Yazar "Korkmaz, Habip Gökay" seçeneğine göre listele

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  • Küçük Resim Yok
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    Investigation of material models on deep drawing and ironing processes
    (2022) Kaplan, Cihangir; Güleç, Cem; Arıkoğlu, Mesut; Toros, Serkan; Korkmaz, Habip Gökay
    Within the scope of this study, the effects of yield and hardening criteria used in forming simulations on part geometric dimensions were investigated. As material 0.8 mm thick DC04 material is used. In the study, the results were compared using the Hill-48 and Barlat-91 yield criteria and experimental flow curve, Hockett-Sherby, Ludwig and Hollomon flow curve models. The studies were carried out in Simufact Sheet Metal Form software. Although all the models studied because of dimensional evaluations estimated within tolerance values, the model in which the experimental data were used with Hill-48 gave the closest results to the nominal dimensions.
  • Küçük Resim Yok
    Öğe
    PASLANMAZ ÇELİKLERDE HİDRO-KESME YÖNTEMİNİN SONLU ELEMANLAR YÖNTEMİ İLE İNCELENMESİ
    (2018) Korkmaz, Habip Gökay; Toros, Serkan; Halkacı, Hüseyin Selçuk
    Yapılan bu çalışmada kesme operasyonunun özellikle son yıllarda birçok alanda yaygın olarak kullanılmayabaşlanan hidro şekillendirme prosesine uyarlanabilirliğinin nümerik olarak değerlendirilmesi yapılmıştır.Nümerik çalışmalar kapsamında malzemenin kesilme yüzeyleri değerlendirilmiş ve çapak oluşum durumuzımbanın farklı hareketi için incelenmiştir. Modelleme çalışması kapsamında Johnson-Cook (J-C) pekleşme vehasar modeli kullanılarak, hedeflenen proses parametrelerinde kesme durumunun gerçekleşip gerçekleşmediğibelirlenmeye çalışılmıştır. Sonuç olarak hedef proses parametrelerinde 304 paslanmaz çeliğin bu proses ilekesilebilirliği gösterilmiştir.
  • Küçük Resim Yok
    Öğe
    Prediction of Yoshida Uemori model parameters by the bees algorithm and Genetic Algorithm for 5xxx series aluminium alloys
    (2021) Korkmaz, Habip Gökay; Toros, Serkan; Kalyoncu, Mete
    In sheet metal forming processes, springback is a very important issue in the view of the excellent quality design. Several mathematical models have been developed to estimate the springback more accurately, including various material parameters. In this study, the model parameters of Yoshida-Uemori two surface plasticity model, which can well predict the springback for different loading conditions, have been determined using The Bees Algorithm and Genetic Algorithm which are frequently used recently for optimization of nonlinear problems. In addition, the performances of the algorithms have been determined for the different frequency of experimental data, dense-sparse, sparse-dense, dense-dense and sparse-sparse for elastic and plastic regions. According to the results, although the determined material parameters have different values, the fitting performances are found similar for both The Bees Algorithm and Genetic Algorithm. However, in the view of the data frequency, the more appropriate results are obtained from the dense-dense data set (Case 3).