# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review
### Improvement of the Investment Casting Process
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Authors, Year**} & \multicolumn{2}{c}{**Short**} & \multicolumn{3}{c}{**Artificial Intelligence Methods**} \\ \cline{3-6}  & **Description** & **GA** & **PSO** & **ANN** & **FL** & **ANFIS** \\ \hline [PERSON] et al. (2008) & \multicolumn{2}{c}{Identification and the control of a nonlinear heat transfer model.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2009) & \multicolumn{2}{c}{Mold level control.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] and [PERSON] (2011) & \multicolumn{2}{c}{Control of the molten steel level of a strip casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2011) & \multicolumn{2}{c}{Modeling of the correlation between continuous casting technological parameters and cooling rate.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2012) & \multicolumn{2}{c}{Breakout prediction in the continuous casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \[PERSON] et al. (2014) & \multicolumn{2}{c}{Control system for continuous steel casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2014) & \multicolumn{2}{c}{Estimation of the heat flux distribution at the metal-mold interface in slab continuous casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2016) & \multicolumn{2}{c}{Optimization of cooling strategy and solidification process in continuous casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2020) & \multicolumn{2}{c}{Control of the mold level in the continuous casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline [PERSON] et al. (2020) & \multicolumn{2}{c}{Optimization of the steelmaking-continuous casting process.} & \multicolumn{3}{c}{\(\bullet\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Artificial intelligence methods used for the improvement of the continuous casting process. composition of the alloy, and the resulting mechanical properties (yield strength, ultimate tensile strength, and percentage elongation) for 800 heats were collected in an industrial investment casting foundry. The study was based on the analysis of three different models of ANN (backpropagation, momentum and adaptive, and Levenberg-Marquardt). The authors' findings imply that ANN is adequate for the prediction of mechanical properties [56]. [PERSON] and [PERSON] used a genetic algorithm (GA) to improve the quality characteristic (surface finish) of the wax patterns in the investment casting process, with the optimized parameters in the surface finish function such as the holding time, injection temperature and die temperature [57]. [PERSON] presented the successful use of ANN to predict a wider range of investment casting defects based on real-time industrial data. The data of 24 parameters related to the process, chemical composition, and defects were collected from about 500 heats in an industrial investment casting foundry. The best results in the establishment of the correlation between the process parameters and investment casting defects were shown by ANN with a Levenberg and Marquardt algorithm [58]. Table 4 presents a brief overview of the analyzed studies of investment casting process improvement.