Abstract
Machine Learning classification models have been trained and validated from a dataset (73 features and 13,616 instances) including experimental information of a clean cold forming steel fabricated by electric arc furnace and hot rolling. A classification model was developed to identify inclusion contents above the median. The following algorithms were implemented: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forests, AdaBoost, Gradient Boosting, Support Vector Classifier and Artificial Neural Networks. Random Forest displayed the best results overall and was selected for the subsequent analyses. The Permutation Importance method was used to identify the variables that influence the inclusion cleanliness and the impact of these variables was determined by means of Partial Dependence Plots. The influence of the final diameter of the coil has been interpreted considering the changes induced by the process of hot rolling in the distribution of inclusions. Several variables related to the secondary metallurgy and tundish operations have been identified and interpreted in metallurgical terms. In addition, the inspection area during the microscopic examination of the samples also appears to influence the inclusion content. Recommendations have been established for the sampling process and for the manufacturing conditions to optimize the inclusionary cleanliness of the steel.
Highlights
The steelmaking industry imposes tight controls on steel cleanliness because nonmetallic inclusions (NMIs) negatively influence both the manufacture and the application of steel products
This paper is focused on developing an Machine Learning (ML) model for the reliable prediction of the K3 index, as defined by the DIN 50602 [9] standard, of a clean cold forming steel fabricated by electric arc furnace and hot rolling
To achieve an adequate understanding, it is necessary to pret the results derived from the ML study in terms of the physical processes involved interpret the results derived from the ML study in terms of the physical processes involved during manufacturing, as well as with the variables that participate in the experimental during manufacturing, as well as with the variables that participate in the experimental determination of the K3 index
Summary
The steelmaking industry imposes tight controls on steel cleanliness because nonmetallic inclusions (NMIs) negatively influence both the manufacture and the application of steel products. NMIs of different nature (mostly oxides, sulfides and nitrides) are always present in steel, but their amount and size greatly varies. They come from the combination between the low solubility metallic elements present in the liquid steel with elements such as oxygen, sulfur or nitrogen. NMIs are classified as “endogenous” or “exogenous” The former occurs within the liquid steel, precipitating out during cooling and solidification (for example, during deoxidation, because of the intentional addition of calcium to combine with sulfur). Available online: https://towardsdatascience.com/introduction-todata-preprocessing-in-machine-learning-a9fa83a5dc9d (accessed on 9 June 2020). Hands-On Machine Learning with Scikit-Learn and TensorFlow; O’Reilly Media, Inc.: Newton, MA, USA, 2017; ISBN 9781491962299. A Guide for Data Scientists; O’Reilly Media, Inc.: Newton, MA, USA, 2016; ISBN 978-1449369415
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
