High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

Section: Database and Data Preprocessingmentioning

confidence: 99%

Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction

Huang

et al. 2022

“…First, the input data are mapped into a high-dimensional feature space by a function column vector of ϕ x ð Þ. The linear regression is conducted in the feature space as shown in Equation (12).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model

Zhang

Yang

et al. 2022

Self Cite

As a harmful element, phosphorus will cause cold brittleness that decreases the strength of steel. In order to ensure the mechanical properties of steel products, the phosphorus is generally removed from molten steel in BOF (basic oxygen furnace) steelmaking process. Therefore, the prediction of end-point P content in BOF is of great significance for steelmaking production.Several methods are usually employed to predict the end-point P content. The first one is using simple empirical formulas based on the previous production data. Through this method, the steelmaking workers can quickly predict the end-point P content on site and then promptly adjust the process parameters of BOF. However, the accuracy of this method is pretty low, and the prediction can only be used as a reference. Another method is the end-point P content prediction with the metallurgical mechanism from the perspective of dephosphorization kinetics. The dephosphorization kinetic model is established based on the coupled reaction model or the first principle. [1] The advantage of this method is that one can obtain not only the end-point P content but also the P content during the process. Although the metallurgical mechanism model (MMM) is with high accuracy, it takes as long as 20 min to complete the calculation for one heat, which cannot satisfy the quick steelmaking process. [2] With the progresses of computer equipment and artificial intelligence algorithms, the machine learning shows its outstanding capabilities in various fields in classification, regression, data mining, image recognition, and so on. The machine learning has also been applied in BOF end-point P content prediction for many years. He et al. proposed a prediction model based on the principal component analysis (PCA) and back propagation neural network (BPNN). [3] The results showed that the PCA-BPNN model had the highest prediction accuracy compared with the multiple linear regression (MLR) model and the normal BPNN model. Phull et al. and Li et al. used twin support vector machines (TWSVM) to classify the degrees of phosphorus removal (such as "High" and 'Low"). [4,5] The models achieved a high accuracy of 99.9% in classification, and the running time was shorter than the other models such as logistic regression and SVM radial basis. Wang et al. established a hybrid method for the end-point prediction of BOF. [6] This model could not only predict the end-point P content but also forecast the end-point C content and temperature. After applying the hybrid algorithm to improve the theoretical model and artificial neural networks (ANN), the performances of the models were improved. Moreover, many researchers applied different models to predict the end-point P content, such as membrane algorithm evolving extreme learning machine, [7] group method of data handling polynomial neural network, [8] and monotone-constrained BP neural network. [9]

“…Ji et al [ 14 ] established a high accuracy prediction model of a strip crown based on support vector machine (SVM), which provided theoretical guidance for hot rolling production. Wu et al [ 15 ] applied a Bayesian neural network for mechanical property modeling of C–Mn steel and optimized the strip process using a strength Pareto evolution multiobjective optimization algorithm. Experiments showed that this method established a reliable model for optimizing the process design.…”

Section: Introductionmentioning

confidence: 99%

Strip Thickness and Profile–Flatness Prediction in Tandem Hot Rolling Process Using Mechanism Model‐Guided Machine Learning

Liu

Wang

Sun

et al. 2022

This study is focused on the multivariable, nonlinear, strong‐coupling, and time‐varying characteristics of the quality control process of hot strip rolling. Using the rolling process data from a continuously variable crown (CVC) mill of a 1580 mm hot tandem rolling line, a new prediction model of the strip thickness, profile, and flatness in hot rolling based on rolling mechanism model‐guided machine learning (ML) is developed. The weighted processing (WP) method is used to fuse the rolling mechanism and process data, to improve the relationship between the strong correlation features and the model. Combined with nondominated sorting genetic algorithm III (NSGA‐III), multiple parameters of multioutput support vector regression (M‐SVR) are optimized. The results show that the root mean square error (RMSE), mean square correlation coefficient (R 2), and mean absolute error (MAE) of the strip thickness, crown, and flatness are 2.0159, 1.3191, and 0.9355; 0.9854, 0.9895, and 0.9873; and 16.601, 1.126, and 0.604, respectively. Moreover, the established method of data fusion rolling mechanism shows strong capability to improve the model prediction accuracy, increasing it by 60%. Thus, it can offer theoretical guidance for realizing accurate control of the quality of strips and improving the quality of hot‐rolled products.