Preliminary communication

https://doi.org/10.31803/tg-20220325193331

Predicting of Roll Surface Re-Machining Using Artificial Neural Network

Miha Kovačič ; ŠTORE STEEL d.o.o., Štore Železarska cesta 3, 3220 Štore, Slovenia / University of Ljubljana, Faculty of mechanical engineering, Ljubljana Aškerčeva cesta 6, 1000 Ljubljana / College of Industrial Engineering Celje, Celje Mariborska cesta 2, 3000 Celje, Slovenia
Andrej Mihevc ; ŠTORE STEEL d.o.o., Štore, Železarska cesta 3, 3220 Štore, Slovenia
Milan Terčelj ; Faculty of Natural Sciences and Engineering, Department of Materials and Metallurgy, Aškerčeva cesta 12, 1000 Ljubljana, Slovenia
Uroš Župerl orcid.org/0000-0002-1505-5085 ; University of Maribor, Faculty of mechanical engineering, Maribor Smetanova ulica 17, 2000 Maribor, Slovenia

Abstract

The paper presents a model for predicting the roll wear in the hot rolling process. It includes all indicators from the entire continuous rolling line that best predict the roll wear in the hot rolling process. Data for model development were obtained from annual production on the first rolling stand of the continuous roll mill. The main goal of the research was to determine significant parameters that affect the wear of the roll in the process of hot rolling. It has been found that the amount of rolled material before the re-machining of the roll surface has the greatest impact on the life of the roll contour. Therefore, the amount of material rolled before re-machining of the roll was used to estimate the wear of the roll. An artificial neural network was used to predict this amount of rolled material and was validated using data from one-year production.

Keywords

artificial neural network; hot rolling; linear regression; prediction; roll wear

Hrčak ID:

276152

URI

https://hrcak.srce.hr/276152

Publication date:

8.5.2022.

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