Integrated Model of ACWGAN-GP and Computer Vision for Breakout Prediction in Continuous Casting

The accurate prediction of mold sticking breakout is an important prerequisite to ensure the stable and smooth production of the continuous casting process. When sticking breakout occurs, the sticking region expands vertically along the casting direction and horizontally along the strand width direction, forming a V-shaped area on the strand surface. This paper uses computer vision technology to visualize the temperature of mold copper plates, extract the geometric and movement characteristics of the sticking region from time and space perspectives, and construct feature vectors to characterize the V-shaped sticking breakout region. We train and test the auxiliary classifier WGAN-GP (ACWGAN-GP) model on true and false sticking feature vector samples, developing a breakout prediction method based on computer vision and a generative adversarial network. The test results show that the model can distinguish between true sticking breakout and false sticking breakout in terms of mold copper plate temperature, providing a new approach for monitoring abnormalities in the continuous casting process.

Automated summary

This paper utilizes computer vision technology and generative adversarial networks to accurately predict mold sticking breakout by extracting feature vectors of the sticking region.