Yazar "Albayrak, Muhammet Gokhan" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    A Different Approach: Effect of Mechanical Alloying on Pack Boronizing
    (Springer, 2024) Albayrak, Muhammet Gokhan; Evin, Ertan
    Surface coating processes are carried out at high temperatures, so high heat input is applied to the material to be coated and may cause internal structure deterioration. In order to overcome this situation, the ability to reduce the coating temperature with a pretreatment such as Mechanical Activation was investigated in this study. In order to minimize the effect of alloying elements, DIN St28 steel is used. Powders containing B+SiC+KBF4 were mechanically alloyed by planetary ball milling devices to 10 and 20 h, respectively. By mixing boron-containing powder and sodium silicate, the samples were boronized at 923-1173 K temperature and 3-12 h. At the end of the mechanical alloying process, it was determined that the powder particle sizes were in the nanometer scale. According to the microstructure analysis, a single-layer Fe2B structure was successfully obtained on the samples surfaces. While no boride layers were formed on the sample surfaces at temperatures below 1023 K without MA pretreatment, boride layers were formed under these temperatures with MA pre-treatment. It has been observed that the depth of the Fe2B boride layer, which has achieved high diffusivity by creating many defects in the form of nanometer-sized crystal particles, increased with repeated fracture and cold welding of the powder particles with increasing mechanical alloying times. By calculating the activation energies of the powders, their relations with the mechanically unalloyed samples were compared and empirical formulas that could be used for similar experimental conditions were produced. The highest microhardness value was measured as 2200 HV and above.
  • Küçük Resim Yok
    Öğe
    Design and Characterization of Y2O3/Pr2O3-Enriched Inconel-718 Alloys for Nuclear Applications
    (Taylor & Francis Inc, 2026) Albayrak, Muhammet Gokhan; Guler, Omer; Guler, Seval Hale; Evin, Ertan; Almisned, Ghada; Sen Baykal, Duygu; Tekin, Huseyin Ozan
    This study explores the physical, structural, and radiation shielding enhancements in Inconel 718 superalloys reinforced with 1 wt% Y2O3 and varying Pr2O3 contents from 0 to 10 wt%, respectively. X-ray diffraction analysis confirmed preservation of the face-centered cubic structure, with increasing Pr2O3 inducing peak broadening and partial amorphization. The scanning electron microscopy/energy-dispersive X-ray spectroscopy results verified the homogeneous dispersion of oxides without agglomeration. Gamma-ray shielding parameters, including the mass attenuation coefficient (MAC), half-value layer, and effective atomic number, significantly improved with higher Pr2O3 content, particularly at low to mid photon energies.The 718Y-10PO sample exhibited the lowest transmission factors and the highest MAC values across all the tested energies. Buildup factors decreased in the Pr-rich samples, confirming reduced photon scattering. Notably, the fast neutron removal cross section for 718Y-10PO as 0.15521 cm-1 exceeded the benchmark materials like graphite and B4C. These findings establish the 718Y-10PO alloy as a promising candidate for advanced nuclear shielding applications, combining structural integrity with promising gamma and neutron attenuation capabilities.
  • Küçük Resim Yok
    Öğe
    Experimental and artificial intelligence approaches to measuring the wear behavior of DIN St28 steel boronized by the box boronizing method using a mechanically alloyed powder source
    (Pergamon-Elsevier Science Ltd, 2023) Albayrak, Muhammet Gokhan; Evin, Ertan; Yigit, Oktay; Togacar, Mesut; Ergen, Burhan
    Wear in moving materials in contact with each other is an inevitable cause of damage. To prevent this damage, various processes are applied to the material surfaces. The most widely used method is the surface hardening method. This study aims to examine the wear properties of the samples by forming a hard boride layer on the surface of low-carbon steel such as St28 with experimental and artificial intelligence approaches. In this context, it is aimed to obtain the boride layer at relatively low temperatures by pre-processing the powder mixture to be used as a boron source, such as Mechanical Alloying (MA). The boronizing process was carried out using the box boronizing technique. The wear behavior of the obtained samples was investigated by the block-on-disk method. In artificial intelligence approaches; The dataset is divided into three categories as 10N, 20N, and 40N. There are 39 sample types and attributes in each category. In this study, feature selection algorithms such as linear regression (LR), ridge, recursive feature elimination (RFE), f-regression, and multiple inclusion criterion (MIC) were used to select the most efficient samples. Then the best samples were classified according to their force types. Ensemble learning methods, machine learning methods, and Bayesian neural networks were used in the classification processes. Thanks to the proposed approach and feature selection algorithm, the best performance has been shown up to 10 feature selection. By ignoring 29 inefficient features, classification was performed with 10 efficient features. In the classification process, 100% overall accuracy was achieved.