Statistical Control of Defects in a Continuously Cast Billet Based on Machine Learning and Data Analysis Methods

Abstract

We consider the problems of defects arising in the production of continuously cast billets at continuous casting plants. We propose a model for predicting slab cracks based on the random forest machine learning algorithm. We determine the main technological parameters that influence the appearance of cracks and present the results of the model.