# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review
### Improvement of the Pressure Die-Casting Process  
The die-casting process is a quick, reliable, and cost-effective manufacturing process which includes many attributes that contribute to the complexity of the process. The mutual relationships of the process parameters are complex, non-linear, and contradictory. This makes finding their optimal values more complicated. Artificial intelligence techniques have significantly contributed to the improvement of the process through various forms of optimization and modeling. [PERSON] et al. presented the modeling of the high-pressure die-casting process (HPDC) by using an artificial neural network (ANN). Their ANN model established a correlation between the input values--such as the inlet melt temperature, initial mold temperature, inlet first phase velocity, and inlet second phase velocity--and the output values of the filling time, solidification time and porosity. The development of the ANN model was based on the results obtained from the numerical simulation performed using ProCast software (a FEM-based flow simulation software) [33]. [PERSON] presented a model based on the genetic algorithm's (GA) application in the definition of the optimal process conditions in pressure die casting. His study aimed to minimize the porosity in the casting part--aluminum alloy AlSi\({}_{9}\)Cu\({}_{3}\)[34]. [PERSON] et al. introduced an evaluation system to quantify the surface defects of the casting part. Their solution referred to the high-pressure die-casting process (HPDC). The artificial neural network was used to establish the correlation between surface defects and die casting parameters, such as the mold temperature, pouring temperature, and injection velocity. The developed neural network showed great ability in prediction, and was used to find the optimal process parameters. The optimal parameters were employed, and casting parts with acceptable surface quality were achieved [35]. For the selection of the optimal conditions in the high-pressure die-casting (HPDC) process, [PERSON] designed an efficient and reliable methodological tool. An adaptive neuro-fuzzy inference system (ANFIS) was applied to investigate the effect of die-casting parameters on porosity formation in AlSi9 Cu3 pressure die castings, utilizing the following inputs: the metal temperature, the die temperature, the piston velocity in the low phase, the die gate velocity, and the solidification pressure. Having compared the experimental results with the predicted values, the author suggested that the proposed model should be regarded an efficient tool in defining optimal process conditions in pressure die casting associated with the minimum porosity percentage [36]. [PERSON] and [PERSON] combined artificial neural networks and genetic algorithms (ANN/GA) to optimize the low-pressure die-cast (LPDC) process. In order to establish the correlation between the process parameters and the casting part quality, an artificial neural network was utilized. The data which were used to build the neural network models were obtained by the numerical simulation of the process. In order to optimize the process parameters with the fitness function based on the trained ANN model, the genetic algorithm was employed [37]. [PERSON] et al. used a back-propagation neural network (BPNN) algorithm for the modeling of the high pressure die-casting (HPDC) process. They established a correlation between the process parameters--the fast shot velocity, the intensification pressure, the phase change-over point and the holding time--and the casting part characteristics of the surface roughness, hardness and porosity. The results showed that the BPNN approach was able to carry out both the forward and reverse mappings effectively, and can be used in the foundries [38]. [PERSON] et al. represented the forward and reverse modeling of the squeeze casting process based on neural networks (ANN).