Affiliation:
1. Process Metallurgy Research Unit University of Oulu PO Box 4300 90014 Oulu Finland
2. Raahe Works SSAB Europe Oy Rautaruukintie 155 Raahe 92101 Finland
Abstract
The formation of defects such as cracks in continuous casting deteriorates the quality of cast products and efficiency of steelmaking. To evaluate the risks and identify the root causes of defect formation, phenomenological quality criteria computed with a solidification and microstructure model known as InterDendritic Solidification (IDS) have previously been applied. This approach is computationally efficient and provides a fundamental perspective to defect formation in continuous casting. The aim of this work is to study the capabilities of these criteria as features in predicting transverse cracking with interpretable machine learning models. IDS is coupled with a heat transfer model known as Tempsimu to simulate the continuous casting process. Measured compositions are utilized in the simulations and defects reported at a steelmaking plant are used as labels in classification. Logistic regression, decision tree, and Gaussian Naïve Bayes classifiers are developed to predict transverse cracking in peritectic C–Mn, low‐carbon B–Ti microalloyed, and peritectic Nb‐microalloyed steels. The corresponding balanced accuracies of the best classifiers from cross‐validation are 92%, 94.6%, and 82.8%. Due to the good performance and the interpretability of the developed classifiers, the fundamental causes of transverse cracking and possibilities of avoiding it by changes in the compositions are identified.
Subject
Materials Chemistry,Metals and Alloys,Physical and Theoretical Chemistry,Condensed Matter Physics