Stelit Kaplamalı Valf Yüzeylerinin Oda Sıcaklığında ve 300°C'de Adhesif Aşınma Davranışının Bulanık Mantık Yöntemi İle Analizi

Year 2024, Volume: 27 Issue: 1, 419 - 427, 29.02.2024

Uğur Arabacı

Abstract

Çalışmam kapsamında, özellikle otomotiv valflinin üretiminde kullanılan çelik (1.4718) yüzeyi, Nikel 60 ve kobalt esaslı Stellite 1, Stellite 6, Stellite F alaşımları ile TIG kaynak yöntemiyle kaplanmıştır. Belirtilen kaynak parametreleri ile kaplanan numuneler, 4400 metre boyunca metal aşındırıcı disk üzerinde oda sıcaklığında ve 300 0C'de 10, 25 ve 40 N yükler altında aşındırılmıştır. Her 1100 metrede ağırlık kayıpları ölçülerek numunelerin aşınma dirençleri belirlenmeye çalışılmıştır. Deneysel çalışma sonuçları ile bulanık mantık tahminlerinin birbiriyle örtüştüğü, test edilmeyen ara değerlerde aşınma davranışının belirlenmesinde bulanık mantık tahminlerinin kullanılabileceği belirlenmiştir.

Keywords

Bulanık mantık, valf kaplama, adhezif aşınma, TIG kaynağı, stelit kaplama

Analysis Of Adhesive Wear Behavior Of Stellite-Coated Valve Surfaces At Room Temperature and 300°C By Fuzzy Logic Method

Abstract

Within the scope of my study, the surface of steel (1.4718), especially used in the production of automotive valves, has been coated with Nickel 60 and cobalt-based Stellite 1, Stellite 6, Stellite F alloys using the TIG welding method. The samples coated with the specified welding parameters were subjected to abrasion tests on a metal abrasive disk for 4400 meters at room temperature and 300°C under 10, 25, and 40 N loads. Weight losses were measured at every 1100 meters to determine the wear resistance of the samples. The experimental results aligned with fuzzy logic predictions, indicating that fuzzy logic predictions could be used to determine wear behavior in untested intermediate values.

