Mold breakout prediction based on computer vision and machine learning

Abstract

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.