# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review
### Improvement of the Continuous Casting Process  
The continuous casting process is a technically complex manufacturing process, although at first sight it does not seem so, and it belongs to the category of highly productive processes. Its great advantage is that it can be fully automated. Exactly this possibility of process automation was a research challenge that included the application of artificial intelligence techniques in order to efficiently control the process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Authors, Year**} & \multirow{2}{*}{
\begin{tabular}{c} **Short** \\ **Description** \\ \end{tabular} } & \multicolumn{4}{c}{**Artificial Intelligence Methods**} \\ \cline{3-6}  & & **GA** & **PSO** & **ANN** & **FL** & **ANFIS** \\ \hline [PERSON] et al. (2008) & \multicolumn{4}{c}{Modeling of the high pressure die-casting (HPDC) process.} & \multicolumn{4}{c}{\(\bullet\)} \\ \[PERSON] (2008) & \multicolumn{4}{c}{Optimization of the conditions in pressure die-casting process.} & \(\bullet\) & & & \\ \hline [PERSON] et al. (2009) & \multicolumn{4}{c}{Evaluation system to quantify surface defects of the casting part.} & \(\bullet\) & & & \\ \hline [PERSON] (2011) & \multicolumn{4}{c}{Optimization of the conditions in the high-pressure die-casting (HPDC) process.} & & & & \(\bullet\) \\ \hline [PERSON] and [PERSON] (2012) & \multicolumn{4}{c}{Optimization of the low-pressure die-cast (LPDC) process.} & \(\bullet\) & & \(\bullet\) & \\ \hline [PERSON] et al. (2016) & Modeling of the squeeze casting process. & & & & \(\bullet\) & \\ \hline [PERSON] et al. (2016) & Optimization of the squeeze casting process. & \(\bullet\) & \(\bullet\) & & \\ \hline [PERSON] et al. (2016) & \multicolumn{4}{c}{Modeling the high pressure die-casting (HPDC) process} & & & \(\bullet\) & \\ \hline [PERSON] et al. (2017) & \multicolumn{4}{c}{Modelling and analysis of the squeeze casting process.} & \(\bullet\) & & \(\bullet\) & \\ \hline [PERSON] and [PERSON] (2020) & \multicolumn{4}{c}{Optimization of the squeeze casting process} & & & \(\bullet\) & \\ \hline [PERSON] et al. (2021) & Smart-factory platform for die casting industry & & & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Artificial intelligence methods used for the improvement of the die-casting process. [PERSON] et al. used an artificial neural network for the successful identification and control of a nonlinear heat transfer model in the continuous casting process. Their research goal was to control the slab surface temperature in a closed loop, using an artificial neural network (ANN) and PID controllers. Their study took into consideration the dynamic surface temperature, which was an important parameter for the final slab quality [44]. [PERSON] et al. presented research that dealt with mold level control in the continuous casting process. The mold level variations were a serious productivity and quality problem in the continuous casting process. The authors proposed a level control structure based on Astrom's modified Smith predictor. The tuning parameters were designed through a particle swarm optimization (PSO) approach. The simulation results confirmed the efficiency of the presented architecture [45]. [PERSON] and [PERSON] developed self-organizing fuzzy control (SOFC) to control the molten steel level of a strip casting process. The strip casting process is a steel-strip manufacturing method that combines continuous casting and hot-rolling processes in a single operation. The process is characterized by complex relationships between the process parameters (such as the molten steel level in the tundish, the solidification position and the roll gap), such that it is difficult to establish a control system of a process. The authors presented a solution to this problem as an intelligent controller that can be easily implemented [46]. [PERSON] et al. established a correlation between the continuous casting technological parameters and the cooling rate of the slab for spring steel by using a BP neural network model.