## 1 Introduction  
The casting process is an old manufacturing technology of which the fundamental principles have not changed to this day. The scientific knowledge about the mentioned production process has been constantly evolving, and as a result, nowadays there are efficient casting processes which make the casting industry diverse and often specialized for a particular type of casting parts. However, a special challenge today is the complete implementation of the idea brought by Industry 4.0 in the foundries. This idea is to build smart foundries, characterized by a centralized system that manages customer orders, supply chains and production activities. The developmental path to the realization of the concept of the smart foundry is long and requires a workforce that understands the importance of the concept, and is ready to monitor the progress in technology. One of the segments of approaching the concept of the smart foundry is the application of artificial intelligence techniques in research, and development of production processes in foundries, as well as their implementation in regular production structures. In the last fifteen years, many researchers have promoted the application of artificial intelligence in the casting process field. Below are provided the main results of the studies in which artificial intelligence techniques have been applied in the following technologies in foundries: the melting metal process, sand casting process, die casting process, continuous casting process and investment casting process. [PERSON] et al. (2004) presented the application of neural networks in the steel industry. Namely, they introduced the control of the melting process by using neural networks, with the goal to optimally control the input variables such as the weights of additives (FeMn,FeSi, and coke) and heating temperature (T) [1]. [PERSON] et al. (2008) developed a neuro-fuzzy model for improving the control through a better prediction of the final temperature and, as a consequence, to reducing the consumption of energy in the electric arc furnace [2]. [PERSON] et al. (2010) presented the control strategies of an electric arc furnace based on artificial neural networks (ANNs) and an adaptive neuro fuzzy inference system (ANFIS). It involves the prediction of the control action which aids in the reduction of carbon, manganese, and other impurities from the in-process molten steel [3]. [PERSON] and [PERSON] (2008) developed an intelligent system based on backpropagation neural networks, with a goal to predict major casting defects such as cracks, misruns, scabs, blowholes and air-locks. Input parameters in the neural network were: green compression strength, green shear strength, permeability, moisture percent, composition of the charge and melting conditions. The research was based on a large set of data, collected in the foundry. The goal of the developed neural network was the preventive action and the prediction of potential defects which were the result of the sand mold system characteristics [4]. [PERSON] et al. (2012) used a genetic algorithm (GA) and particle swarm optimization (PSO) in the optimization of green sand mold system. After establishing a correlation between the process parameters, such as grain fineness number, percentage of clay, percentage of water and number of strokes, and responses like green compression strength, permeability, hardness and bulk density, finding an optimal solution for the green sand mold system is performed by using the above-mentioned optimization techniques. The optimization goal was to find a compromise solution that fulfills and respects all four specially considered objectives: green compression strength, permeability, hardness, and bulk density [5]. [PERSON] et al. (2016) in their study presented a methodology that enables effective reduction in the defects produces during casting and improvement of the casting quality. Their goal was to minimize the parameters such as filling time, solidification time and oxide ratio.