## 1 Introduction
The optimized values were riser diameter, pouring temperatures, pouring speed, riser position and pouring diameter. They used the QPSO algorithm (improved PSO algorithm) as an optimization technique, and it enabled reducing the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively [6]. [PERSON] et al. (2017) presented a methodology of optimization of the gating system for sand casting using the genetic algorithm (GA). The subject of optimization was the geometry of the gating system, namely the cross section of the ingate and the casting height. The main goal of optimization was to minimize the casting process time. Numerical simulation (software MAGMA5, Aachen, Germany) was used to verify the validity of the optimized geometry of the gating system [7]. [PERSON] (2011) developed a reliable and effective methodological tool for the selection of the optimal conditions in the high-pressure die casting (HPDC) process. An adaptive neuro-fuzzy inference system (ANFIS) was applied to study the effect of die casting parameters on porosity formation in AlSi\({}_{9}\)Cu\({}_{3}\) pressure die castings. Inputs into the developed system were: metal temperature, die temperature, piston velocity in low phase, die gate velocity and solidification pressure. After comparing the experimental results with predicted values, the author concluded that the proposed model represented an effective tool in defining optimal process conditions in pressure die-casting-associated processes, with the minimum porosity percent [8]. [PERSON] and [PERSON] (2013) combined the artificial neural network and genetic algorithm (ANN/GA) in order to optimize the low-pressure die-cast (LPDC) process. The artificial neural network was used to establish a correlation between process parameters and casting part quality. The data used to build neural network models were obtained by numerical simulation of the process. The genetic algorithm was employed to optimize the process parameters with the fitness function based on the trained ANN model [9]. [PERSON] et al. (2012) researched the effectiveness of the genetic algorithm-based back propagation (GABP) neural network model and its application to the breakout prediction in the continuous casting process [10]. [PERSON] et al. (2016) presented a combined approach for optimization of the cooling strategy and solidification process in continuous casting. The mentioned approach included a common application of particle swarm optimization (PSO) algorithm, mathematical heat transfer model and the experimental temperature to determine the heat transfer coefficient [11]. [PERSON] and [PERSON] (2014) in their study used ANN to establish a correlation between the mechanical properties of investment castings on one side and the process parameters and chemical composition on the other side. The data of related process parameters (wax making, shell making, dewaxing, melting etc. ), the chemical composition of the alloy and the resulting mechanical properties (ultimate tensile strength, yield strength, and percentage elongation) for 800 heats were collected in an industrial investment casting foundry. Their study included the analysis of three different models of ANN (back propagation, momentum and adaptive, and Levenberg-Marquardt), and the authors concluded that ANN can successfully predict the mechanical properties [12]. [PERSON] and [PERSON] (2014) in their study used the genetic algorithm (GA) to improve the quality characteristic (surface finish) of the wax patterns used in the investment casting process. The optimized parameters in the surface finish function were: injection temperature, holding time and die temperature [13]. The goal of this paper is to present the application of machine learning, as a subset of artificial intelligence (AI), in the control of the white cast iron production. The control task is the control of the alloying process in order to obtain the desired chemical composition of white cast iron.