Character of blooming mill roll wear

Metallurgist ◽  
1979 ◽  
Vol 23 (4) ◽  
pp. 273-275
Author(s):  
V. A. Chegolya ◽  
V. T. Litvinenko ◽  
V. G. Chernyatevich ◽  
A. F. Trofimov
Keyword(s):  
Mill Roll ◽  
Roll Wear

scholarly journals Big Data as a Tool for Building a Predictive Model of Mill Roll Wear

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 859
Author(s):  
Natalia Vasilyeva ◽  
Elmira Fedorova ◽  
Alexandr Kolesnikov
Keyword(s):  
Random Forest ◽  
Predictive Model ◽  
Mill Roll ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Large Array ◽  
Operational Control ◽  
Control Data ◽  
Straight Line ◽  
Roll Wear

Big data analysis is becoming a daily task for companies all over the world as well as for Russian companies. With advances in technology and reduced storage costs, companies today can collect and store large amounts of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will ultimately be faced by all companies seeking to maintain their competitiveness and place in the market. An approach to the study of metallurgical processes using the analysis of a large array of operational control data is considered. Using the example of steel rolling production, the development of a predictive model based on processing a large array of operational control data is considered. The aim of the work is to develop a predictive model of rolling mill roll wear based on a large array of operational control data containing information about the time of filling and unloading of rolls, rolled assortment, roll material, and time during which the roll is in operation. Preliminary preparation of data for modeling was carried out, which includes the removal of outliers, uncharacteristic and random measurement results (misses), as well as data gaps. Correlation analysis of the data showed that the dimensions and grades of rolled steel sheets, as well as the material from which the rolls are made, have the greatest influence on the wear of rolling mill rolls. Based on the processing of a large array of operational control data, various predictive models of the technological process were designed. The adequacy of the models was assessed by the value of the mean square error (MSE), the coefficient of determination (R2), and the value of the Pearson correlation coefficient (R) between the calculated and experimental values of the mill roll wear. In addition, the adequacy of the models was assessed by the symmetry of the values predicted by the model relative to the straight line Ypredicted = Yactual. Linear models constructed using the least squares method and cross-validation turned out to be inadequate (the coefficient of determination R2 does not exceed 0.3) to the research object. The following regressions were built on the basis of the same operational control database: Linear Regression multivariate, Lasso multivariate, Ridge multivariate, and ElasticNet multivariate. However, these models also turned out to be inadequate to the object of the research. Testing these models for symmetry showed that, in all cases, there is an underestimation of the predicted values. Models using algorithm composition have also been built. The methods of random forest and gradient boosting are considered. Both methods were found to be adequate for the object of the research (for the random forest model, the coefficient of determination is R2 = 0.798; for the gradient boosting model, the coefficient of determination is R2 = 0.847). However, the gradient boosting algorithm is recognized as preferable thanks to its high accuracy compared with the random forest algorithm. Control data for symmetry in reference to the straight line Ypredicted = Yactual showed that, in the case of developing the random forest model, there is a tendency to underestimate the predicted values (the calculated values are located below the straight line). In the case of developing a gradient boosting model, the predicted values are located symmetrically regarding the straight line Ypredicted = Yactual. Therefore, the gradient boosting model is preferred. The predictive model of mill roll wear will allow rational use of rolls in terms of minimizing overall roll wear. Thus, the proposed model will make it possible to redistribute the existing work rolls between the stands in order to reduce the total wear of the rolls.

Reduction in slabbing-mill roll wear

Metallurgist ◽  
1981 ◽  
Vol 25 (8) ◽  
pp. 321-322
Author(s):  
V. I. Beznos ◽  
A. E. Rudnev ◽  
D. I. Isirov ◽  
B. E. Dubinskii ◽  
I. A. Titarenko
Keyword(s):  
Mill Roll ◽  
Slabbing Mill ◽  
Roll Wear

Reduction in Roll Wear Through Technological Interventions at CRM & SSM of Rourkela Steel Plant

