Prediction Model for Elongation of Tension Leveling Based on Machine Learning Algorithm and Numerical Analysis

doi:10.12068/j.issn.1005-3026.2022.02.012

Abstract

Abstract: In the process of cold rolling bending straightening， aiming at the setting of process parameters of tension straightener， the prediction accuracy of elongation models established by empirical formula and finite element simulation is not high. To improve the accuracy， the traditional analytical models and machine learning algorithms are studied. The accuracies of the two methods are compared. It is found that the elongation prediction model of machine learning algorithm has higher goodness of fit (R²) than the numerical analytical model. Comparing BP neural network algorithm with SVM (support vector machine) algorithm， the prediction model accuracies of the two machine learning algorithms are basically the same. In order to further improve the prediction accuracy， the BP neural network is optimized by Adam algorithm， and the parameters of SVM prediction model are optimized by genetic algorithm. Finally， the MAPE(mean absolute percentage error) and R^○2 of the optimal prediction model are 13.4% and 0.953 respectively， which can provide technical guidance for actual production.

Key words: SVM; BP neural network; elongation; prediction model optimization; cold rolled sheet

CLC Number:

TG333.2

CHEN Bing， HAN Jin-yang， TANG Xiao-lei， XIA Bo-ran. Prediction Model for Elongation of Tension Leveling Based on Machine Learning Algorithm and Numerical Analysis[J]. Journal of Northeastern University(Natural Science), 2022, 43(2): 236-242.

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