Testing of Polymers and Composite Materials

  • Reference work entry
  • First Online:
Handbook of Nondestructive Evaluation 4.0
  • 1981 Accesses

Abstract

This chapter presents advanced imaging and sensing technologies for NDE of polymer and polymer matrix-based composite materials and highlights how concepts such as artificial intelligence, data mining, and advanced computing can be used to improve our ability to identify damage in these materials. Recent advances in high-resolution imaging methods are generating large data sets relating to the failure of these materials under a wide variety of load conditions. Additionally, distributed sensor networks are often being incorporated into these materials for real-time inspection of structural components. Automating imaging and nondestructive evaluation (NDE) processes for polymer and composite materials and taking advantage of advances in artificial intelligence based processing methods is therefore critical to our ability to effectively use the results of these advanced testing infrastructure to understand the failure of existing materials systems and the design of new ones in the future. The chapter first describes the types of defects that need to be detected and analyzed. These defects can either be introduced during the fabrication of the composite materials or be created due to failure of the material during loading of the composite. Afterwards, several current NDE inspection methods for composite materials are described, in addition to opportunities to automate data processing for these inspections using artificial intelligence-based processing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 799.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 849.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wunderlich C, Tschöpe C, Duckhorn F. Advanced methods in NDE using machine learning approaches. In: 44th annual review of progress in quantitative nondestructive evaluation. 2018.

    Google Scholar 

  2. Garcea SC, Wang Y, Withers PJ. X-ray computed tomography of polymer composites. Compos Sci Technol. 2018;156:305–19. https://doi.org/10.1016/j.compscitech.2017.10.023.

    Article  CAS  Google Scholar 

  3. Croom B, Wang WM, Li J, Li X. Unveiling 3D deformations in polymer composites by coupled micro X-ray computed tomography and volumetric digital image correlation. Exp Mech. 2016;56:999–1016. https://doi-org.prox.lib.ncsu.edu/10.1007/s11340-016-0140-7

    Article  CAS  Google Scholar 

  4. Ihn JB, Chang FK. Detection and monitoring of hidden fatigue crack growth using a built-in piezoelectric sensor/actuator network: I. Diagnostics. Smart Mater Struct. 2004;13:609–20. https://doi.org/10.1088/0964-1726/13/3/020.

    Article  Google Scholar 

  5. Daniel IM, Ishai O. Engineering mechanics of composite materials. Chapter 2. New York: Oxford University Press; 2006.

    Google Scholar 

  6. Kedari VR, Farah BI, Hsiao KT. Effects of vacuum pressure, inlet pressure, and mold temperature on the void content, volume fraction of polyester/e-glass fiber composites manufactured with VARTM process. J Compos Mater. 2011;45:2727–42. https://doi.org/10.1177/0021998311415442.

    Article  CAS  Google Scholar 

  7. Daniel IM, Ishai O. Engineering mechanics of composite materials. Chapter 5. New York: Oxford University Press; 2006.

    Google Scholar 

  8. Wang Y, Burnett T, Chai Y, Soutis C, Hogg PJ, Withers PJ. X-ray computed tomography study of kink bands in unidirectional composites. Compos Struct. 2017;160:917–24. https://doi.org/10.1016/j.compstruct.2016.10.124.

    Article  Google Scholar 

  9. Schmidt F, Rheinfurth M, Protz R, Horst P, Busse G, Gude M, Hufenbach. Monitoring of multiaxial fatigue damage evolution in impacted composite tubes using non-destructive evaluation. Compos Part A. 2012;43:537–46. https://doi.org/10.1016/j.compositesa.2011.12.002.

    Article  CAS  Google Scholar 

  10. Pawar SS, Peters K. Through-the thickness identification of impact damage in composite laminates through pulsed phase thermography. Meas Sci Technol. 2013;24:115601. https://doi.org/10.1088/0957-0233/24/11/115601.

