scholarly journals The Use of Acoustic Emission and Neural Network in the Study of Phase Transformation below MS

Materials ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 551
Author(s):  
Małgorzata Łazarska ◽  
Tadeusz Z. Wozniak ◽  
Zbigniew Ranachowski ◽  
Andrzej Trafarski ◽  
Szymon Marciniak
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Isothermal Transformation ◽  
Bearing Steel ◽  
Optimal Heat Treatment ◽  
Isothermal Hardening ◽  
Start Temperature ◽  
Event Occurrence

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.

Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

2020 ◽  
Vol 14 (2) ◽  
pp. 6789-6800
Author(s):  
Vishal Jagota ◽  
Rajesh Kumar Sharma
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Sliding Wear ◽  
Austenitizing Temperature ◽  
Tempering Temperature ◽  
Die Steel ◽  
Artificial Neural ◽  
Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

scholarly journals Effect of Freeze–Thaw Cycling on the Failure of Fibre-Cement Boards, Assessed Using Acoustic Emission Method and Artificial Neural Network

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2181 ◽  
Author(s):  
Tomasz Gorzelańczyk ◽  
Krzysztof Schabowicz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Freeze Thaw ◽  
Fibre Cement ◽  
Artificial Neural ◽  
Building Practice

This paper presents the results of investigations into the effect of freeze–thaw cycling on the failure of fibre-cement boards and on the changes taking place in their structure. Fibre-cement board specimens were subjected to one and ten freeze–thaw cycles and then investigated under three-point bending by means of the acoustic emission method. An artificial neural network was employed to analyse the results yielded by the acoustic emission method. The investigations conclusively proved that freeze–thaw cycling had an effect on the failure of fibre-cement boards, as indicated mainly by the fall in the number of acoustic emission (AE) events recognized as accompanying the breaking of fibres during the three-point bending of the specimens. SEM examinations were carried out to gain better insight into the changes taking place in the structure of the tested boards. Interesting results with significance for building practice were obtained.

Application of Artificial Neural Network for Prediction of Heat Treated Sintered Steels Properties

2008 ◽  
Vol 273-276 ◽  
pp. 323-328 ◽  
Author(s):  
H. Khorsand ◽  
M. Arjomandi ◽  
H. Abdoos ◽  
S.H. Sadati
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Initial Density ◽  
Back Propagation ◽  
Heat Treated ◽  
Algorithm Comparison ◽  
Artificial Neural

Heat treatment is an important method for improving the mechanical properties of industrial parts that are made through the powder metallurgy. Most PM steels are subjected to hardening and tempering, and it is due to this treatment that tempered martensite is formed. After heat treatment, these steel’s mechanical properties are affected by the heat treatment parameters and the initial density. In this paper, in order to make an evaluation of the effect of the above parameters, FN-0205 PM steel with various densities is heat treated in different austenite conditions and tempering time. Their mechanical properties are then evaluated and recorded. Afterwards, this data obtained by experimental procedure are predicted for various conditions. The method employed here is the well-known feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. Comparison between predicted values and experimental data, in the present study, indicate that the predicted results from this model are in good agreement with the experimental values.

scholarly journals Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Xiangxin Liu ◽  
Zhengzhao Liang ◽  
Yanbo Zhang ◽  
Xianzhen Wu ◽  
Zhiyi Liao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Good Method ◽  
Rock Types ◽  
Fracturing Process ◽  
Different Types ◽  
Artificial Neural ◽  
Ae Signals

Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises.

The Divorce Eutectoid Transformation in the Higher Mn Bearing Steel

2013 ◽  
Vol 634-638 ◽  
pp. 1798-1802 ◽  
Author(s):  
Zhi Xin Yin ◽  
Harry Bhadeshia
Keyword(s):  
Heat Treatment ◽  
Isothermal Transformation ◽  
Bearing Steel ◽  
Eutectoid Transformation ◽  
Detailed Mechanism ◽  
Bearing Steels ◽  
Original Boundary ◽  
Electronic Microscope

The detailed mechanism of divorced pearlite in bearing steels began to be actively discussed more recently. The survey of divorced pearlite in higher Mn bearing steel was carried out through scanning electronic microscope following isothermal transformation heat treatment. The results show that while divorced transformation taking place in the higher Mn bearing steel just slightly below A1, the cementite could be produced in such way as growing on the base of pre-existing cementite particles in austenite, or emerge either in globular particles or in short rod directly from the austenite. Under isothermal transformation, the carbon atoms in austenite prefer to precipitate on the original boundary of the austenite. The enough small size of the cementite particles remained in the austenite is helpful for getting spheroidal cementite particles structure through divorced eutectoid transformation.

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