Determining the boundary conditions of heat transfer in steel manufacturing is a very important issue. The heat transfer effect during contact of two solid bodies occurs in the continuous casting steel process. The temperature fields of solids taking part in heat transfer are described by the Fourier equation. The boundary conditions of heat transfer must be determined to get an accurate solution to the heat conduction equation. The heat flux between the tool and the object processed depends mainly on temperature, pressure and time. It is very difficult and complicated to accomplish direct identification and determination of the boundary conditions in this process. The solution to this problem may be the construction of a process model, performing measurements at a test stand, and using numerical methods. The proposed model must be verified on the basis of parameters which can easily be measured in industrial processes. One of them is temperature, which may be used in inverse methods to determine the heat transfer coefficient. This work presents the methodology for determining the heat flux between two solid bodies staying in contact. It consists of two stages – the experiment and the numerical computation. The problem was solved by using the finite element method (FEM) and a numerical program developed at AGH University of Science and Technology in Krakow. The findings of the conducted research are relationships describing the value of the heat flux versus the contact time and surface temperature.
The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.