Defects analysis of protective slag based on multi-model stacked ensemble algorithm
Due to the increasing requirements of current steel product quality,a stacking algorithm based on multiple machine learning models was proposed to improve on-site production while reduc-ing slag defects caused by high-frequency data fluctuations within the continuous casting.Firstly,high-frequency feature data which extracted from the continuous casting mold is segmented into sub-sequences by sliding window,important features in each window are extracted from the fluctuation patterns of the high-frequency data.Secondly,the locations of the slag defects which identified by the hot-rolling surface inspector are corresponded to the window locations.Finally,a stacked classification algorithm that combines multiple machine learning models is utilized for defect prediction.Compari-son experimental results show that the stacking classification model performed better than each single machine learning model and voting classification model,and the overall prediction performance and generalization ability is better than others.Currently,this slag defect analysis model based on the stacked ensemble algorithm has been deployed on a hot-rolling production line.The comparison of on-line prediction results with actual data demonstrates that the occurrence and location of slag defects can be predicted in advance by the model to improve the efficiency of slag defect analysis and new approaches and insights are given for investigating the causes of slag defects.
NETL
NSTL
万方数据
