Yazarlar
- Colak, M.(1)
- Colak, Murat(1)
- Dispinar, Derya(1)
- Erzi, Eray(1)
- Gürsoy, Özen(1)
- Ipek, M.(1)
- Kiraz, A.(1)
- Teke, C.(1)
- Yüksel, Çağlar(1)
Yayınlar
- MakaleAçık Erişim2019Scopus SJRQ2Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network
Teke, C.; Colak, M.; Kiraz, A.; Ipek, M.
Casting simulation programs are 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 out of casting modeling depends on the incorporation of many accurate parameters and boundary conditions. Critical Fraction of Solid (CFS), which is one of the most important of these parameters, is defined as the point where solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies based 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, and 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 are obtained in this validation range of 33% to 61%. These CFS values are used in the development of ANN models. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Square Error (MSE) are used to assess the prediction accuracy of the ANN models. Calculated values of MAE, MAPE, and MSE are 0.0188, 7.06%, and 0.0006, respectively. The results show that the proposed ANN model predicts a CFS value with high accuracy.
AtıfScopus6WoS6OpenAlex4
İncele - MakaleAçık Erişim2019Scopus SJRQ2Determination of acceptable quality limit for casting of a356 aluminium alloy: Supplier’s quality index (SQI)
Erzi, Eray; Gürsoy, Özen; Yüksel, Çağlar; Colak, Murat; Dispinar, Derya
Aluminium and its alloys have been widely used in the automotive industry for some time and A356 is one of the most popular aluminium alloys today in the sector. It contains approximately 7 wt.% Si and 0.3 wt.% Mg. Due to the defects that may be present in the cast parts, expected mechanical properties may not be reached and this alloy may perform in service under its potential. In a low pressure die casting, several precautions have to be taken in order to produce high quality and defect-free castings. Temperature of casting and the schemical composition of the melt is recorded continuously. Die temperature, pressure levels and their durations are optimized; degassing and fluxing operations are performed. Yet, regardless of the precautions, there could still be rejected parts. It is becoming clear that a good quality raw material is one of the most underrated points of the industry and that the starting material has a great effect on the final product and it should be taken in to account more seriously. Therefore, regarding the first rule of Campbell’s 10 Rules of Casting, in this study, the quality of the starting material of supply chains for a wheel manufacturer was investigated. Chemical compositions were compared, fluidity tests, mechanical tests and bifilm indexes of these various sources and ingots were measured and a final quality index was proposed to create a reference for the quantification of quality of supplier’s ingots. This new index was compared with the rejection rates.
AtıfScopus40WoS33OpenAlex39İncele