Advertisement

Detection of Crack Initiation and Propagation in Aluminum Alloy Under Tensile Loading, Comparing Signals Acquired by Acoustic Emission and Vibration Sensors

  • Hassan Sayar
  • Mohammad AzadiEmail author
  • Mohsen Alizadeh
Article
  • 31 Downloads

Abstract

The application of aluminum alloys in various industries such as automotive and aerospace, is inclusive. In addition, in these industries, holes in these materials are used for bolts and rivets. Accordingly, in this article, the initiation and the propagation of cracks in the 2024 aluminum alloy were detected, by means of two methods including acoustic emission and vibration analysis approaches. For this objective, acoustic and vibration sensors were connected to the open-hole aluminum specimen under tensile loading and signals were acquired. Obtained results indicated that the energy of signals, which was recorded by sensors, was comparable to the stress–strain diagram and therefore, the efficiency of two methods in detecting the crack initiation was proved. The calculated maximum stress at the specimen edge by the vibration analysis was closer to experimental data, in comparison to the acoustic emission approach. Then, the fracture frequency of the aluminum alloy was calculated using two mentioned methods and by the fast Fourier transform. By the use of the cumulative energy, which was calculated from recorded signals, the crack propagation was also detected by both approaches and a better efficiency for predicting the fracture by the acoustic emission method was shown. At the end, images of the scanning electron microscopy from the crack and the fracture surface were also demonstrated.

Keywords

Aluminum alloy Crack detection Acoustic emission Vibration analysis Fast Fourier transform 

Notes

Acknowledgements

Authors should thank Irankhodro Powertrain Company (IPCo.) for the financial support and providing the equipment for the acoustic emission approach and the vibration analysis. Authors also tend to have a special thanks to Dr. S.M. Jafari and Dr. A. Moosavian, for their guidance.

