Reliable failure detection and prediction in laminated composite materials are critical for the utilization of these materials. Many quantitative techniques have been successfully researched and implemented for failure detection and prediction. Most of them are based on non-destructive methods for data collection and are quantified by using neural networks. In this paper a review of the subject is presented. The non-destructive techniques considered are limited to natural frequencies, electric conductivity, and lamb waves. The paper gives a brief introduction to each technique followed by summaries and evaluations for some relevant studies.
References
1.
TodorokiA., The Effect of Number of Electrodes and Diagnostic Tool for Monitoring the Delamination of CFRP Laminates by Changes in Electrical Resistance, Composites Science and Technology, 61 (2001), 1871–1880.
2.
GrahamaD., MaasP., DonaldsonaG. B., and CarrC., Impact Damage Detection in Carbon Fibre Composites Using HTS SQUIDs and Neural Networks CARRB, NDT&E International, 37 (2004), 565–570.
3.
ChakrabortyD., Artificial Neural Network Based Delamination Prediction in Laminated Composites, Material and Design, 26 (2004), 1–7.
4.
ShenfangY., LeiW., and GeP., Neural Network Method Based on a New Damage Signature for Structural Health Monitoring, Thin-Walled Structures, 43 (2005), 553–563.
5.
MacherleJ., Finite-Element Modelling of Non-Destructive Material Evaluation, and Addendum: a Bibliography, (1997-2003), Modelling and Simulation in Materials Science and Engineering, 12 (2004), 799–834.
6.
SuZ., and YeL., Lamb Wave-Based Quantitative Identification of Delamination in CF/EP Composite Structures using Artificial Neural Algorithm, Composite Structures, 66 (2004), 627–637
7.
SuZ., and YeL., Lamb Wave Propagation-Based Damage Identification for Quasi-Isotropic CF/EP Composite Laminates using Artificial Neural Algorithm, Part I: Methodology and Database Development. J. Intell Mater Syst. Struct. in Press, (2005), 97–111.
8.
RobinsonT., HochbergM., and RenalsS., The Use of Recurrent Neural Networks in Continuous Speech Recognition, Kluwer Academic Publishers, Boston, MA, 1996.
9.
NakanoH., OkamotoY., NakabayashiH., TakamatsuS., TsujiiH., and MatsuokaH., Application of Neural Network to the Interpretation of Laboratory Data for the Diagnosis of Two Forms of Chronic Active Hepatitis, International Hepatology Communications, 5, (1996), 160–165.
10.
AitkenheadM. J., and McDonaldA. J. S., A Neural Network Face Recognition System, Engineering Applications of Artificial Intelligence, 16, (2003), 167–176.
11.
HenY., and HwangJ. N., Handbook of Neural Network Signal Processing, CRC press, Boca Raton, 2001.
12.
PoggioT., and GirosiF., Networks for Approximation and Learning, Proceedings of the IEEE, 1990. Technical Report 1140, AIM, 1989.
13.
SangerT. D., Optimal Unsupervised Learning in a Single Layer Linear Feed-Forward Neural Network, Neural Networks, 2 (1989), 459–473.
14.
PhamD., and LiuX., Neural Networks for Identification, Prediction, and Control, Berling: Springer, 1995.
15.
BishopC., Neural Networks for Pattern Recognition, Oxford: Clarendon Press, 1995.
16.
ZarembaT., Technology in Search of a Buck. Neural Network PC Tools, London: Academic Press, 1995.
17.
KhandetskyV., and AntonyukI., Signal Processing in Defect Detection Using Back Propagation Neural Networks, NDT&E Int, 35 (2002), 483–488.
18.
StergiouC., and SiganosD., Neural Networks, Report:Imperial College, London.
19.
KhemiriN., and IrvingP., Experimental and Finite Element Study of the Electrical Potential Technique for Damage Detection in CFRP Laminates, N Angelidis1, Smart Material Structures, 14 (2005), 147–154.
20.
ChangF., Structural Health Monitoring: A Summary Report, Proceedings of the 2nd International Workshop on Structural Health Monitoring, Stanford, CA, (1999), 8–10.
21.
GiurgiutiuV., JingjingB., and ZhaoW., Active Sensor Wave Propagation Health Monitoring of Bean and Plate Structures, Proceedings of the SPIE International Symposium on Smart Structures and Material, Newport Beach, Ca, (2001), 5–8.
22.
KesslerS., SpearingS., AtallaM., CesnikC., and SoutisC., Damage Detection in Composite Materials Using Frequency Response Methods, Composites Part B: Engineering, 33 (2002), 87–95.
23.
MujumdarP. M., and SuryanarayanS., Flexural Vibrations of Beams with Delaminations, Sound and Vibration, 125 (1988), 441–461.
24.
CawlyP., and AdamsR., A Vibration Technique for Non-Destructive Testing of Fibre Composite Structures, Composite Material, 13 (1979), 161–175.
25.
DugganM., and OchoaO., Natural Frequency Behaviour of Damaged Composite Materials, Sound and Vibration, 158 (1992), 545–551.
26.
KulkarniS., and FredrickD., Frequency as a Parameter in Delamination Problem - a Preliminary Investigation, Composite Materials, 1971.
27.
