Gradient Boosted Regression Trees for Modelling Onset of Austenite Decomposition During Cooling of Steels
Juho Luukkonen, Aarne Pohjonen, Seppo Louhenkilpi, Jyrki Miettinen, Mikko J. Sillanpää, Erkki K. Laitinen
Abstract
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. R 2 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.