Quality prediction of the continuous casting bloom based on the extreme learning machine

Abstract

To solve the problems of slow training, weak generalization ability, and low prediction accuracy in the traditional prediction model established in terms of the BP neural network, a method of the quality prediction of the continuous casting bloom based on the extreme learning machine (ELM) was proposed to predict the degree of the center porosity and the central segregation of 60Si2Mn continuous casting bloom produced by Fangda Special Steel. Comparing the prediction models of the BP neural network and the GA-BP neural network, the results show that the prediction accuracy of the model based on ELM is improved to 85% and 82.5% in the center loose and central segregation, respectively, and the operation time is reduced to 0.1 s. The model can rapidly and accurately analyze the quality of a continuous casting billet, thus providing a new method for the online application of continuous casting billet quality prediction.

Keywords

• continuous casting bloom
• bp neural network
• genetic algorithm
• extreme learning machine
• quality prediction