Yazar "Ipek, M." seçeneğine göre listele

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    Modeling of the Impact of Initial Mold Temperature, Al5Ti1B and Al10Sr Additions on the Critical Fraction of Solid in Die Casting of Aluminum Alloys using Fuzzy Expert System
    (Polish Acad Sciences Inst Physics, 2019) Teke, C.; Colak, M.; Tas, M.; Ipek, M.
    In the casting of liquid metal, the feeding stops when the mushy zone is clogged and does not allow the transfer of feeding liquid. The growing resistance of the solid dendrites against the fluidity of the feeding liquid is defined as the critical fraction of solid (CFS). CFS value varies depending on many factors such as alloy solidification range, initial mold temperature, and the grain size. Therefore, in many casting simulation applications, it is quite common to get inconsistent results due to insufficient information about the CFS. In this study, a fuzzy expert system (FES) model has been developed in order to determine the value of the CFS in the die casting process, based on the parameters of the alloy type, the initial mold temperature, Al5Ti1B addition and Al10Sr addition. In order to create the rule base for the FES model, 54 die casting experiments have been carried out. The CFS values obtained using the FES model has revealed that the developed model of the FES predicts the CFS value in a high performance.
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    Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network
    (Sharif Univ Technology, 2019) 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. (C) 2019 Sharif University of Technology. All rights reserved.