# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review
### Improvement of the Sand Casting Process
[PERSON] et al. established a correlation between the characteristics of cement-bonded sand molds and process parameters by using feed-forward neural networks trained with the help of a back-propagation algorithm and a genetic algorithm, separately. The mold characteristics, which were the subject of the prediction, were the compression strength and hardness, and the process parameters were the percentages of cement, accelerator and of water, and the testing time. In order to construct a neural network model, a set of 1000 data was used, which were previously obtained based on conventional statistical regression equations derived earlier by the authors. The developed neural networks demonstrated a high performance in the prediction of mold characteristics, in particular a genetic algorithm-trained neural network [24]. [PERSON] et al. dealt with a similar problem of the mold system using a genetic fuzzy system. A binary-coded genetic algorithm (GA) was used for the optimization of the knowledge base and the rule base. This approach has shown good accuracy in prediction [25]. The proper and complete filling of the mold is the desired result of the sand casting process. The role of casting system design--which consists of a gating system, a feeding system and process parameters--is very significant in achieving this goal. The improper design of the casting system leads to numerous defects in the casting process, resulting in the low quality of the casting part. Casting system design is a complex task that requires expert knowledge of the process. This applied knowledge is often the result of numerous implemented optimizations, modeling, and simulation processes. Artificial intelligence techniques have made a significant contribution to the improvement of the design of casting systems. [PERSON] et al. presented a multi-objective optimization problem in the gravity sand casting process. In their study, the authors emphasized a combined approach of software for the casting process simulation and a genetic algorithm (GA) as optimization techniques. The optimization goal was to minimize the top feeder volume, minimize the shrinkage porosity, and limit the centerline porosity, utilizing an optimized arrangement of the chills [26]. [PERSON] et al. conducted research in which they elaborated on an efficient method to reduce the casting defects and upgrade the casting quality. As they intended to minimize the parameters such as the solidification time, filling time, and oxide ratio, the optimized values used were the pouring temperatures, riser diameter, pouring speed, riser position, and pouring diameter. The QPSO algorithm (an improved PSO algorithm) was utilized as an optimization technique, and it provided the reduction of the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively [27]. [PERSON] et al. presented a genetic optimization of the feeder geometry. The optimization process setting (the creation of the fitness function and the appropriate constraints) were based on respecting some rules for the feeding casting part proposed by [PERSON] [28; 29]. With the main objective to minimize the casting process time, [PERSON] et al. developed a methodology of optimization for the gating system for sand casting using the genetic algorithm (GA). The optimization focus was on the geometry of the gating system, i.e., the cross-section of the ingate and the casting height. In order to verify the validity of the optimized geometry of the gating system, software MAGMA5 was utilized for a numerical simulation [30]. Furthermore, [PERSON] et al. presented a laboratory setting that promoted fuzzy and neuro-fuzzy intelligent systems for the automatic control of mold filling employed in casting plants. The concept of precision mold filling presupposed three key points in the process, i.e., the precise pouring of the stream into the basin, the maintenance of a constant level of molten metal in the basin, and finally the elimination of the overflow of molten metal from the mold [31].