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Determination of the damage mechanisms in armor structural materials via self-organizing map analysis

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Abstract

Dynamic compression fracture behaviors together with damage mechanisms of materials subjected to a compressive load at a high strain rate were studied by using a Self-organizing map (SOM). The materials used for the analysis were Al5083, Rolled homogeneous armor (RHA) and tungsten heavy alloy (WHA). The deformation behavior and Acoustic emission (AE) signal were acquired through a Split Hopkinson pressure bar (SHPB)-AE coupled test. The self-organization map which is one of the artificial neural network technique was employed to classify the AE energy, amplitude, and peak frequency according to the characteristics of the signal. In addition, distributions of AE signals were represented in stress-strain curves. The correlation between AE characteristics and material damage mechanisms was investigated. Based on the results, it was found that cluster 1 with high AE energy, high amplitude and low frequency was the cluster of the AE signal generated near the yield point of the material. Cluster 3, which has the opposite tendency, was confirmed as a cluster of AE signals that occurred just before a fracture of the material. The change in the measured value can be seen depending on the strain rate and the material, but the overall tendency was similar.

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References

  1. P. K. Jena, N. Jagtap, K. Siva Kumar and T. Balakrishna Bhat, Some experimental studies on angle effect in penetration, International Journal of Impact Engineering, 37 (2008) 489–501.

    Article  Google Scholar 

  2. S. C. Woo, J. T. Kim, C. H. Cho, J. Y. Kim and T. W. Kim, The dynamic compressive behavior of armor structural materials in split Hopkinson pressure bar test, The Journal of Strain Analysis for Engineering Design, 48 (2013) 420–436.

    Article  Google Scholar 

  3. S. C. Woo and T. W. Kim, High-strain-rate impact in Kevlar–woven composites and fracture analysis using acoustic emission, Composites Part B, 60 (2014) 125–136.

    Article  Google Scholar 

  4. S. C. Woo and T. W. Kim, High strain-rate failure in carbon/Kevlar hybrid woven composites via a novel SHPB-AE coupled test, Composites Part B, 97 (2016) 317–328.

    Article  Google Scholar 

  5. J. A. Nixon, M. G. Phillips, D. R. Moore and R. S. Prediger, A study of the development of impact damage in cross-ply carbon fiber/PEEK laminates using acoustic emission, Composites Science and Technology, 31 (1) (1988) 1–14.

    Article  Google Scholar 

  6. N. Godin, S. Huguet and R. Gaertner, Integration of the Kohonen's self-organising map and k-means algorithm for the segmentation of the AE data collected during tensile tests on cross-ply composites, NDT&E International, 38 (2005) 299–309.

    Article  Google Scholar 

  7. R. de Oliveira and A. T. Marques, Health monitoring of FRP using acoustic emission and artificial neural networks, Computers and Structures, 86 (2008) 367–373.

    Article  Google Scholar 

  8. L. Calabrese, G. Campanella and E. Proverbio, Use of cluster analysis of acoustic emission signals in evaluating damage severity in concrete structures, Journal of Acoustic Emission, 28 (2010) 129–141.

    Google Scholar 

  9. D. Crivelli, M. Guagliano and A. Monici, Development of an artificial neural network processing technique for the analysis of damage evolution in pultruded composites with acoustic emission, Composites: Part B, 56 (2010) 948–959.

    Article  Google Scholar 

  10. M. Shaira, N. Godin, P. Guy, L. Vanel and J. Courbon, Evaluation of the strain-induced martensitic transformation by acoustic emission monitoring in 304L austenitic stainless steel: Identification of the AE signature of the martensitic transformation and power-law statistics, Materials Science and Engineering A, 492 (2008) 392–399.

    Article  Google Scholar 

  11. V. Venkatesh and J. R. Houghton, Neural network approach to acoustic emission source location, Journal of Acoustic Emission, 14 (2) (1996) 61–68.

    Google Scholar 

  12. J. Jiao, C. He, B. Wu, R. Fei and X. Wang, Application of wavelet transform on modal acoustic emission source location in thin plates with one sensor, International Journal of Pressure Vessels and Piping, 81 (5) (2004) 427–431.