References

  1. 1.
    Mathers, G.: The Welding of Aluminum and its Alloy. Woodhead Publishing, Cambridge (2002)CrossRefGoogle Scholar
  2. 2.
    Khazaee, M., Ahmadi, H., Omid, M., Moosavian, A., Khazaee, M.: Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory. Proc. Inst. Mech. Eng. Part E 228(1), 21–32 (2014)CrossRefGoogle Scholar
  3. 3.
    Grosse, C.U., Masayasu, O.: Acoustic Emission Testing. Springer, New York (2008)CrossRefGoogle Scholar
  4. 4.
    Baydar, N., Ball, A.: A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. Mech. Syst. Signal Process. 15(6), 1091–1107 (2001)CrossRefGoogle Scholar
  5. 5.
    Saxena, A., Saad, A.: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl. Soft Comput. 7(1), 441–454 (2007)CrossRefGoogle Scholar
  6. 6.
    Rafiee, J., Arvani, F., Harifi, A., Sadeghi, M.H.: Intelligent condition monitoring of a gearbox using artificial neural network. Mech. Syst. Signal Process. 21(4), 1746–1754 (2007)CrossRefGoogle Scholar
  7. 7.
    Cousland, K., Scala, C.M.: Acoustic emission during the plastic deformation of aluminum alloys 2024 and 2124. Mater. Sci. Eng. 57, 23–29 (1983)CrossRefGoogle Scholar
  8. 8.
    Blanchette, Y., Dickson, J.I., Bassim, M.N.: Acoustic emission behavior crack growth of 7075-T651 Al alloy. Eng. Fract. Mech. 24(5), 647–656 (1986)CrossRefGoogle Scholar
  9. 9.
    Yang, D., Wang, J., Li, D., Kuang, K.S.C.: Fatigue crack monitoring using plastic optical fiber sensor. Procedia Struct. Integr. 5, 1168–1175 (2017)CrossRefGoogle Scholar
  10. 10.
    Sayar, H., Alizadeh, M., Azadi, M., Ghasemi-Ghalebahman, A., Jafari, S.M.: Investigation of crack growth behavior in aluminum alloy under low-cycle fatigue loading using a acoustic emission method. In: 26th Annual International Conference of Iranian Society of Mechanical Engineers, Semnan, Iran, April 24–26, 2018Google Scholar
  11. 11.
    Sayar, H., Alizadeh, M., Azadi, M., Ghasemi-Ghalebahman, A., Jafari, S.M., Mafi, A.: Investigation of crack growth behavior in aluminum alloy used in engine components, by acoustic emission method. J. Engine Res. 48, 3–12 (2017)Google Scholar
  12. 12.
    Chai, M., Zhang, J., Zhang, Z., Duan, Q., Cheng, G.: Acoustic emission studies for characterization of fatigue crack growth in 316LN stainless steel and welds. Appl. Acoust. 126, 101–113 (2017)CrossRefGoogle Scholar
  13. 13.
    Rivera, F.G., Edwards, G., Eren, E., Soua, S.: Acoustic emission technique to monitor crack growth in a mooring chain. Appl. Acoust. 139, 156–164 (2018)CrossRefGoogle Scholar
  14. 14.
    Hao, Q., Zhang, X., Wang, K., Shen, Y., Wang, Y.: A signal-adapted wavelet design method for acoustic emission signals of rail cracks. Appl. Acoust. 139, 251–258 (2018)CrossRefGoogle Scholar
  15. 15.
    Bruzelius, K., Mba, D.: An initial investigation on the potential applicability of acoustic emission to rail track fault detection. NDT&E Int. 37, 507–516 (2004)CrossRefGoogle Scholar
  16. 16.
    Jomdecha, C., Prateepasen, A., Kaewtrakulpong, P.: Study on source location using an acoustic emission system for various corrosion types. NDT&E Int. 40, 584–593 (2007)CrossRefGoogle Scholar
  17. 17.
    Toutountzakis, T., Tan, C.K., Mba, D.: Application of acoustic emission to seeded gear fault detection. NDT&E Int. 38, 27–36 (2005)CrossRefGoogle Scholar
  18. 18.
    Abdulkarem, W., Amuthakkannan, R., Al-Raheem, K.F.: Centrifugal pump impeller crack detection using vibration analysis. In: 2nd International Conference on Research in Science, Engineering and Technology, Dubai, UAE, March 21–22, 2014Google Scholar
  19. 19.
    Li, Z., Ma, Z., Liu, Y., Teng, W., Jiang, R.: Crack fault detection for a gearbox using discrete wavelet transform and an adaptive resonance theory neural network. J. Mech. Eng. 61(1), 63–73 (2015)CrossRefGoogle Scholar
  20. 20.
    Moosavian, A., Najafi, G., Ghobadian, B., Mirsalim, M.: The effect of piston scratching fault on the vibration behavior of an IC engine. Appl. Acoust. 126, 91–100 (2017)CrossRefGoogle Scholar
  21. 21.
    Bhalla, S., Soh, C.K.: High frequency piezoelectric signatures for diagnosis of seismic/blast induced structural damages. NDT&E Int. 37, 23–33 (2004)CrossRefGoogle Scholar
  22. 22.
    Loutridis, S., Douka, E., Hadjileontiadis, L.J.: Forced vibration behavior and crack detection of cracked beams using instantaneous frequency. NDT&E Int. 38, 411–419 (2005)CrossRefGoogle Scholar
  23. 23.
    Eftekharnejad, B., Addali, A., Mba, D.: Shaft crack diagnostics in a gearbox. Appl. Acoust. 73, 723–733 (2012)CrossRefGoogle Scholar
  24. 24.
