Prediction of Continuous Cooling Diagrams for the Precision Forged Tempering Steel 50CrMo4 by Means of Artificial  Neural Networks

Nürnberger, Florian; Schaper, Marco; Bach, Friedrich‐Wilhelm; Mozgova, Iryna; Kuznetsov, Konstjantyn; Halikova, Anna; Perederieieva, Olga

doi:10.1155/2009/582739

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The increasing popularity of computational intelligence methods in many fields of science and engineering is also reflected by the area of materials engineering [18][19][20][21][22][23][24][25].Use of hybrid methods is an important trend related to modelling in materials engineering [26][27][28][29]. The numerically verified models are used to calculate the chemical composition of steel with required transformation temperatures and hardness of steel cooled continuously from the austenitizing temperature.…”

Section: Discussionmentioning

confidence: 99%

Archives of Metallurgy and Materials

Trzaska

2018

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The article shows a new model of Continuous Cooling Transformation (CCT) diagrams of structural steels and engineering steels. The modelling used artificial neural networks and a set of experimental data prepared based on 550 CCT diagrams published in the literature. The model of CCT diagrams forms 17 artificial neural networks which solve classification and regression tasks. Neural model is implemented in a computer software that enables calculation of a CCT diagram based on chemical composition of steel and its austenitizing temperature.

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Section: Discussionmentioning

confidence: 99%

Archives of Metallurgy and Materials

Trzaska

2018

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“…Calculation of the CCT diagram can also be an introduction to laboratory investigations [4]. Various methods are used to model CCT diagrams [5][6][7][8][9][10][11][12]. The models presented in the literature can be used in various range of mass concentrations of elements.…”

Section: Introductionmentioning

confidence: 99%

Archives of Metallurgy and Materials

Trzaska¹

2020

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The article shows examples of simulation of the chemical composition effect on austenite transformation during continuous cooling. The calculations used own neural model of CCT (Continuous Cooling Transformation) diagrams describing austenite transformations that occur during continuous cooling. The model allows to calculate a CCT diagrams of structural steels and engineering steels based on chemical composition of steel and austenitizing temperature. Examples of simulation shown herein are related to the effect of selected elements on the temperatures of phase transformations, hardness and volume fraction of ferrite, pearlite, bainite and martensite in steel.

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“…In this paper the use of neural networks as blast furnace forecasting aids using above burden temperature evolutions is proposed. Although their use in the iron industry is not new, most authors use neural networks to predict with a multilayer perceptron (MLP) [1][2][3][4][5][6] while others prefer a radial basis function (RBF). 7,8) Only a few references using neural networks to make a qualitative classification of data 9) can be found along with proposals to use them to recognize patterns in the iron making process.…”

Section: Introductionmentioning

confidence: 99%

A New “User-friendly” Blast Furnace Advisory Control System Using a Neural Network Temperature Profile Classifier

et al. 2010

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The adaptation of blast furnaces to the new technologies has increased the operation information so that the sensor information can be known at every moment. However this often results in the supply of excessive data volume to the plant operators. This paper describes an industrial application for self-organized maps (SOM) in order to help them make decisions regarding blast furnace control by means of pattern recognition and the matching of temperature profiles supplied by the thermocouples placed on the above burden. The classification of patterns via easy color coding indicates to the operator what the blast furnace operational situation is, thus making the necessary corrections easier.KEY WORDS: ironmaking; blast furnace; neural networks; self-organized maps (SOM); forecasting. perature data. This temperature is measured each hour by means of two probes, each containing twelve thermocouples. The probes are identical and their anchorages are located in the wall, describing a diameter of the blast furnace section, so the expected behaviour would be a symmetrical temperature profile. However irregularities in the feeding processes, in the load composition and some other factors like cooling yield asymmetric profiles. Besides that, a great dispersion of the data, with many outlier samples, is confirmed. This dispersion (above all in the central thermocouples) is due to the feeding rotational movement so the load is not at the same level at every point. The cooling system acts upon the central zone of the blast furnace if the temperature exceeds 450°C, which may cause an M-profile like that of Fig. 2 and contributes to the dispersion of the data. Figure 3 shows the pig iron temperatures measured in the blast furnace with their mean and standard deviation. The pig iron temperature should remain over 1 450°C from the time it leaves the blast furnace until it is processed in the plant to make iron, this being due to the risk of undesirable reactions between compounds that would yield poor quality iron. This is a big problem in the industry so a lot of effort has been put into improving it. 10)The main objective of using a neural network is to supply useful information to the plant operators. This information must be displayed quickly and in an easy to understand format because an excessive amount of information is overwhelming to plant operators, and therefore useless. In neural networks, each neuron learns a different pattern during training, even if most of them are quite similar to others. In order to avoid excessive information due to the use of a high number of neurons it is necessary consult seasoned plant operators, whose knowledge based on their experience serves to classify all these patterns into two or three classes. Three classifications can be compared: one based on the plant operators' knowledge, the neural network classification and a third, based on the criteria detailed in Table 1, which is purely mathematical but real and commonly used. In later sections we will demonstrate that the classification...

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Prediction of Continuous Cooling Diagrams for the Precision Forged Tempering Steel 50CrMo4 by Means of Artificial Neural Networks

Cited by 5 publications

References 19 publications

Archives of Metallurgy and Materials

Archives of Metallurgy and Materials

Archives of Metallurgy and Materials

A New “User-friendly” Blast Furnace Advisory Control System Using a Neural Network Temperature Profile Classifier

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