JSC Vyksa Steel Works, Vyksa, Russia ; Bauman Moscow State Technical University, Moscow, Russia:

A. E. Sevidov, Leading Software Engineer¹, Postgraduate Student², e-mail: sevidov_ae@vsw.ru
A. V. Muntin, Cand. Eng., Deputy Director for Research Activities¹, Associate Prof.², e-mail: muntin_av@omk.ru

Bauman Moscow State Technical University, Moscow, Russia:
A. G. Kolesnikov, Dr. Eng., Prof., Head of the Scientific and Educational Complex "Machine-Building Technologies"

Modern methods of modeling a mechanical wear of hot rolling mill work rolls under industrial conditions are presented. The use of machine learning algorithms based on multidimensional linear regression, decision trees, and neural networks made it possible to describe more accurately the features of work roll wear depending on many technological factors. At the same time, the data set from the production database was subjected to additional classification for each technological parameter in a certain range. This classification approach significantly improved the quality of the output model for all major metrics. The input variables for training were the main technological factors measured in the finishing group of stands during the campaign of each work roll. The output parameters – wear curves on a roll grinder measured after roll changes. 93 input parameters were used to calculate each of the 300 output values of mechanical wear along the length of the roll barrel and to train machine learning algorithms. Roll wear forecasting models based on multivariate linear regression, random forest, gradient boosting and neural networks have been developed. A comparative analysis of the developed models with classical approaches for calculating the work roll mechanical wear showed that machine learning methods have a higher accuracy. An approach and recommendations for the introduction of new models to the automated process control system of hot strip rolling mills were proposed.
The research was carried out within the framework of the program of strategic academic leadership of the Russian Federation "Priority 2030", aimed at supporting the development programs of educational institutions of higher education, the scientific project PRIOR/SN/NU/22/SP5/26 "Creation of innovative digital tools for the use of applied artificial intelligence and advanced statistical analysis of big data in technological processes of manufacture of metallurgical products", as well as within the framework of scientific and technical cooperation between JSC Vyksa Metallurgical Plant and Bauman MSTU.

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