    Barelli, L., Bidini, G., Buratti, C., Mariani, R.: Diagnosis of internal combustion engine through vibration and acoustic pressure non-interasive measurement. Appl. Therm. Eng. 29, 1707–1713 (2009)CrossRefGoogle Scholar
  25. 25.
    Chandroth, G., Sharkey, A., Sharkey, N.: Cylinder pressures and vibration in internal combustion engine condition monitoring. Proc. Comadem 99, 294–297 (1999)Google Scholar
  26. 26.
    ASTM D5766/D5766M-11: Standard Test Method for Open-hole Strength of Polymer Matrix Composite Laminates. ASTM International, West Conshohocken, PA (2011)Google Scholar
  27. 27.
    Metals Handbook: Volume 2, Properties and Selection: Nonferrous Alloys and Special-Purpose Materials. ASM International (1990)Google Scholar
  28. 28.
    Okafor, C., Natarajan, S.: Acoustic emission monitoring of tensile testing of corroded and un-corroded clad aluminum 2024-T3 and characterization of effects of corrosion on AE source events and material tensile properties. AIP Proc. 58, 492–500 (2014)Google Scholar
  29. 29.
    ASTM E976-10: Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response. ASTM International (2010)Google Scholar
  30. 30.
    Beattie, A.G.: Acoustic emission, principles and instrumentation. J. Acoust. Emiss. 2, 95–128 (1983)Google Scholar
  31. 31.
    ISO 12716: Nondestructive Testing-Acoustic Emission Inspection: Vocabulary (2001)Google Scholar
  32. 32.
    Nakamura, H., Ohtsu, M., Enoki, M., Mizutani, Y., Shigeishi, M., Inaba, H., Nakano, M., Shiotani, T., Yuyama, S., Sugimoto, S.: Practical Acoustic Emission Testing. Springer, Berlin (2016)Google Scholar
  33. 33.
    Sujatha, C.: Vibration and Acoustics Measurement and Signal Analysis. McGraw Hill, London (2010)Google Scholar
  34. 34.
    Rao, K.R., Kiml, D.N., Hwang, J.J.: Fast Fourier Transform: Algorithms and Applications. Springer, Berlin (2010)CrossRefGoogle Scholar
  35. 35.
    Roberts, T.M., Talebzadeh, M.: Acoustic emission monitoring of fatigue crack propagation. J. Constr. Steel Res. 59, 695–712 (2003)CrossRefGoogle Scholar
  36. 36.
    Bassim, M.N., Lawrence, S.S., Liu, C.D.: Detection of the onset of fatigue crack growth in rail steels using acoustic emission. Eng. Fract. Mech. 47(2), 207–214 (1994)CrossRefGoogle Scholar
  37. 37.
    Harris, D.O., Dunegan, H.L.: Continuous monitoring of fatigue-crack growth by acoustic-emission techniques. Exp. Mech. 14, 71–81 (1974)CrossRefGoogle Scholar
  38. 38.
    Shigley, J.E., Mischkle, C.R.: Standard Hand book of Machine Design. McGraw Hill, London (1986)Google Scholar
  39. 39.
    Pilkey, W.D.: Peterson’s Stress concentration Factors. Wiley, New York (1997)CrossRefGoogle Scholar
  40. 40.
    Howland, R.C.J.: On the stresses in the neighborhood of a circular hole in a strip under tension. Philos. Trans. R. Soc. Lond. Ser. A 229, 49–86 (1930)zbMATHCrossRefGoogle Scholar
  41. 41.
    Young, W.C., Budynas, R.G.: Roark’s Formulas for Stress and Strain, 7th edn. McGraw Hill, London (2001)Google Scholar
  42. 42.
    Ennaceur, C., Laksimi, A., Herve, C., Cherfaoui, M.: Monitoring crack growth in pressure vessel steels by the acoustic emission technique and the method of potential difference. Int. J. Press. Vessels Pip. 83, 197–204 (2006)CrossRefGoogle Scholar
  43. 43.
    Khazaee, M., Ahmadi, H., Omid, M., Banakar, A., Moosavian, A.: Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals. Insight Non-Destruct. Test. Cond. Monit. 55(6), 323–330 (2013)CrossRefGoogle Scholar
  44. 44.
    Khazaee, M., Ahmadi, H., Omid, M., Moosavian, A., Khazaee, M.: Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on dempster-shafer evidence theory. Proc. Inst. Mech. Eng. Part E 228(1), 21–32 (2014)CrossRefGoogle Scholar
  45. 45.
    Chen, J., Yuan, X., Hu, Z., Li, T., Wu, K., Li, C.: Improvement of resistance-spot-welded joints for DP 600 steel and A5052 aluminum alloy with Zn slice interlayer. J. Manuf. Process. 30, 396–405 (2017)CrossRefGoogle Scholar
  46. 46.
    Tocci, M., Pola, A., Montesano, L., Merlin, M., Garagnani, G.L., La Vecchia, G.M.: Tensile behavior and impact toughness of an AlSi3MgCr alloy. Procedia Struct. Integr. 3, 517–525 (2017)CrossRefGoogle Scholar
  47. 47.
    Bobbili, R., Madhu, V., Gogia, A.K.: Tensile behavior of aluminum 7017 alloy at various temperatures and strain rates. J. Mater. Res. Technol. 5(2), 190–197 (2016)CrossRefGoogle Scholar
  48. 48.
    Ma, H., Huang, L., Tian, Y., Li, J.: Effects of strain rate on dynamic mechanical behavior and microstructure evolution of 5A02-O aluminum alloy. Mater. Sci. Eng. A 606, 233–239 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringSemnan UniversitySemnanIran
  2. 2.Faculty of Aerospace EngineeringSemnan UniversitySemnanIran

Personalised recommendations