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Prediction of longitudinal facial crack in steel thin slabs funnel mold using different machine learning algorithms

Prediction of longitudinal facial crack in steel thin slabs funnel mold using different machine... PurposeLongitudinal facial cracks (LFC) are one of the major defects occurring in the continuous-casting stage of thin slab caster using funnel molds. Longitudinal cracks occur mainly owing to non-uniform cooling, varying thermal conductivity along mold length and use of high superheat during casting, improper casting powder characteristics. These defects are difficult to capture and are visible only in the final stages of a process or even at the customer end. Besides, there is a seasonality associated with this defect where defect intensity increases during the winter season. To address the issue, a model-based on data analytics is developed.Design/methodology/approachAround six-month data of steel manufacturing process is taken and around 60 data collection point is analyzed. The model uses different classification machine learning algorithms such as logistic regression, decision tree, ensemble methods of a decision tree, support vector machine and Naïve Bays (for different cut off level) to investigate data.FindingsProposed research framework shows that most of models give good results between cut off level 0.6–0.8 and random forest, gradient boosting for decision trees and support vector machine model performs better compared to other model.Practical implicationsBased on predictions of model steel manufacturing companies can identify the optimal operating range where this defect can be reduced.Originality/valueAn analytical approach to identify LFC defects provides objective models for reduction of LFC defects. By reducing LFC defects, quality of steel can be improved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Innovation Science Emerald Publishing

Prediction of longitudinal facial crack in steel thin slabs funnel mold using different machine learning algorithms

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1757-2223
DOI
10.1108/ijis-09-2020-0172
Publisher site
See Article on Publisher Site

Abstract

PurposeLongitudinal facial cracks (LFC) are one of the major defects occurring in the continuous-casting stage of thin slab caster using funnel molds. Longitudinal cracks occur mainly owing to non-uniform cooling, varying thermal conductivity along mold length and use of high superheat during casting, improper casting powder characteristics. These defects are difficult to capture and are visible only in the final stages of a process or even at the customer end. Besides, there is a seasonality associated with this defect where defect intensity increases during the winter season. To address the issue, a model-based on data analytics is developed.Design/methodology/approachAround six-month data of steel manufacturing process is taken and around 60 data collection point is analyzed. The model uses different classification machine learning algorithms such as logistic regression, decision tree, ensemble methods of a decision tree, support vector machine and Naïve Bays (for different cut off level) to investigate data.FindingsProposed research framework shows that most of models give good results between cut off level 0.6–0.8 and random forest, gradient boosting for decision trees and support vector machine model performs better compared to other model.Practical implicationsBased on predictions of model steel manufacturing companies can identify the optimal operating range where this defect can be reduced.Originality/valueAn analytical approach to identify LFC defects provides objective models for reduction of LFC defects. By reducing LFC defects, quality of steel can be improved.

Journal

International Journal of Innovation ScienceEmerald Publishing

Published: Jan 22, 2021

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