    Article  CAS  Google Scholar 

  11. Hsu DK. Non-destructive evaluation (NDE) of aerospace composites: ultrasonic techniques. In: Karbhari VM, editor. Non-destructive evaluation (NDE) of polymer matrix composites. Elsevier Science & Technology; 2013.

    Google Scholar 

  12. Wronkowicz-Katunin A, Katunin A, Dragan K. Reconstruction of barely visible impact damage in composite structures based on non-destructive evaluation results. Sensors. 2019;19:4629. https://doi.org/10.3390/s19214629.

    Article  Google Scholar 

  13. Smith RA, Nelson LJ, Mienczakowski MJ, Challis RE. Automated analysis and advanced defect characterization from ultrasonic scans of composites. Insight. 2009;13:82–7. https://doi.org/10.1784/insi.2009.51.2.82.

    Article  Google Scholar 

  14. Guerjouma RE, Marec A, Nechad H, Thomas JH. Non-destructive evaluation and testing and structural health monitoring of composite materials by ultrasound and acoustic emission. In: Bruneau M, Potel C, editors. Materials and acoustics handbook. ISTE; 2009.

    Google Scholar 

  15. Castaigns M. Linear methods of ultrasonic non-destructive testing and evaluation. In: Bruneau M, Potel C, editors. Materials and acoustics handbook. ISTE; 2009.

    Google Scholar 

  16. Drinkwater BW, Wilcox PD. Ultrasonic arrays for non-destructive evaluation: a review. NDT&E Int. 2006;39:525–41. https://doi.org/10.1016/j.ndteint.2006.03.006.

    Article  CAS  Google Scholar 

  17. Leo M, Looney D, D’Orazio T, Mandic DP. Identification of defective areas in composite materials by bivariate EMD analysis of ultrasound. IEEE Trans Instrum Meas. 2012;61:221–32. https://doi.org/10.1109/TIM.2011.2150630.

    Article  Google Scholar 

  18. D’Orazio T, Leo M, Distante A, Guaragnella C, Pianese V, Gavaccini G. Automatic ultrasonic inspection for internal defect detection in composite materials. NDT&E Int. 2008;41:145–54. https://doi.org/10.1016/j.ndteint.2007.08.001.

    Article  Google Scholar 

  19. Meksen TM, Boudraa B, Boudraa M. Defects clustering using Kohonen networks during ultrasonic inspection. IAENG Int J Comput Sci. 2009;36:225–8.

    Google Scholar 

  20. Simas Filho EF, Souza YN, Lopes JLS, Farias CTT, Albuquerque MCS. Decision support system for ultrasound inspection of fiber metal laminates using statistical signal processing and neural networks. Ultrasonics. 2013;53:1104–11. https://doi.org/10.1016/j.ultras.2013.02.005.

    Article  Google Scholar 

  21. Avdelidis NP, Ibarra-Castanedo C, Maldague X, Marioli-Riga ZP, Almond DP. A thermographic comparison study for the assessment of composite patches. Infrared Phys Technol. 2004;45:291–9. https://doi.org/10.1016/j.infrared.2004.01.001.

    Article  Google Scholar 

  22. Cowley P. The rapid non-destructive inspection of large composite structures. Composites. 1994;25:351–7. https://doi.org/10.1016/S0010-4361(94)80005-7.

    Article  Google Scholar 

  23. Ibarra-Castanedo C, Genest M, Servais P, Maldague X, Bendada A. Qualitative and quantitative assessment of aerospace structures by pulsed thermography. Nondestruct Test Eval. 2007;22:199–215. https://doi.org/10.1080/10589750701448548.

    Article  Google Scholar 

  24. Maldague X. Theory and practice of infrared technology for nondestructive testing. New York: Wiley; 2001.

    Google Scholar 

  25. Corvaglia P, Galietti U, Largo A, Nenna S, Spagnolo L. Feasibility of different thermal analysis of FRP–reinforced concrete. In: 8th international conference on quantitative infrared thermography. 2006.

