Application practice of molten iron automatic slag skimming system based on machine vision and path planning algorithm

With the rapid development of the iron and steel industry, there is an increasingly urgent demand for automation and intellectualization in slag skimming operations. Aiming at the current situation where slag skimming operations mainly rely on manual work, which suffers from low efficiency, poor accuracy and high safety risks, this paper proposes an automatic molten iron slag skimming system based on machine vision and path planning algorithm. On the basis of fully investigating the on-site working logic of molten iron desulfurization and slag skimming, the system integrates deep learning convolutional neural network and intelligent visual perception methods. It can accurately segment the elliptical area of the molten iron ladle mouth, and complete the distinction between molten iron and light/dark slag through image preprocessing, gray feature analysis and Otsu multi-threshold segmentation, thereby realizing the functions of intelligent determination of slag amount on the molten iron surface and automatic path planning of the slag skimming arm. This paper elaborates on the design principle, implementation method of the system and its effect in practical application. Through the operation verification in the intelligent control center of a steel plant, the recognition error of molten iron area is no greater than 5%, and the determination qualification rate based on 500 ladles of molten iron is no less than 96%. These results significantly demonstrate its advantages in improving the efficiency and accuracy of slag skimming, providing new ideas and practical cases for the intelligent transformation of the iron and steel industry.