Mold breakout prediction based on computer vision and machine learning  

作　　者：Yan-yu Wang Qi-can Wang Yong-chang Zhang Yong-hui Cheng Man Yao Xu-dong Wang 

机构地区：[1]School of Materials Science and Engineering,Dalian University of Technology,Dalian 116024,Liaoning,China [2]Key Laboratory of Solidification Control and Digital Preparation Technology,Dalian University of Technology,Dalian 116024,Liaoning,China

出　　处：《Journal of Iron and Steel Research International》2024年第8期1947-1959,共13页钢铁研究学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China(51974056).

摘　　要：Breakout is the most serious production accident in continuous casting and must be detected and predicted by stable and reliable methods.The sticking region,which forms on the local copper plate and expanded into a"V"shape,is the typical precursor of breakout.Therefore,computer vision technology was exploited to visualize the temperature change rate of the copper plate based on the temperature signals from thermocouples;then,the static and dynamic features of the abnormal sticking region were extracted.Meanwhile,logistic regression and Adaboost models were used to study and identify these features,resulting in the development of a mold breakout prediction model based on computer vision and machine learning.The test results demonstrate that the proposed model can effectively distinguish anomalous temperature patterns and considerably reduce false alarms without any missing reports.As a result,the proposed method could offer valuable insights into the realm of abnormality detection and prediction during continuous casting process.

关 键 词：Abnormality detection Image processing ADABOOST Logistic regression Continuous casting 

分 类 号：TF70[冶金工程—钢铁冶金] TP39[自动化与计算机技术—计算机应用技术]