University of Oulu

Luukkonen, J., Pohjonen, A., Louhenkilpi, S. et al. Gradient Boosted Regression Trees for Modelling Onset of Austenite Decomposition During Cooling of Steels. Metall Mater Trans B 54, 1705–1724 (2023).

Gradient boosted regression trees for modelling onset of austenite decomposition during cooling of steels

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Author: Luukkonen, Juho1; Pohjonen, Aarne2; Louhenkilpi, Seppo3;
Organizations: 1Research Unit of Mathematical Sciences, University of Oulu, P. O. Box 3000, Oulu, 90014, Finland
2Materials and Mechanical Engineering, Faculty of Technology, University of Oulu, P. O. Box 4200, 90014, Oulu, Finland
3Process Metallurgy Research Group, University of Oulu, P. O. Box 8000, 90014, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.4 MB)
Persistent link:
Language: English
Published: Springer Nature, 2023
Publish Date: 2023-09-07


Continuous cooling transformation (CCT) diagrams can be constructed by empirical methods, which is expensive and time consuming, or by fitting a model to available experimental data. Examples of data-driven models implemented so far include regression models, artificial neural networks, k-Nearest Neighbours and Random Forest. Gradient boosting machine (GBM) has been succesfully used in many machine learning applications, but has not been used before in modelling CCT-diagrams. This article presents a novel way of predicting ferrite start temperatures for low alloyed steels using gradient boosting. First, transformation onset temperatures are predicted over a grid of values with a trained GBM-model after which a physically-based model is fitted to the piecewise constant curve obtained as output from the model. Predictability of the GBM-model is tested with two sets of CCT-diagrams and compared to Random Forest and JMatPro software. GBM outperforms its competitors under all tested model performance metrics: e.g. for test data is 0.92, 0.87 and 0.70 for GBM, Random Forest and JMatPro respectively. Output from the GBM-model is used for fitting a physically based model, which enables the estimation of transformation start for any linear or nonlinear cooling path. This can be further converted to Time-Temperature-Transformation (TTT) diagram.

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Series: Metallurgical and materials transactions. B, Process metallurgy and materials processing science
ISSN: 1073-5615
ISSN-E: 1543-1916
ISSN-L: 1073-5615
Volume: 54
Issue: 4
Pages: 1705 - 1724
DOI: 10.1007/s11663-023-02782-9
Type of Publication: A1 Journal article – refereed
Field of Science: 216 Materials engineering
Funding: Open Access funding provided by University of Oulu including Oulu University Hospital.
Copyright information: © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit