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- MakaleAçık Erişim2021Scopus SJRQ4Electrochemical Corrosion and Metal Ion Release Protective Efficiency of the Multilayer TaN Coatings on CoCrMo Biomedical Alloy
Aslan, Ali Kemal; Bahçe, Erkan; Güler, Mehmet Sami
CoCrMo alloy is widely used in artificial orthopedic joint applications due to its high mechanical properties. But, high levels of the metal ion release from alloy surfaces cause variable toxic and allergic effects on the patient in vivo use. This case restricts the lifetime of the implant and results in the failure of the artificial joint. In order to enhance the corrosion resistance and to decrease the metal ion release, alloy surface was multilayer-coated by closed field unbalanced magnetron system. The protective efficiency of the coatings was determined by potentiodynamic polarization tests and static immersion tests were performed for 45, 60 and 90 days in the simulated body fluid in order to understand the effect of the coatings on the metal ion release. The metal ion concentrations in the simulated body fluids were determined by using inductively coupled plasma mass spectrometry device. The electrochemical corrosion results showed that the multilayer coatings provided 93 % protective efficiency in terms of corrosion resistance. Multilayer coatings decreased the metal ions release levels significantly, especially Co leaking was decreased approximately 52 times compared to uncoated samples.
AtıfScopus2WoS3OpenAlex1
İncele - MakaleAçık Erişim2018Scopus SJRQ4The effect of hygrothermal aging on the glass and carbon reinforced epoxy composites for different stacking sequences
Dogan, Akar; Arman, Yusuf
The present paper examines the effects of hygrothermal aging on the reinforced epoxy composites. The roles of reinforcement types and stacking sequence on the mechanical properties of composites under hygrothermal aging are investigated. Four different composite materials with different stacking sequence were produced. Unidirectional E-glass and carbon fabrics with an areal density 300 gr/m2 as reinforcement and epoxy as matrix were used. Composites were manufactured using vacuum-assisted resin infusion molding. In order to determine the mechanical properties of the composites, specimens were prepared according to the ASTM standards. The aging process was carried out in climatic test cabin at a temperature of 90oC and a humidity of 90%. As a result of the tests, while the tensile strength of the glass reinforced composites decreased with aging time, no significant change was observed for carbon and hybrid composites. On the other hand, the tensile strength decreased in the transverse direction for all types of composites. XPS analysis was used to understand the distribution of atoms on the surface of specimens.
AtıfScopus22WoS19OpenAlex23İncele - MakaleAçık Erişim2018Scopus SJRQ3Recognition of road type and quality for advanced driver assistance systems with deep learning
Tumen, Vedat; Yildirim, Ozal; Ergen, Burhan
To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road–smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.
AtıfScopus22WoS16OpenAlex25İncele - DergiMechanika
- DergiElektronika ir Elektrotechnika
- DergiMedziagotyra