Neural networks model for prediction of the hardness of steels cooled from the austenitizing temperature
Journal Title: Archives of Materials Science and Engineering - Year 2016, Vol 82, Issue 2
Abstract
Purpose: The paper presents the new neural networks model making it possible to estimate the hardness of continuously-cooled steel from the austenitizing temperature.Design/methodology/approach: The method proposed in the paper employs two applications of the neural networks of: classification and regression. Classification and consists in determining the value of dichotomous variables describing the occurrence of: ferrite, pearlite, bainite and martensite in the microstructure of a steel. The values of dichotomous variables have been used for calculating steel hardness. The other task is regression, which aims at calculating the steel hardness.Findings: The presented neural networks model can be used only in the range of concentrations of alloying elements shown in this paper.Practical implications: The model worked out makes it possible to calculate hardness for the steel with a known chemical composition. This model deliver important information for the rational selection of steel for those parts of the machines that are subjected to the heat treatment. The presented model make it possible the analysis of the interaction of the chemical composition on the hardness curves of the steel cooled from the austenitizing temperature.Originality/value: The paper presents the method for calculating hardness of the structural and engineering steels, depending on their chemical composition, austenitizing temperature and cooling rate.
Authors and Affiliations
J. Trzaska
Keywords
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- EP ID EP216249
- DOI 10.5604/01.3001.0009.7105
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