    Article  Google Scholar 

  13. H. Kuhn and D. Medlin, ASM handbook: Mechanical testing and evaluation, ASM International, 8 (1984) 462–476.

    Google Scholar 

  14. R. M. Davies, A critical study of the Hopkinson pressure bar, Philos. Trans. A, 204 (1948) 375–457.

    Article  MATH  Google Scholar 

  15. J. M. Shim and D. Mohr, Using split Hopkinson pressure bars to perform large strain compression tests on polyurea at low, intermediate and high strain rates, International Journal of Impact Engineering, 36 (2009) 1116–1127.

    Article  Google Scholar 

  16. O. S. Lee, H. B. Choi and H. M. Kim, High-temperature dynamic deformation of aluminum alloys using SHPB, Journal of Mechanical Science and Technology, 25 (2011) 143–148.

    Article  Google Scholar 

  17. J. U. Gu and N. S. Choi, Analysis of fracture signals from tooth/composite restoration according to AE sensor attachment, Journal of the Korean Society for Nondestructive Testing, 30 (5) (2011) 500–507.

    Google Scholar 

  18. T. Kohonen, Self-organizing maps, Third Ed., Springer-Verlag, Berlin Heidelberg, Germany (2001).

    Book  MATH  Google Scholar 

  19. J. A. Hertz, A. S. Krogh and R. G. Palmer, Introduction to the theory of neural computation, Addison-Wesley USA (1991) 217–250.

    Google Scholar 

  20. F. Marini, J. Zupan and A. L. Magri, Class-modeling using Kohonen artificial neural networks, Analytica Chimica Acta., 544 (2005) 306–314.

    Article  Google Scholar 

  21. S. Huguet, N. Godin, R. Gaertner, L. Salmon and D. Villard, Use of acoustic emission to identify damage modes in glass fibre reinforced polyester, Composites Science and Technology, 62 (2002) 1433–1444.

    Article  Google Scholar 

  22. M. Huang, L. Jiang, P. K. Liaw, C. R. Brooks, R. Seeley and D. L. Klarstrom, Using acoustic Emission in fatigue and fracture materials research, Journal of Metals, 50 (11) (1998).

    Google Scholar 

  23. R. W. Rohde, B. M. Butche, J. R. Holland and C. H. Karnes, Metallurgical effects at high strain rates, New York: Plenum Press (1973) 519–530.

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Automotive Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea

    Jong-Tak Kim & Jae Sakong

  2. Survivability Technology Defense Research Center, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea

    Sung-Choong Woo

  3. Agency for Defense Development, P.O. Box 35, Yuseong, Daejeon, 305-600, Korea

    Jin-Young Kim

  4. Department of Mechanical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea

    Tae-Won Kim

Authors
  1. Jong-Tak Kim
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  2. Jae Sakong
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  3. Sung-Choong Woo
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  4. Jin-Young Kim
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  5. Tae-Won Kim
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Corresponding author

Correspondence to Tae-Won Kim.

Additional information

Recommended by Associate Editor Jin Weon Kim

Jong-Tak Kim received his M.S. degree (2012) in Automotive Engineering from Hanyang University, Republic of Korea. He is currently a Ph.D. student in Automotive Engineering in the Graduate School of Hanyang University. In this study, he carried out SHPB-AE coupled test and ANN analysis.

Tae-Won Kim received his D.Phil. (1998) in Engineering Science from Oxford University, UK. He is currently a Professor at the Department of Mechanical Engineering, Hanyang University and Director of the Survivability Technology Defense Research Center. His current interests are modeling materials behavior, failure analysis, and finite element implementation. In this research, he was in charge of the overall ANN analysis and experimental work, particularly with microstructural characteristics.

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Kim, JT., Sakong, J., Woo, SC. et al. Determination of the damage mechanisms in armor structural materials via self-organizing map analysis. J Mech Sci Technol 32, 129–138 (2018). https://doi.org/10.1007/s12206-017-1214-x

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  • DOI: https://doi.org/10.1007/s12206-017-1214-x

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