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Simulation of recrystallization and martensite revision in 304L austenitic stainless steel after multi-pass rolling processes

Alavi, P ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1007/s41939-020-00072-4
  3. Publisher: Springer Science and Business Media B.V , 2020
  4. Abstract:
  5. In this work, non-isothermal annealing of cold-rolled 304L austenitic stainless steel was studied at temperatures ranging between 400 and 800 °C. In the first place, a dual-phase structure containing work-hardened austenite and strain-induced martensite was produced by means of multi-pass plate rolling, and then, the deformed steel was subjected to annealing heat treatments. To determine the distribution of stored strain energy in the rolling operations, an elastic–plastic finite-element modeling was conducted. Afterwards, an artificial neural network technique was coupled with the thermal-cellular automata model for prediction of martensite reversion, temperature history, and recrystallization progress during subsequent non-isothermal heat treatments. Experimental methods including X-ray diffraction, optical metallography, and Feritscope measurements were carried out to assess the material constants as well as to verify the predictions. Comparison between the predicted results and experimental data showed a good agreement indicating the capability of the model under practical conditions. It was found that that martensite revision started at about 550 °C, while the onset of recrystallization occurred around 670 °C. The activation energies for nucleation and growth during static recrystallization were determined as 180 kJ/mole and 240 kJ/mole, respectively. In addition, the homogenous nucleation mechanism was found to be operative in the rolled steel subjected to total reduction of 35% or higher that resulted in a uniform fine-grained structure. © 2020, Springer Nature Switzerland AG
  6. Keywords:
  7. Austenitic stainless steel ; Cellular automata ; Cold rolling ; Martensite reversion ; Neural network ; Static recrystallization
  8. Source: Multiscale and Multidisciplinary Modeling, Experiments and Design ; Volume 3, Issue 4 , 2020 , Pages 227-244
  9. URL: https://link.springer.com/article/10.1007/s41939-020-00072-4