Accelerating nano-bainite transformation based on a new constructed microstructural predicting model

Shortening the bainite transformation time is one crucial issue on the application of high carbon nano-bainite steel. In this paper, an accurate black-box model that could predict the plate thickness of bainitic ferrite was constructed based on gradient boosting decision tree model, and a polynomial...

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Bibliographic Details
Published in:Materials science & engineering. A, Structural materials : properties, microstructure and processing Structural materials : properties, microstructure and processing, 2019-03, Vol.748, p.16-20
Main Authors: Yang, Zhinan, Chu, Chunhe, Jiang, Feng, Qin, Yuman, Long, Xiaoyan, Wang, Shuli, Chen, Da, Zhang, Fucheng
Format: Article
Language:English
Subjects:
Bainite
Decision trees
Ferrites
Heat treatment
Mathematical models
Mechanical property
Microstructure
Nanobainite
Polynomials
Size distribution
Transformation kinetic
Transformations
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Summary:Shortening the bainite transformation time is one crucial issue on the application of high carbon nano-bainite steel. In this paper, an accurate black-box model that could predict the plate thickness of bainitic ferrite was constructed based on gradient boosting decision tree model, and a polynomial model was constructed to make predicting process easier. According to the constructed model, a two-steps process was proposed, which shortened the transformation time from over 60 h to nearly 25 h. After the new process treatment, the size distribution of bainitic ferrite in microstructure became more uniform, and the strength and toughness of steel were also improved simultaneously.
ISSN:0921-5093
1873-4936
DOI:10.1016/j.msea.2019.01.061