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Archives of Metallurgy and Materials

The Journal of Institute of Metallurgy and Materials Science and Commitee on Metallurgy of Polish Academy of Sciences

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2300-1909
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The Application of Acoustic Emission and Artificial Neural Networks in an Analysis of Kinetics in the Phase Transformation of Tool Steel During Austempering

M. Łazarska
  • Corresponding author
  • Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego Str., 02-106 Warszawa, Poland
  • Institute of Technology, Kazimierz Wielki University, 30 Chodkiewicza Str., 85-064 Bydgoszcz, Poland
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ T.Z. Woźniak
  • Institute of Technology, Kazimierz Wielki University, 30 Chodkiewicza Str., 85-064 Bydgoszcz, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Z. Ranachowski
  • Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego Str., 02-106 Warszawa, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ P. Ranachowski
  • Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego Str., 02-106 Warszawa, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ A. Trafarski
  • Institute of Technology, Kazimierz Wielki University, 30 Chodkiewicza Str., 85-064 Bydgoszcz, Poland
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  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-07-22 | DOI: https://doi.org/10.1515/amm-2017-0089

Abstract

During the course of the study it involved tool steel C105U was used. The steel was austempered at temperatures of 130°C, 160°C and 180°C respectively. Methods of acoustic emission (AE) were used to investigate the resulting effects associated with transformations and a large number of AE events were registered. Neural networks were applied to analyse these phenomena. In the tested signal, three groups of events were identified of: high, medium and low energy. The average spectral characteristics enabled the power of the signal spectrum to be determined. After completing the process, the results were compiled in the form of diagrams of the relationship of the AE incidence frequency as a function of time. Based on the results, it was found that in the austempering of tool steel, in the first stage of transformation midrib morphology is formed. Midrib is a twinned thin plate martensite. In the 2nd stage of transformation, the intensity of the generation of medium energy events indicates the occurrence of bainite initialised by martensite. The obtained graphic of AE characteristics of tool steel austempering allow conclusions to be drawn about the kinetics and the mechanism of this transformation.

Keywords: carbon steel; austempering; lower bainite; acoustic emission (AE); neural networks

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About the article

Published Online: 2017-07-22

Published in Print: 2017-06-01


Citation Information: Archives of Metallurgy and Materials, ISSN (Online) 2300-1909, DOI: https://doi.org/10.1515/amm-2017-0089.

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© 2017 Polish Academy of Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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