Yazar "Mustu, Mustafa" seçeneğine göre listele

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    An Investigation of Mechanical and Wear Performance of TiB2/GNPs-Reinforced ZK60 Mg Matrix Composites Fabricated Via Powder Metallurgy
    (Springer, 2023) Mustu, Mustafa; Demir, Bilge; Aydin, Fatih
    In this study, ZK60 alloy, ZK60/15 wt.% TiB2, and ZK60/15 wt.% TiB2 - 0.5 GNPs composites were prepared by hot pressing. The density, microstructure, hardness, and tribological and compression behavior of the samples were investigated. Microstructure investigations showed that TiB2 particles were generally homogeneously dispersed, but GNPs particles were agglomerated. Under 10 and 40 N loads, the best wear performance was obtained with 15 wt.% TiB2 - 0.5 GNPs and ZK60/15 wt.% TiB2 samples, respectively. Abrasion at low and delamination at high loads were observed as the dominant wear mechanism. According to the mechanical test results, the highest hardness (87.1 HV), compressive yield strength (290.1 MPa) and ultimate compressive strength (379.2 MPa) were obtained for the ZK60/15 wt.% TiB2 composite. Fracture surface investigations showed that the mechanism was cleavage fracture and the presence of crack formations on the surface.
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    An investigation of the PMEDM processing and surface characterizations of AZ61 matrix composites via experimental and optimization methods
    (Elsevier Science Sa, 2023) Mustu, Mustafa; Demir, Bilge; Aydin, Fatih; Gurun, Hakan
    In this study, AZ61/15 wt%TiB2 and AZ61/15 wt%TiB2-0.5 wt%GNPs composites were manufactured by hot pressing, and the machinability of the produced samples was carried out by powder mixed electrical discharge machining (PMEDM). The influence of PMEDM parameters, namely pulse on time, current and materials, were studied by surface roughness (SR) and material removal rate (MRR) using the Taguchi design. The microstructure and surface quality of the machined surfaces and cross-sections were investigated using 3D microscopy, scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and X-ray diffraction (XRD). Results showed that PMEDM produced a melting, white layer and transition zone direct proportionally to the processing parameters. The white layer did not differ from the metal integrity of the transition zone. Additionally, volcanic craters, holes, cracks, debris covered with molten metal, and reinforcement particles were observed at the machined surface and cross-section. A high amount of oxygen was detected on the machined surface as a result of the interaction between kerosene and generated heat changing proportionally to the amount of the EDM parameters. The analysis of variance (ANOVA) showed that the pulse on time and materials, with 65.46% and 40.86%, were the most significant parameters on the SR and MRR, respectively. For regression models, the determination coefficient (R2) for the prediction of SR and MRR was noted to be 0.98 and 0.85, respectively.
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    Prediction of wear performance of ZK60/CeO2 composites using machine learning models
    (Elsevier Sci Ltd, 2023) Aydin, Fatih; Durgut, Rafet; Mustu, Mustafa; Demir, Bilge
    In this study, ZK60 magnesium matrix composites were produced with different content of CeO2 (0.25, 0.5 and 1 wt%) by hot pressing. The wear behaviour of the samples was investigated under loads of 5 N, 10 N, 20 N and 30 N, at sliding speeds of 75 mm/s, 110 mm/s and 145 mm/s. The worn surfaces, wear debris, and counterface material was analysed to reveal the wear mechanisms. Five machine learning algorithms were established to compare their prediction abilities of wear behaviour on a limited dataset measured under different test operations. The hyperparameter tuning phase of each model was conducted to provide a fair comparison. The prediction results were examined under various statistical measures. In the light of prediction results, the superior model was determined.