2018 ◽  
Author(s):  
S.K. Thakur ◽  
S. K. Mohapatra ◽  
P. Pathak ◽  
D. Roy ◽  
C. Mishra
Keyword(s):  
Steel Plant ◽  
Roll Wear ◽  
Technological Interventions

Study on Roll Wear Model for Hot Strip Mill

2012 ◽  
Vol 535-537 ◽  
pp. 697-700
Author(s):  
Zhong Feng Guo ◽  
Jun Hong Hu ◽  
Xue Yan Sun
Keyword(s):  
Work Roll ◽  
Hot Strip Mill ◽  
Rolling Schedule ◽  
Wear Model ◽  
C Language ◽  
Hot Strip ◽  
Roll Wear ◽  
Calculation Results ◽  
Strip Length ◽  
Good Agreement

Roll wear model for Hot Strip Mill (HSM) was researched and the factors affect roll wear are analyzed. The simulation program was compiled by program visual C++ language and work roll wear was calculated according to the rolling schedule. Calculation results shows that roll wear like box shape. Strip width affects roll wear clearly. The strip length is one of the important issues which affect roll wear. Work roll wear of F7 top roll middle get to 280μm after a rolling schedule. Roll wear curve calculated by program were good agreement with the wear curve got by high-precision grinder. The results show that the roll wear model has high accuracy.

The Influence of the Interstand Tension of the Band on Roll Wear during the Continuous Groove-Rolling Process

2015 ◽  
Vol 220-221 ◽  
pp. 898-904 ◽  
Author(s):  
Piotr Szota ◽  
Sebastian Mróz ◽  
Andrzej Stefanik ◽  
Henryk Dyja
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Quantitative Evaluation ◽  
Theoretical Study ◽  
Rolling Process ◽  
Wear Model ◽  
Bar Rolling ◽  
Groove Rolling ◽  
Roll Wear ◽  
Round Bar

Numerical modelling of the round bar rolling process, while considering the wear of passes depending on their shape, was carried out within the present work. The analysis of the rolling process was conducted thus analysing the influence of interstand tension on roll wear. For the theoretical study of the rolling process, Forge2011® was employed, which is finite element method-relying software that enables the thermo-mechanical simulation of rolling processes in a triaxial state of strain. The wear model implemented in Forge2011® permits no quantitative evaluation, but only a comparative analysis of the wear of rolls. In order to use the results of simulation employing the simplified Archard model for the quantitative evaluation of roll wear, it is necessary to define the factor of wear and the hardness of the tool as a function of temperature.

scholarly journals Reproduction of Hot Rolling Mill Roll with High-Speed Tool Steel Type Shell Material Manufactured by Continuous Pouring Process for Cladding

2019 ◽  
Vol 60 (5) ◽  
pp. 770-776 ◽  
Author(s):  
Atsuo Yamamoto ◽  
Yoshio Ishii ◽  
Hyo-Gyoun Kang ◽  
Futoshi Sakata ◽  
Akio Sonoda ◽  
...  
Keyword(s):  
Tool Steel ◽  
Hot Rolling ◽  
Rolling Mill ◽  
High Speed ◽  
Mill Roll ◽  
Shell Material ◽  
Steel Type ◽  
Rolling Mill Roll ◽  
Hot Rolling Mill

Mathematical modelling of roll wear in a tandem mill hot strip rolling process

2013 ◽  
Vol 4 (1) ◽  
pp. 56
Author(s):  
Carlos Arturo Vega Lebrún ◽  
Rumualdo Servin Castañeda ◽  
Genoveva Rosano Ortega ◽  
Juan Manuel Lopez ◽  
José Luis Cendejas Valdéz ◽  
...  
Keyword(s):  
Mathematical Modelling ◽  
Rolling Process ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Hot Strip ◽  
Roll Wear

DEVELOPMENT OF PLATE MILL ROLL GRADES

2012 ◽  
Author(s):  
Tommy Nylen ◽  
Michael Brandner ◽  
Alexander Mayr.
Keyword(s):  
Mill Roll ◽  
Plate Mill
Sign in / Sign up

Export Citation Format

Share Document