Electrical resistance tomography is a method for sensing the spatial distribution of electrical conductivity. Therefore, this type of tomography is suitable for sensing damages, which affect electrical conductivity. The utilization of resistance tomography for the structural health monitoring of carbon fiber–reinforced polymer composites is questionable owing to its low spatial resolution and the strong anisotropy of carbon fiber–reinforced polymer composites. This article deals with the employment of resistance tomography with regularization based on a Gaussian anisotropic smoothing filter for the detection of cuts. The advantages of the filter are shown through the image reconstruction of rectangular composite specimens with three different laminate stacking sequences. The cuts are implemented by a milled groove. Visual comparison of the images shows a substantial improvement in the shape reconstruction ability. In addition to visual comparison, the image reconstructions are assessed in terms of the reconstruction error and cross-correlation.
AzzamHMcFeatJ. Development, validation, verification and certification of structural health monitoring systems for military aircraft. Paper 123, Military Aircraft Structural Airworthiness Advisory Group (MASAAG), 2016 United Kingdom Ministry of Defence and Military Aviation Authority.
2.
GardnierG.Structural health monitoring: NDT-integrated aerostructures enter service. Compositesworld2015; 1(8): 42–49.
3.
MichalcováLKadlecM.Carbon/epoxy composite delamination analysis by acoustic emission method under various environmental conditions. Eng Fail Anal2016; 69: 88–96.
4.
MichalcováLHronR.Quantitative evaluation of delamination in composites using lamb waves. IOP Conf Ser: Mat Sci2018; 326(1): 012006.
5.
RuzekRKudrnaPKadlecM, et al. Strain and damage monitoring in CFRP fuselage panels using fiber Bragg grating sensors. Part II: mechanical testing and validation. Compos Struct2014; 107: 737–744.
6.
LiuXDengY.Electromagnetic imaging methods for nondestructive evaluation applications. Sensors2011; 11(12): 11774–11808.
7.
FrerichsI.Electrical impedance tomography (EIT) in applications related to lung and ventilation: a review of experimental and clinical activities. Physiol Meas2000; 21(2): R1–R21.
8.
HolderDS.Feasibility of developing a method of imaging neuronal activity in the human brain: a theoretical review. Med Biol Eng Comput1987; 25(1): 2–11.
9.
CherepeninVKarpovAKorjenevskyA, et al. A 3D electrical impedance tomography (EIT) system for breast cancer detection. Physiol Meas2001; 22(1): 9–18.
10.
PellerinLWannamakerPE.Multi-dimensional electromagnetic modeling and inversion with application to near-surface earth investigations. Comput Electron Agr2005; 46(1): 71–102.
11.
Process tomography: principles, techniques and applications. 1st ed.Oxford; Boston, MA: Butterworth-Heinemann, 1995.
12.
BaltopoulosAPolydoridesNPambaguianL, et al. Damage identification in carbon fiber reinforced polymer plates using electrical resistance tomography mapping. J Compos Mater2013; 47(26): 3285–3301.
13.
CalvanoFRubinacciGTamburrinoA.Fast methods for shape reconstruction in electrical resistance tomography. NDT&E Int2012; 46: 32–40.
14.
GschoßmannSZhaoYSchagerlM. Development of data acquisition devices for electrical impedance tomography of composite materials. In: Proceedings of the 17th European conference on composite materials (ECCM17), vol. 17, Munich26–30th June 2016.
15.
ZarafshaniAChatwinCBachT, et al. Using planar electrical impedance tomography as a structural health monitoring method to detect and evaluate the damage to CFRP composite. In: 2016 IEEE national aerospace and electronics conference (NAECON) and Ohio Innovation Summit (OIS), Dayton, OH, 25–29 July, pp. 74–79. New York: IEEE.
16.
TallmanTNHernandezJA.The effect of error and regularization norms on strain and damage identification via electrical impedance tomography in piezoresistive nanocomposites. NDT&E Int2017; 91: 156–163.
17.
TallmanTNGungorSKooGM, et al. On the inverse determination of displacements, strains, and stresses in a carbon nanofiber/polyurethane nanocomposite from conductivity data obtained via electrical impedance tomography. J Intel Mat Syst Str2017; 28(18): 2617–2629.
18.
NonnSSchagerlMZhaoY, et al. Application of electrical impedance tomography to an anisotropic carbon fiber-reinforced polymer composite laminate for damage localization. Compos Sci Technol2018; 160: 231–236.
19.
