Sharif University of Technology Scientia Iranica 1026-3098 26 6 2019 12 01 Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network 3304 3312 21225 10.24200/sci.2019.50819.1881 EN C. Teke Institute of Natural Sciences, Sakarya University, Sakarya, Turkey M. Colak Department of Mechanical Engineering, Bayburt University, 69000, Bayburt, Turkey. A. Kiraz Department of Industrial Engineering, Sakarya University, 54187, Sakarya, Turkey. M. Ipek Department of Industrial Engineering, Sakarya University, 54187, Sakarya, Turkey. Journal Article 2018 04 17 Casting simulation programs are the computer programs that digitally model the casting of an alloy in the sand, shell or permanent mold and then the cooling and solidification processes. However, obtaining consistent results from the casting modeling depends on providing many parameters and boundary conditions accurately. Critical fraction of solid (CFS), which is one of the most important of these parameters, is defined as the point where the solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies depending on many factors, inconsistent results can be experienced in the modeling applications. In this study, the CFS value obtained during the solidification of various commercial aluminum alloys' casting process carried out using low pressure die casting method, is predicted by using artificial neural network (ANN) method based on alloy type, grain refiner and modifier additions, initial mold temperature, pressure level parameters. In the scope of the study, 162 experiments are conducted. The results obtained from the low pressure die casting experiments using a special model designed for the study are validated by using SOLIDCast casting simulation. The CFS values obtained from this validation range from 33% to 61%. https://scientiairanica.sharif.edu/article_21225_61e269adf2f6609b0ad620d06d9d1e4c.pdf