# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review  
### Improvement of the Sand Casting Process  
The sand casting process is a very frequently used casting process that provides the possibility of producing a variety of metal components with complex geometries. The produced parts can vary greatly in size and weight, ranging from a couple of ounces to several tons. The mold's properties have a significant influence on the quality of the sand casting process, i.e., the casting part quality. These properties are: dimensional and thermal stability at elevated temperatures, permeability, hardness, consistent cleanliness, composition, and others. All of the above-mentioned mold properties depend on the process parameters, such as the grain size and shape, the percentage of clay, and the percentage of water, etc. The mentioned dependence between the process parameters and mold characteristics is a research challenge that has existed for many years. In 1960s, [PERSON] and [PERSON] defined the pressure and hardness distributions in sand molds [18]. [PERSON] developed significant mathematical equations that established the correlation between the important parameters and quality of sand molds [19]. [PERSON] provided a strong theoretical approach by explaining sand-clay-water relationships [20]. The application of artificial intelligence isnoticeable in recent research studies. Its application is reflected in the modeling of complex dependencies between input parameters and mold characteristics, as well as in the optimization of input parameters to secure a mold with the pre-defined characteristics. [PERSON] and [PERSON] researched the definition of the optimal composition of a green sand mixture in order to obtain the desired characteristics of sand mold systems. In their study, they used neural networks and genetic algorithms to achieve the defined goal. In the first part of the study, they trained neural networks, the inputs of which were the mold characteristics (green compression strength, green shear strength, permeability, dry compression strength and dry shear strength), and the outputs of which were the process parameters (the grain fineness number, clay percentage, moisture percentage, mulled time, and hardness). The task of the developed neural network was to project the process parameters based on the desired characteristics of the mold. In the second part of the study, the fitness function for the work of the genetic algorithm was formed. The task was the same as in the first part of the study: the prediction of the process parameters with which the desired mold characteristics were obtained. When it comes to this task, genetic algorithms yielded better results [21]. In order to help detect the most common casting defects--such as cracks, misruns, scabs, blowholes, and airlocks--the same research team, [PERSON] and [PERSON], designed an intelligent system based on back-propagation neural networks. The research was based on the utilization of an immense database, and the main objective was to upgrade the prevention features of the neural network prediction so that potential faults which were the result of the sand mold system's characteristics could be detected more easily [22]. The neural network input parameters were the composition of the charge, melting conditions, moisture percentage, green compression strength, permeability, and green shear strength. For the optimization of a green sand mold system, [PERSON] et al. used particle swarm optimization (PSO) and genetic algorithms (GA). They utilized the specific optimization approach to achieve the optimal solution for a green sand mold system, by which they calculated a correlation between the process parameters such as the grain fineness number, percentage of clay, percentage of water, and number of strokes, and responses such as the green compression strength, permeability, hardness, and bulk density. Their objective was to reach a middle-ground solution that included all of the four satisfactory features: green compression strength, permeability, hardness, and bulk density [23].