Selection of the best phase transformation model for optimization of manufacturing processes of pearlitic steel rails

Selection of the best model for simulation of manufacturing processes of pearlitic steel rails was the objective of the paper. Achieving a proper balance between its predictive capabilities and computing costs was used as a criterion. Review of the pearlitic transformation models was performed and m...

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Bibliographic Details
Published in:Archives of Civil and Mechanical Engineering 2019-03, Vol.19 (2), p.535-546
Main Authors: Kuziak, Roman, Pidvysots’kyy, Valeriy, Pernach, Monika, Rauch, Łukasz, Zygmunt, Tomasz, Pietrzyk, Maciej
Format: Article
Language:English
Subjects:
Civil Engineering
Cooling
Dilatometry
Empirical equations
Engineering
Finite element method
Grain size
Hot rolling
Manufacturing
Mechanical Engineering
Mechanical properties
Original Research Article
Pearlitic steel
Pearlitic transformations
Phase transformation
Phase transitions
Rail steels
Rails
Simulation
Structural Materials
Thermal cycling
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Summary:Selection of the best model for simulation of manufacturing processes of pearlitic steel rails was the objective of the paper. Achieving a proper balance between its predictive capabilities and computing costs was used as a criterion. Review of the pearlitic transformation models was performed and modification of the JMAK equation was selected for further analysis. Empirical models were developed to describe microstructure and mechanical properties of rails. Dilatometric tests were performed to supply data for identification of the phase transformation model. Physical simulations of various thermal cycles were performed to validate and verify the models. Finite element (FE) simulations of the hot rolling provided distributions of the temperature and the austenite grain size at the cross section of the rail, which were used as an input for modelling of phase transformations during cooling. Accelerated cooling by a cyclic immersion of the rail head in the polymer solution was considered as a case study. Performed simulations confirmed good predictive capabilities of the model.
ISSN:1644-9665
2083-3318
DOI:10.1016/j.acme.2018.12.004