Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Abstract

Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model.

Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

“…The results indicated that the model was suitable for a complex hot-rolling process. Zou et al [13] established a carbon segregation prediction model for CC billets based on the regularized ELM (RELM) algorithm, by comparing it with multiple linear regression and the classical ELM model. The results showed that the RELM model had better prediction accuracy and generalization ability.…”

“…Effective quality prediction can improve the final slab quality, save energy consumption, and stabilize the production. Several machine learning algorithms have been widely used by scholars in the field of CC to construct excellent quality prediction models, such as: random forest (RF) [8], [9], support vector machine (SVM) [10], [11], and artificial neural networks, especially extreme learning machine (ELM) [12], [13]. Nevertheless, most studies ignore the natural class imbalance in CC datasets that the number of normal slabs is much more than that of abnormal slabs.…”

Investigations on Decreased High Temperature Ductility of Different Continuously Cast Steel Grades

“…Rejection of elements from the solid phase leads to an increase in the residual melt and consequently to a positive microsegregation. [ 31–33 ] The primary solidification in steel castings can be columnar dendritic, equiaxed dendritic, or globular, depending on the boundary conditions. [ 34–37 ] During dendritic solidification, the microsegregated melt gets captured between the dendritic arms.…”

Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

“…Varfolomeev et al [26] and Ye et al [27] used the random forest algorithm to predict crack occurrence in the continuous casting billets. Furthermore, researchers, including the author, have also applied AI algorithms to predict central carbon segregation in continuous casting billets [28][29][30]. However, the previous studies only assessed whether cracks would occur (two-category problem) or determined the probability of cracks.…”