# Casting Process Improvement by the Application of Artificial Intelligence
## 4 Conclusions  
This paper provides a detailed overview of the application of five artificial intelligence techniques (artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), fuzzy logic (FL) and the adaptive network-based fuzzy inference system (ANFIS)) at the improvement of four casting technologies (sand casting, pressure die casting, continuous casting and investment casting). The application of artificial intelligence techniques has a diverse nature, depending on the specificity of the casting technology. In the improvement of the sand casting process, most studies deal with the modeling and optimization of the design of the green sand mold system, the geometry of the gating system, the feeders, and the process parameters. The most dominant applied artificial intelligence technique is the genetic algorithm (GA), which was used in 63% of the analyzed studies. The artificial intelligence techniques applied to improve the pressure die casting process, mainly through modeling and optimization, established the relationship between the process parameters and the characteristics of the casting part, in order to obtain a high-quality casting part. The most frequent was the application of artificial neural networks, with 72% participation in the analyzed studies. In the continuous casting process in the analyzed studies, artificial intelligence techniques were mainly used in the improvement control process. The most widespread artificial intelligence techniques in these studies were artificial neural networks and fuzzy logic, with 40% participation each in the analyzed studies. The studies related to the improvement of the investment casting process presented the application of artificial intelligence techniques through modeling and optimization, in order to establish a correlation between the process parameters and the characteristics which reflect the quality of the casting part. The most dominant applied \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Authors, Year**} & \multicolumn{2}{c}{**Short**} & \multicolumn{4}{c}{**Artificial Intelligence Methods**} \\ \cline{2-6}  & **Description** & **GA** & **PSO** & **ANN** & **FL** & **ANFIS** \\ \hline [PERSON] et al. (2009) & Intelligent precisely planning of the investment casting process. & & & & \\ \hline [PERSON] et al. (2012) & Optimization of the process parameters in the investment casting process. & & & & \\ \hline [PERSON] and [PERSON] (2014) & Modeling of the investment casting process. & & & & \\ \hline [PERSON] and [PERSON] (2015) & Optimization of the process parameters in the investment casting process. & & & & \\ \hline [PERSON] (2016) & Modeling of the investment casting process. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Artificial intelligence methods used to improve the investment casting process. artificial intelligence technique was the artificial neural network (ANN), which was used in 60% of the analyzed studies. In general, a representation of each artificial intelligence technique separately in all analyzed studies is as follows: ANN--51.3%, GA--37.8%, FL--18.9%, PSO--16.2%, and ANFIS--5.4%. All of the analyzed studies gave a clear overview of the possibility of the application of artificial intelligence techniques in the improvement of the casting process. In addition to the aforementioned clear review of possibilities, the analyzed studies provide a basis for new ideas for researchers and engineers. The results of the conducted research have shown the great potential for the application of artificial intelligence as an integral part of the concept called Industry 4.0 in the development of digitized smart foundries. Conceptualization: N.D. and S.M. ; Methodology: N.D. and Z.C. ; Resources: R.R. and A.J. ; Formal Analysis: R.R. and A.J. ; Validation: S.M. and Z.C. ; Writing--Original Draft Preparation: A.J. ; Writing--Review and Editing: N.D. All authors have read and agreed to the published version of the manuscript.