    Google Scholar 

  26. Maldague X, Marinetti S. Pulse phase infrared thermography. J Appl Phys. 1996;79:2694–8.

    Article  CAS  Google Scholar 

  27. Fernandes H, Zhang H, Figueiredo A, Malheiros F, Igancio LH, Sfarra S, Ibarra-Castanedo C, Guimaraes G, Maldague X. Machine learning and infrared thermography for fiber orientation assessment on randomly-oriented strands parts. Sensors. 2018;18:288. https://doi.org/10.3390/s18010288.

    Article  Google Scholar 

  28. Pethrick RA. Non-destructive evaluation (NDE) of composites: dielectric techniques for testing partially conducting composite materials. In: Karbhari VM, editor. Non-destructive evaluation (NDE) of polymer matrix composites. Elsevier Science & Technology; 2013.

    Google Scholar 

  29. Kazilas MC, Partridge IK. Exploring equivalence of information from dielectric and calorimetric measurements of thermoset cure – a model for the relationship between curing temperature, degree of cure and electrical impedance. Polymer. 2005;46:5868–78. https://doi.org/10.1016/j.polymer.2005.05.005.

    Article  CAS  Google Scholar 

  30. Boinard P, Boinard E, Pethrick RA, Banks WM, Crane RL. Dielectric spectroscopy as a non-destructive technique to assess water sorption in composite materials. Sci Eng Compos Mater. 1999;8:175–9.

    Article  CAS  Google Scholar 

  31. Bull DJ, Helfen L, Sinclair I, Spearing SM, Baumbach T. A comparison of multi-scale 3D X-ray tomographic inspection techniques for assessing carbon fibre composite impact damage. Compos Sci Technol. 2013;75:55–61. https://doi.org/10.1016/j.compscitech.2012.12.006.

    Article  CAS  Google Scholar 

  32. Sammons D, Winfree WP, Burke E, Ji S. Segmenting delaminations in carbon fiber reinforced polymer composite CT using convolutional neural networks. AIP Conf Proc. 2016;1706:110014. https://doi.org/10.1063/1.4940585.

    Article  Google Scholar 

  33. Maiti A, Venkat A, Kosiba GD, Shaw WL, Sain JD, Lindsey RK, Grant CD, Bremer PT, Gyulassy AG, Pascucci V, Gee RH. Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging. Comput Mater Sci. 2020;182:109782. https://doi.org/10.1016/j.commatsci.2020.109782.

    Article  CAS  Google Scholar 

  34. Badran A, Marshall D, Legault Z, Makovetsky R, Provencher B, Piché N, Marsh M. Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning. J Mater Sci. 2020;55:16273–89. https://doi.org/10.1007/s10853-020-05148-7.

    Article  CAS  Google Scholar 

  35. Topal E, Löffler M, Zschech E. Deep learning-based inaccuracy compensation in reconstruction of high resolution XCT data. Sci Rep. 2020;10:7682. https://doi.org/10.1038/s41598-020-64733-7.

    Article  CAS  Google Scholar 

  36. Capriotti M, Kim HE, Lanza di Scalea F, Kim H. Non-destructive inspection of impact damage in composite aircraft panels by ultrasonic guided waves and statistical processing. Materials. 2017;10. https://doi.org/10.3390/ma10060616.

  37. Radzienski M, Kudela P, Marzani A, de Marchi L, Ostachowicz W. Damage identification in various types of composite plates using guided waves excited by a piezoelectric transducer and measured by a laser vibrometer. Sensors. 2019;19:1958. https://doi.org/10.3390/s19091958.

    Article  CAS  Google Scholar 

  38. Hosseini SMH, Duczek S, Gabbert U. Damage localization in plates using mode conversion characteristics of ultrasonic guided waves. J Nondestruct Eval. 2014;33:152–65. https://doi.org/10.1007/s10921-013-0211-y.