BaltopoulosA. Sensing capabilities of multifunctional composite materials using carbon nanotubes. In: 61st international astronautical congress 2010 (IAC 2010), p. 13. 27 September and 1 October 2010.
20.
Silvera-TawilDRyeDSoleimaniM, et al. Electrical impedance tomography for artificial sensitive robotic skin: a review. IEEE Sens J2015; 15(4): 2001–2016.
21.
LoyolaBRSaponaraVLLohKJ, et al. Spatial sensing using electrical impedance tomography. IEEE Sens J2013; 13(6): 2357–2367.
22.
LohKJHouTCLynchJP, et al. Carbon nanotube sensing skins for spatial strain and impact damage identification. J Nondestruct Eval2009; 28(1): 9–25.
23.
HouTCLohKJLynchJP.Spatial conductivity mapping of carbon nanotube composite thin films by electrical impedance tomography for sensing applications. Nanotechnol2007; 18(31): 315501.
24.
DaiHGalloGJSchumacherT, et al. A novel methodology for spatial damage detection and imaging using a distributed carbon nanotube-based composite sensor combined with electrical impedance tomography. J Nondestruct Eval2016; 35(2): 1–15.
25.
TallmanTNGungorSWangKW, et al. Damage detection via electrical impedance tomography in glass fiber/epoxy laminates with carbon black filler. Struct Health Monit2015; 14(1): 100–109.
26.
GalloGJThostensonET.Spatial damage detection in electrically anisotropic fiber-reinforced composites using carbon nanotube networks. Compos Struct2016; 141: 14–23.
27.
ZhangDYeLWangD, et al. Assessment of transverse impact damage in GF/EP laminates of conductive nanoparticles using electrical resistivity tomography. Compos Part A: Appl S2012; 43(9): 1587–1598.
28.
BaltopoulosAPolydoridesNPambaguianL, et al. Exploiting carbon nanotube networks for damage assessment of fiber reinforced composites. Compos Part B: Eng2015; 76: 149–158.
29.
KarhunenKSeppänenALehikoinenA, et al. Electrical resistance tomography imaging of concrete. Cement Concrete Res2010; 40(1): 137–145.
30.
HallajiMSeppänenAPour-GhazM.Electrical impedance tomography-based sensing skin for quantitative imaging of damage in concrete. Smart Mater Struct2014; 23(8): 085001.
31.
HallajiMSeppänenAPour-GhazM.Electrical resistance tomography to monitor unsaturated moisture flow in cementitious materials. Cement Concrete Res2015; 69: 10–18.
32.
ReichlingKRaupachMKlitzschN.Determination of the distribution of electrical resistivity in reinforced concrete structures using electrical resistivity tomography. Mater Corros2015; 66(8): 763–771.
33.
ZhangTZhouLAmmariH, et al. Electrical impedance spectroscopy-based defect sensing technique in estimating cracks. Sensors2015; 15(5): 10909–10922.
34.
Electrical impedance tomography: methods, history and applications. Bristol; Philadelphia, PA: CRC Press, 2004.
35.
VauhkonenMVadaszDKarjalainenPA, et al. Tikhonov regularization and prior information in electrical impedance tomography. IEEE T Med Imaging1998; 17(2): 285–293.
36.
AdlerAGuardoR.Electrical impedance tomography: regularized imaging and contrast detection. IEEE T Med Imaging1996; 15(2): 170–179.
37.
BorsicALionheartWRBMcLeodCN.Generation of anisotropic-smoothness regularization filters for EIT. IEEE T Med Imaging2002; 21(6): 579–587.
38.
SomersaloECheneyMIsaacsonD.Existence and uniqueness for electrode models for electric current computed tomography. SIAM J Appl Math1992; 52(4): 1023–1040.
39.
CagáňJ.Hardware implementation of electrical resistance tomography for damage detection of carbon fibre–reinforced polymer composites. Struct Health Monitor2017; 16(2): 129–141.
40.
PolydoridesNLionheartWRB. A Matlab toolkit for three-dimensional electrical impedance tomography: a contribution to the Electrical Impedance and Diffuse Optical Reconstruction Software project. Meas Sci Technol2002; 13(12): 1871–1883.
41.
KyriakosP.The inverse conductivity problem: anisotropy, finite elements and resistor networks. PhD Thesis, School of Mathematics, The University of Manchester, Oxford, 2013.
42.
CagáňJRöslerJ.Design of multiplexer for electrical impedance tomography. Mater Today: Proc2017; 4(5 Pt 1): 5755–5760.