KaminskP. C., The Approximate Location of Damage Through the Analysis of Natural Frequencies with Artificial Networks, Process Mechanical Engineering, 209 (1995), 117–123.
28.
IslamA. S., and CraigK. C., Damage Detection in Composite Structures Using Piezoelectric Materials, Smart Material Structures, 3 (1994), 318–328.
29.
ReddyA. D., RehfieldL. W., and HaagR. S., Influence of Prescribed Delaminations on Stiffness Controlled Behaviour of Composite Laminates Effects of Defects in Composite Materials, ASTM STP 836 Am. Soc. for Testing Mater, 1984.
30.
DuaR., WatkinsS. E., WunschD. C., ChandrashekharaK., and AkhavanF., Detection and Classification of Impact Induced Damage in Composite Plates using Neural Networks, Proceedings of the IJCNN, Washington D. C., (2001), 681–686.
31.
JonesR. T., and SirkisJ. S., Detection of Impact Location and Magnitude for Isotropic Plates Using Neural Networks, Intelligent Material Systems and Structures, 7 (1997), 90–99.
32.
CeravoloR., De StefanoA., and SabiaD., Hierarchical use of Neural Techniques in Structural Damage Recognition, Smart Material Structures, 4 (1995), 270–280.
33.
LiC., and AsokR., Neural Network Representation of Fatigue Damage Dynamics, Smart Material Structures, 4 (1995), 126–133.
34.
AkhavanF., WatkinsS. E., and ChandrashekharaK., Predictions of Impact Contact Forces of Composite Plates Using Fiber Optic Sensors and Neural Networks, Mechechanics of Composite Materials Structure, 7 (2000), 195–205.
35.
WatkinsS. E., Gilbert SandersW., FarhadA., and ChandrashekharaK., Model Analysis Using Fiber Optic Sensors and Neural Networks for Prediction of Composite Beam Delamination, Smart Material Structures, 11 (2002), 489–495.
36.
CrispinD., and GerardF., Detecting Impact Damage in a Composite Material with an Optical Fiber Vibration Sensor System, Smart Material Structures, 7 (1998), 543–549.
37.
AkhavanF., WatkinsS. E., and ChandrashekharaK., Measurement and Analysis of Impact-Induced Strain Using Extrinsic Fabry-Perot Fiber Optic Sensors, Smart Material Structures, 7 (1998), 745–751.
38.
SeoD., and LeeJ., Effect of Embedded Optical Fiber Sensors on Transverse Crack of Smart Composite Strctures, Composite Structures, 32 (1995), 51–58.
39.
LeeD., LeeJ., and YunS., The Mechanical Characteristics of Smart Composite Structures with Embedded Optical Fiber Sensors, Compsoste Structures, 32 (1995), 39–50.
40.
IrvingP., and ThiagarajanC., Fatigue Damage Characterization in Carbon Fiber Composite Materials Using an Electric Potential Technique, Smart Materials and Structures, 7 (1998), 456–466.
41.
AbryJ., BochardS., ChateauminoisA., SalviaM., and GiraudG., In-situ Detection of Damage in CFRP Laminates by Electric Resistance Measurements, Composites Science and Technology, 59 (1999), 925–935.
42.
Dae-CheolS., and Jung-JuL., Damage Detection in CFRP Laminates Using Electrical Resistance Measurement and Neural Network, Composite Structures, 47 (1999), 525–530.
43.
TodorokiA., and SuzukiH., Health Monitoring of Internal Delamination Cracks for Graphite/ Epoxy by Electric Potential Method, Applied Mechanics of Engineering, 5 (2000), 238–294.
44.
TodorokiA., and TanakaY., Delamination Identification of Cross -Ply Graphite/Epoxy Composite Beams Using Electric Resistance Change Method, Composites Science and Technology, 62 (2001), 629–639.
45.
TodorokiA., TanakaY., and ShimamuraY., Measurement of Orthotropic Electric Conductance of CFRP Laminates and Analysis of the Effect on Delamination Monitoring with Electric Resistance Change Method, Composites Science and Technology, 62 (2002), 619–628.
46.
TodorokiA., TanakaY., and DelaminationY., Monitoring of Graphite / Epoxy Laminated Composite Plate of Electric Resistance Change Method, Composites Science and Technology, 62 (2002), 1151–1160.
47.
TodorokiA., TanakaY., and ShimamuraY., Electrical Resistance Change Method for Monitoring Delaminations of CFRP Laminates: Effect of Spacing Between Electrodes, Composites Science and Technology, 65 (2005), 37–46.
48.
LambH., OnWaves in an Elastic Plate, Proceedings of the Royal Society of London, 93 (1917), 114–128.
49.
DiamantiaK., SoutisbC., and HodgkinsonJ., Lamb Waves for the Non-Destructive Inspection of Monolithic and Sandwich Composite Beams, Composites: Part A, 36 (2005), 189–195.
50.
WorltonD. C., Experimental Confirmation of Lamb Waves at Megacycle Frequencies, Applied Physics, 32 (1961), 967–971.
51.
SuZ., and YeL., Lamb Wave-Based Quantitative Identification of Delamination in CF/EP Composite Structures Using Artificial Neural Algorithm, Composite Structures, 66 (2004), 627–637.
