Analysis of Acoustic Emission Signals at Austempering of Steels Using Neural Networks

Malgorzata Łazarska; Tadeusz Z. Wozniak; Zbigniew Ranachowski; Andrzej Trafarski; Grzegorz Domek

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자료유형: 학술저널

저자정보: Malgorzata Łazarska (Polish Academy of Sciences) Tadeusz Z. Wozniak (Polish Academy of Sciences) Zbigniew Ranachowski (Polish Academy of Sciences) Andrzej Trafarski (Kazimierz Wielki University) Grzegorz Domek (Kazimierz Wielki University)

저널정보: 대한금속·재료학회 Metals and Materials International Metals and Materials International Vol.23 No.3

발행연도: 2017.1

수록면: 426 - 433 (8page)

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Bearing steel 100CrMnSi6-4 and tool steel C105U were used to carry out this research with the steels beingaustempered 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 ofthree groups of events of: high, medium and low energy and in addition their spectral characteristics wereplotted. The results were presented in the form of diagrams of AE incidence as a function of time. It wasdemonstrated that complex transformations of austenite into martensite and bainite occurred when austemperingbearing steel at 160 °C and tool steel at 130 °C respectively. The selected temperatures of isothermalquenching of the tested steels were within the area near to MS temperature, which affected the complex course ofphase transition. The high activity of AE is a typical occurrence for martensitic transformation and this isthe transformation mechanism that induces the generation of AE signals of higher energy in the first stage oftransition. In the second stage of transformation, the initially nucleated martensite accelerates the occurrenceof the next bainitic transformation.

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참고문헌 신청

A. Lambert / 2000 / Scripta Mater 43:161

R. Botten / 2001 / Acta Mater 49:1687

E. S. Davenport / 1930 / T. Metall. Soc. AIME 90:117

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