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Analysis of acoustic emission signals at austempering of steels using neural networks

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Abstract

Bearing steel 100CrMnSi6-4 and tool steel C105U were used to carry out this research with the steels being austempered to obtain a martensitic-bainitic structure. During the process quite a large number of acoustic emissions (AE) were observed. These signals were then analysed using neural networks resulting in the identification of three groups of events of: high, medium and low energy and in addition their spectral characteristics were plotted. The results were presented in the form of diagrams of AE incidence as a function of time. It was demonstrated that complex transformations of austenite into martensite and bainite occurred when austempering bearing steel at 160 °C and tool steel at 130 °C respectively. The selected temperatures of isothermal quenching of the tested steels were within the area near to MS temperature, which affected the complex course of phase transition. The high activity of AE is a typical occurrence for martensitic transformation and this is the transformation mechanism that induces the generation of AE signals of higher energy in the first stage of transition. In the second stage of transformation, the initially nucleated martensite accelerates the occurrence of the next bainitic transformation.

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References

  1. A. Lambert, X. Garat, T. Sturel, A. F. Gourgues, and A. Gingell, Scripta Mater. 43, 161 (2000).

    Article  Google Scholar 

  2. R. Botten, X. Wu, D. Hu, and M. H. Loretto, Acta Mater. 49, 1687 (2001).

    Article  Google Scholar 

  3. E. S. Davenport and E. C. Bain, T. Metall. Soc. AIME 90, 117 (1930).

  4. M. Kang, M.-X. Zhang, and M. Zhu, Acta Mater. 54, 2121 (2006).

    Article  Google Scholar 

  5. S. M. C. Van Bohemen, M. J. Santofimiaa, and J. Sietsma, Scripta Mater. 58, 488 (2008).

    Article  Google Scholar 

  6. H. K. D. H. Bhadeshia and C. H. Young, Mater. Sci. Tech. 10, 209 (1994).

    Article  Google Scholar 

  7. M. Hillert, Scripta Mater. 47, 175 (2002).

    Article  Google Scholar 

  8. N. F. Kennon, Metall. Trans. A 9, 57 (1978).

    Article  Google Scholar 

  9. T. Z. Wozniak and Z. Ranachowski, Arch. Acoust. 31, 3 (2006).

    Google Scholar 

  10. T. Z. Wozniak, Mat. Sci. Eng A 408, 309 (2005).

    Article  Google Scholar 

  11. T. Z. Wozniak, Mater. Charact. 59, 708 (2008).

    Article  Google Scholar 

  12. T. Z. Wozniak, J. Jelenkowski, K. Rozniatowski, and Z. Ranachowski, Mater. Sci. Forum 726, 55 (2012).

    Article  Google Scholar 

  13. T. Z. Wozniak, K. Rozniatowski, and Z. Ranachowski, Kovove Mater. 49, 319 (2011).

    Google Scholar 

  14. T. Z. Wozniak, K. Rozniatowski, and Z. Ranachowski, Met. Mater. Int. 17, 365 (2011).

    Article  Google Scholar 

  15. T. Z. Wozniak, Z. Ranachowski, P. Ranachowski, W. Ozgowicz, and A. Trafarski, Arch. Metall. Mater. 59, 1705 (2014).

    Google Scholar 

  16. B. Widrow, R. G. Winter, and R. A. Baxter, Proc. IEEE First Annual International Conference on Neural Networks: Learning Phenomena in Layered Neural Networks, Vol. 2, p. 411, IEEE Expert, California, USA (1987).

    Google Scholar 

  17. A. Pawelek, J. Kusnierz, Z. Jasienski, Z. Ranachowski, and J. Bogucka, Arch. Metall. Mater. 54, 83 (2009).

    Google Scholar 

  18. A. Pawelek, J. Kusnierz, J. Bogucka, J. Jasinski, Z. Ranachowski, T. Debowski, et al. Arch. Acoust. 32, 955 (2007).

    Google Scholar 

  19. M. Oka and H. Okamoto, Metall. Trans. A 19, 447 (1988).

    Article  Google Scholar 

  20. G. R. Speich and A. J. Schwoeble, STP571Monitoring Structural Integrity by Acoustic Emission (eds. J. C. Spanner and J. W. McElroy), pp. 40–58, ASTM International, USA (1975).

  21. A. Navarro-López, J. Sietsma, and M. J. Santofimia, Metall. Trans. A 47, 1028 (2016).

    Article  Google Scholar 

  22. H. Hu, G. Xu, Y. Zhang, Z. Xue, and M. Zhou, Met. Mater. Int. 30, 818 (2015).

    Google Scholar 

  23. S. Chupatanakul, P. Nash, and D. Chen, Met. Mater. Int. 6, 453 (2006).

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-103, Warszawa, Poland

    Malgorzata Łazarska & Zbigniew Ranachowski

  2. Institute of Technology, Kazimierz Wielki University, 85-064, Bydgoszcz, Poland

    Malgorzata Łazarska, Tadeusz Z. Wozniak, Andrzej Trafarski & Grzegorz Domek

Authors
  1. Malgorzata Łazarska
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  2. Tadeusz Z. Wozniak
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  3. Zbigniew Ranachowski
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  4. Andrzej Trafarski
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  5. Grzegorz Domek
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Correspondence to Malgorzata Łazarska.

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Łazarska, M., Wozniak, T.Z., Ranachowski, Z. et al. Analysis of acoustic emission signals at austempering of steels using neural networks. Met. Mater. Int. 23, 426–433 (2017). https://doi.org/10.1007/s12540-017-6347-z

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  • DOI: https://doi.org/10.1007/s12540-017-6347-z

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