Abstract

The cleanliness and defects for cold-rolled steel sheet caused by inclusions are greatly influenced by parameters in the metallurgical processing. Good control of parameters during the processing can lead to a better product. In this paper, data mining was used to explore the influence of parameters on defects in steel sheets. A decision tree model was established and it was found that the oxygen content before deoxidation, the end-point temperature of the converter, and the temperature before deoxidation had a great impact on the defects in the cold-rolled sheet that were caused by inclusions. This finding was confirmed by experiments with infrared absorption, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and automatic inclusion analysis methods. After optimization according to results from the model and experiments, the defect rate caused by the inclusions was reduced from 0.92% to 0.38%.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call