Szczegóły publikacji
Opis bibliograficzny
Selection of the best phase transformation model for optimization of manufacturing processes of pearlitic steel rails / Roman Kuziak, Valeriy Pidvysots'kyy, Monika PERNACH, Łukasz RAUCH, Tomasz Zygmunt, Maciej PIETRZYK // Archives of Civil and Mechanical Engineering / Polish Academy of Sciences. Wrocław Branch, Wrocław University of Technology ; ISSN 1644-9665. — 2019 — vol. 19 iss. 2, s. 535–546. — Bibliogr. s. 545–546, Abstr. — Publikacja dostępna online od: 2019-01-24
Autorzy (6)
- Kuziak Roman
- Pidvysots'kyy Valeriy
- AGHPernach Monika
- AGHRauch Łukasz
- Zygmunt Tomasz
- AGHPietrzyk Maciej
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 120250 |
|---|---|
| Data dodania do BaDAP | 2019-03-06 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.acme.2018.12.004 |
| Rok publikacji | 2019 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Archives of Civil and Mechanical Engineering |
Abstract
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. © 2018
