Determination of destruction cause of experimental mill roll of stand roughing group of mill 150.

High quality of purchased resources plays a critical role under the conditions of severe international competition. Raw materials, materials and components are purchase for product production and their quality is an essential part in the production of steel at OJSC “BSW – Management Company of the Holding “BMC” holding». The problem of excessively high level of dynamic loads is especially acute for roughing stands. The main reasons for dynamic overloads are intense transient processes occurring in the rolling cycle, the presence of elastic constraints and gaps in the system, imperfection of electric drive system and errors made during development. Therefore, there is a need for selection of material and manufacturing technology for mill rolls capable to withstand such loads for a sufficiently long period. Investigations of destruction causes of experimental mill roll of stand roughing group of mill 150 of section rolling shop at OJSC “BSW – Management Company of the Holding “BMC” holding were carried out. Main classification features are established based on the analysis of the fracture. The results of metallographic studies of microstructure and chemical composition of central and subsurface zone roll fragments are presented. The reason of destruction of experimental mill roll was discovered – nonconformance of roll material grade with the requirements of technological instructions and normative documentation for rough group of stands No. 1–3

Big Data as a Tool for Building a Predictive Model of Mill 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.