velocity impacts. The algorithm was based on themaximum strain amplitude measured by FBG sensorsduring an impact and the geometrical relationshipamong the sensors. The algorithm was tested on simu-lated and experimental data from woven compositeplates. Different FBG configurations were used for thecomposite laminates to test the robustness of the algo-rithm and the minimum number of FBG sensorsrequired for accurate localization prediction. The loca-lization of impacts on a plate with five FBG sensorsshowed an average error of 0.95 in. for the simulationsresults and 0.57 in. for the experimental results. Whenthe number of sensors was reduced to four, it increasedits average error of all impacts to 1.42 in. for differentFBG configurations and orientations. Future work will

Figure 6. Localization results on simulated plate of (a) impacts within sensing region and (b) impacts outside or near edge of sensingregion.

Figure 7. Localization results on simulated plate using a fourFBG sensor configuration.

Figure 8. Localization results on composite plates.

2066 Journal of Intelligent Material Systems and Structures 22(17)

focus on improving the algorithm to account forimpacts located at the edges of the sensing region,which require fewer number of relative locations. Also,the algorithm will be tested in complex compositestructures.

Acknowledgments

The authors gratefully acknowledge the support of thisresearch by the Army Research Office, AMSRD-ARL-RO-SIProposal Number: 49008-EG, Agreement Number: W911NF-07-1-0132, Program Manager: COL. Reed F. Young. We alsoacknowledge Dr. Mark Seaver from the Naval Research Lab

for his guidance in this project.

References

Aymerich F and Meili S (2000) Ultrasonic evaluation ofmatrix damage in impacted composite laminates. Compo-

sites Part B: Engineering 31(1): 1–6.Betz DC, Thursby G, Culshaw B and Staszewski W (2007)

Structural damage location with fiber Bragg gratingrosettes and Lamb waves. Structural Health Monitoring

6(4): 299–308.Cantwell WJ and Morton J (1989) Comparison of the low

and high velocity impact response of CFRP. Composites

20(6): 545–551.Chan THT, Li ZX and Ko JM (2001) Fatigue analysis and

life prediction of bridges with structural health monitoringdata—Part II: Application. International Journal of Fati-gue 23(1): 55–64.

Chattopadhyay A, Papandreou-Suppappola A, Peralta P andKovvali N (2010) A multidisciplinary approach to struc-tural health monitoring and damage prognosis of aero-space hotspots. Aeronautical Journal 113(1150): 799–810.

Coelho CK, Das S, Chattopadhyay A, Papandreou-Suppap-pola A and Peralta P (2007) Detection of fatigue cracks

and torque loss in bolted joints. Proceedings of SPIE

6532(653204-12):.Genest M, Martinez M, Mrad N, Renaud G and Fahr A

(2009) Pulsed thermography for non-destructive evalua-

tion and damage growth monitoring of bonded repairs.

Composite Structures 88(1): 112–120.Hiche C, Liu KC, Seaver M and Chattopadhyay A (2008)

Characterization of impact damage in woven fiber compo-

sites using fiber Bragg grating sensing and NDE. Proceed-

ings of SPIE 7294(72940E-11):.Kim J-K and ShamM-L (2000) Impact and delamination fail-

ure of woven-fabric composites. Composites Science and

Technology 60: 745–761.Kundu T, Das S, Martin SA and Jata KV (2008) Locating

point of impact in anisotropic fiber reinforced composite

plates. Ultrasonics 48(3): 193–201.Matt HM and di Scalea FL (2007) Macro-fiber composite

piezoelectric rosettes for acoustic source location in com-

plex structures. Smart Materials and Structures 16: 1489.Naik NK and Chandra Sekher Y (1998) Damage in laminated

composites due to low velocity impact. Journal of Rein-

forced Plastics and Composites 17(14): 1232–1263.Sximsxir M and Ankara A (2007) Comparison of two non-

destructive inspection techniques on the basis of sensitivity

and reliability. Materials & Design 28(5): 1433–1439.Siow YP and Shim VPW (1998) An experimental study of

low velocity impact damage in woven fiber composites.

Journal of Composite Materials 32(12): 1178–1201.Todd MD, Johnson GA and Althouse BL (2001) A novel

Bragg grating interrogation system utilizing a scanning fil-

ter, a Mach–Zehnder interferometer and a 3 3 3 coupler.

Measurement Science and Technology 12: 771–771.Yu Y-H, Choi J-H, Kweon J-H and Kim D-H (2006) A study

on the failure detection of composite materials using an

acoustic emission. Composite Structures 75: 163–169.

C. Hiche et al. 2067