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Abstract:
The goal of the research was to analyze the acoustic emission signal recorded during the heat treatment. On a special stand, samples prepared from 27MnCrB52 steel were tested. The steel samples were heated to 950°C and then cooled continuously in the air. Signals from phase changes occurring during cooling were recorded using the system for registering Acoustic Emission. As a result of the changes, Widmanstätten ferrite and bainite structures were observed under the scanning microscope. The recorded Acoustic Emission signal was analyzed and the signal was assigned to the appropriate phase transformation with the use of artificial neural networks.

Keywords:
microstructure, phase transformation, ultrasonics, acustic emision, continuous cooling

Abstract:
Acoustic emission and dilatometry were applied to investigate the characteristics of phase transformations in bearing steel 100CrMnSi6-4 during austempering below the martensite start temperature (MS 175 °C) at 150 °C. The aim of this study is to characterize the product of transformation occurring below the MS temperature using various research methods. Analysis of the dilatometric curves shows that, after the formation of athermal martensite below the MS temperature, the austenite continues to undergo isothermal transformation, indicating the formation of bainite. Additionally, tests were carried out with the use of acoustic emission during isothermal hardening of the adopted steel. The obtained acoustic emission signals were analyzed using an artificial neural network. The results, in the form of a graph of the frequency of acoustic emission (AE) event occurrence as a function of time, make it possible to infer about the bainite isothermal transformation. The results of this research may be used in the future to design optimal heat treatment methods and, consequently, may enable desired microstructure shaping.

Keywords:
bainite, austempering, acoustic emission, neural networks, dilatometry

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

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.

Keywords:
microstructure, phase transformation, dislocation, ultrasonics, alloys