Artificial neural network application for modeling the rail rolling process

Rail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabük, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making.