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.
Highlights
In order to reduce production cost and improve product competitiveness in the continuous casting process of steel, technologies have been developing rapidly, such as continuous casting hot charging and continuous casting direct rolling [1,2]
Central carbon segregation is a common internal defect of continuous cast steel billets, leading to non-uniformity in the mechanical properties of the final products, which can hardly be alleviated by the subsequent heat treatment and rolling processes [3]
Extreme learning machine (ELM) has the advantages of fewer setting parameters, faster learning speed, and stronger generalization ability compared with other artificial neural networks (ANNs), two major issues still remain in ELM [26,27,28,29]: (1)
Summary
In order to reduce production cost and improve product competitiveness in the continuous casting process of steel, technologies have been developing rapidly, such as continuous casting hot charging and continuous casting direct rolling [1,2]. Traditional quality evaluation methods have been unable to meet the needs of modern steel plants, and there is no chance to check the steel billets off-line through artificial sampling due to its compact and continuous production characteristics. In this setting, effective online prediction models of quality defects are urgently needed to guide the actual production. Electromagnetic stirring combined with soft reduction is a common method to suppress central segregation in the continuous casting process of steel. The correctness and generalization ability of the optimal model are further verified
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