Gradient boosted regression trees for modelling onset of austenite decomposition during cooling of steels
Luukkonen, Juho; Pohjonen, Aarne; Louhenkilpi, Seppo; Miettinen, Jyrki; Sillanpää, Mikko J.; Laitinen, Erkki (2023-05-08)
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). https://doi.org/10.1007/s11663-023-02782-9
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https://urn.fi/URN:NBN:fi-fe20230907121608
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Abstract
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. R² 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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