Yazarlar

• Colak, M.(1)
• Colak, Murat(1)
• Coşar, Mehmet(1)
• Güngör, Kadir(1)
• Ipek, M.(1)
• Kiraz, A.(1)
• Teke, C.(1)
• Teke, Çağatay(1)
• Uslu, Emin(1)
• Yilmaz, Galip(1)
• Çolak, Murat(1)
• Şüküroğlu, Ebru Emine(1)

• MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/10920
  Experimental and Modeling Study of Fluidity in Ductile Iron (GGG40)

  Coşar, Mehmet; Şüküroğlu, Ebru Emine; Güngör, Kadir; Çolak, Murat

  Ductile irons (spheroidal graphite cast irons, SGI) are widely used in automotive and energy systems due to their high strength, ductility, and excellent machinability. The casting quality of these alloys largely depends on the flow and filling behavior of the molten metal, namely its fluidity. In this study, the effects of pouring temperature, filling time, and metallurgical quality on the fluidity of GGG40 ductile iron were investigated through both experimental and numerical approaches. Experimental trials were conducted at pouring temperatures of 1350 °C, 1400 °C, and 1450 °C; metallurgical quality levels of 10%, 50%, and 90%; and filling times of 5 s and 10 s. The fluidity lengths were measured and statistically evaluated. The results revealed that increasing the pouring temperature and metallurgical quality significantly improved fluidity, while shorter filling times allowed the molten metal to travel longer distances before solidification. Modeling studies performed using a commercial casting simulation software showed strong correlation with the experimental data. Microstructural examinations indicated that higher pouring temperatures and improved metallurgical quality led to a more homogeneous distribution of graphite nodules and an increased ferrite fraction in the matrix. The findings provide valuable insights into the influence of process parameters on the fluidity behavior of ductile iron and offer critical design data for enhancing casting quality and productivity in industrial foundry applications.

  AtıfScopus1

  İncele
• MakaleYayıncıda açık erişim2023Scopus SJRQ3/r/bayburt/12923
  Solidification analysis for variable thickness aluminum castings: simulation and chill design insights

  Yilmaz, Galip; Colak, Murat; Uslu, Emin

  Manufacturing high-quality casting parts with complex geometries requires high engineering skill and precision. One essential quality concern is isolated hot spots within the castings, often in thick sections. Each hot spot must be consistently fed or mitigated through directional solidification techniques. The impact of various mold sands and the geometry of chill parts on solidification direction was investigated using specialized casting and general-purpose simulation programs. A parametric optimization method was employed to analyze directional solidification to adjust the geometry of the chill parts. The results indicate that employing diverse mold sands to enhance cooling in the thick sections was a viable strategy for achieving directional solidification in parts where the feeding pathway is obstructed due to changes in cross-section. Furthermore, the study revealed that intricate details in the chill part’s geometry are not critical; however, a minimum volume (or weight) was necessary for adequate directional solidification. Lastly, an easily applicable mathematical model has been developed to determine the required volume of chill parts to ensure successful directional solidification.

  AtıfScopus1·WoS1·OpenAlex1

  İncele
• MakaleAçık Erişim2022Scopus SJRQ2/r/bayburt/13395
  Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach

  Teke, Çağatay

  Ductile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R2) value of 0.986 confirms the high prediction performance of the model.

  AtıfScopus4·WoS4

  İncele
• MakaleAçık Erişim2019Scopus SJRQ2/r/bayburt/13561
  Prediction 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.

  AtıfScopus6·WoS6·OpenAlex4

  İncele