    Article  Google Scholar 

  39. Sherafat M, Guitel R, Quaegebeur N, Lessard L, Hubert P, Masson P. Guided wave scattering behavior in composite bonded assemblies. Compos Struct. 2016;136:696–705. https://doi.org/10.1016/j.compstruct.2015.10.046.

    Article  Google Scholar 

  40. Giri P, Kharkovsky S, Zhu X, Clark SM, Samali B. Debonding detection in a carbon fiber reinforced concrete structure using guided waves. Smart Mater Struct. 2019;28:045020. https://doi.org/10.1088/1361-665X/ab0b6e.

    Article  CAS  Google Scholar 

  41. Rajic N, Davis C, Thomson A. Acoustic-wave-mode separation using a distributed Bragg grating sensor. Smart Mater Struct. 2009;18:125005. https://doi.org/10.1088/0964-1726/18/12/125005.

    Article  Google Scholar 

  42. Mustafa S, Ye L. Non-destructive evaluation (NDE) of composites: assessing debonding in sandwich panels using guided waves. In: Karbhari VM, editor. Non-destructive evaluation (NDE) of polymer matrix composites. Elsevier Science & Technology; 2013.

    Google Scholar 

  43. Willberg C, Koch S, Mook G, Pohl J, Gabbert U. Continuous mode conversion of lamb waves in CFRP plates. Smart Mater Struct. 2012;21:075022. https://doi.org/10.1088/0964-1726/21/7/075022.

    Article  Google Scholar 

  44. Hudson T, Hou TH, Grimsley BW, Yuan FG. Imaging of local porosity/voids using a fully non-contact air-coupled transducer and laser Doppler vibrometer system. Struct Health Monit. 2017;16:164–73. https://doi.org/10.1177/1475921716668843.

    Article  Google Scholar 

  45. Giurgiutiu V. Structural health monitoring with piezoelectric wafer active sensors. Amsterdam: Academic; 2008.

    Google Scholar 

  46. Ostachowicz WM. Guided waves in structures for SHM: the time-domain spectral element method. Chichester: Wiley; 2012.

    Book  Google Scholar 

  47. Melville J, Alguri KS, Deemer C, Harley JB. Structural damage detection using deep learning of ultrasonic guided waves. In: 44th annual review of progress in quantitative nondestructive evaluation. 2018. https://doi.org/10.1063/1.5031651.

  48. Nair AK, Machavaram VR, Mahendran RS, Pandita SD, Paget C, Barrow C, Fernando GF. Process monitoring of fiber reinforced composites using a multi-measurand fibre-optic sensor. Sensors Actuators B Chem. 2015;212:93–106. https://doi.org/10.1016/j.snb.2015.01.085.

    Article  CAS  Google Scholar 

  49. Etches JA, Fernando GF. Evaluation of embedded optical fiber sensors in composites: EFPI sensor fabrication and quasi-static evaluation. Polym Compos. 2009;30:1265–74. https://doi-org.prox.lib.ncsu.edu/10.1002/pc.20690

    Article  CAS  Google Scholar 

  50. Oman K, van Hoe B, Aly K, Peters K, van Steenberge G, Stan N, Schultz S. Instrumentation of integrally stiffened composite panel with fiber Bragg grating sensors for vibration measurements. Smart Mater Struct. 2015;24:085031. https://doi.org/10.1088/0964-1726/24/8/085031.

    Article  CAS  Google Scholar 

  51. Maung P, Prusty BG, White JM, David M, Phillips AW, St John NA. Structural performance of a shape-adaptive composite hydrofoil using automated fibre placement. Eng Struct. 2019;183:351–65. https://doi.org/10.1016/j.engstruct.2019.01.014.

    Article  Google Scholar 

  52. Grattan KTV, Meggitt BT. Optical fiber sensor technology: fundamentals. Boston: Kluwer Academic; 2000.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kara Peters .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Peters, K. (2022). Testing of Polymers and Composite Materials. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds) Handbook of Nondestructive Evaluation 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-73206-6_25

Download citation

Publish with us

Policies and ethics

Navigation