Keywords

Fuzzy logic, valve coating, adhesive wear, TIG welding, stellite coating

References

• [1] Wong, S.V., at all,.”Generalized Fuzzy Model For Metal Cutting Data Selection”, Journal of Materials Processing Technology, 50-99, 310-317. (1999).
• [2] Babuska, R., “Fuzzy Modeling for Control. Kluwer” Academic Publishers, 257. (1998).
• [3] Passino, K.M., Yurkovich, S., “Fuzzy Control”, Addison Wesley Longman, Inc., 468. (1998).
• [4] Allahverdi, N., Uzman Sistemler, “Bir Yapay Zeka Uygulaması”, Atlas Yayın Dağ., 248. (2002).
• [5] Klir, G.J., Yuan, B., “Fuzzy Sets and Fuzzy Logic : Theory and Applications”, Prentice, Hall PTR, 574 (1995).
• [6] Yalçın B., Varol R., Yılmaz N., “Demir Esaslı Toz Metal (T/M) Yatakların Aşınma Özelliklerinin Bulanık Mantıkla (BM) Modellenmesi”, Makine Teknolojileri Elektronik Dergisi, (4) 1-8.(2004).
• [7] Tavoosi, J., Zhang, C., Mohammadzadeh, A., Mobayen, S., Mosavi, A.H. “Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network. Front”. Neuroinform. 15. (2021)
• [8] Tavoosi, J, Suratgar A. A, Menhaj M.B., Mosavi A., Mohammadzadeh A, Ranjbar E., “Modeling Renewable Energy Systems By A Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System For Power Prediction”. Sustainability. 13. (2021).
• [9] Sadeghiravesh, M.H., Khosravi, H., Abolhasani, A., Ghodsi, M., Mosavi, A. “Fuzzy Logic Model to Assess Desertification Intensity Based on Vulnerability Indices.” Acta Polytech. Hung 18, 7–24. (2021).
• [10] Claywell, R., Nadai L., Felde I., Ardabili S., Mosavi A. “Adaptive Neuro-Fuzzy Inference System And A Multilayer Perceptron Model Trained With Grey Wolf Optimizer For Predicting Solar Diffuse Fraction”. Entropy 22. 1192. (2020).
• [11] Jantzen, J. “Foundations of Fuzzy Control”, Hoboken, USA (2007).
• [12] Khuntia, S.R., Mohanty K., Panda S., Ardil C. “A. Comparative Study of PI, IP, Fuzzy, and Neuro-Fuzzy Controllers for Speed Control of DC Motor Drive.” World Academy of Science and Technology. Paris France, Volume 68. (2009).
• [13] Thaker, S., Nagori V. “Analysis Of Fuzzification Process In Fuzzy Expert System”. Procedia Comput. Sci. 132. 1308–1316. (2018).
• [14] Lashin M. M. A., Al Samhan A. M., Badwelan, “A., Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength”. Coatings. 12 (10), 1442.(2022).
• [15] P.M. Rudenko, V.S. Gavrish, S.I. Kuchuk-Yatsenko, A.V. Didkovsky and E.V. Antipin. “Influence Of Flash Butt Welding Process Parameters On Strength Characteristics Of Railway Rail Butts”. Avtomaticheskaya Svarka (Automatic Welding), 87-90. (2017).
• [16] Kuchuk-Yatsenko S.I., Rudenko P.M., Gavrish V.S., Didkovsky О.V., Antipin, Ye.V., Ziakhor I.V. “Operational Control as a Means of the Evaluation of Quality of Welded Connections for Flash-Butt Welding of Modern High- Strength Steels”. Science and innovation. 16. (2020).
• [17] S.I. Kuchuk-Yatsenko, P.M. Rudenko, V.S. Gavrısh, A.V. Dıdkovsky And E.V. Antıpın. “Statıstıcal Control OF Process OF Flash-Butt Weldıng Of Raıls. Two-Level Control System.” The Paton Welding Journal, 5-6. (2016).
• [18] S.I. Kuchuk-Yatsenko, P.M. Rudenko, V.S. Gavrysh, A.V. Didkovsky, V.I. Shvets, E.V. Antipin, P. Wojtas, A. Kozłowski.. “Real-Time Operational Control In Information Management System For Flash-Butt Welding Of Rails. Mınıng – Informatıcs,” Automatıon And Electrıcal Engıneerıng No. 1-529. (2017).
• [19] Alghannam, Lu, Ma, Cheng, Gonzalez, Zang, & Li. A. “Novel Method of Using Vision System and Fuzzy Logic for Quality Estimating Resistance Spot Welding”. Symmetry. 11(8), 990. (2019).
• [20] Na S. J., Kim J. W. “A self-organizing fuzzy control approach to arc sensor for weld joint tracking in gas metal arc welding of butt joints” 72:2. Welding Journal. United States. (1993).
• [21] S. Nweze, J. Achebo. “The Use of Fuzzy Logic in Predicting Percentage (%) Dilution of Weld during Tig Welding Process”. Materials Sciences and Applications. Vol.10, 406-422. (2019).
• [22] BAL, G., OZTURK, N., ÖNCÜ, S., ÜNAL, K. “Otomatik Gerilim Regülatörü İçin Hibrit Bir Denetleyici Tasarımı”. Politeknik Dergisi, 199-207. 26(1), (2023).
• [23] KOCAKULAK, T., SOLMAZ, H., & ŞAHİN, F. “Control and Optimization of Pre-Transmission Parallel Hybrid Vehicle with Fuzzy Logic Method and Comparison with Conventional Rule Based Control Strategy.” Politeknik Dergisi, 1035-1047. 26(3), (2023).
• [24] BULUT, M. “Bulanık Ters Model Kullanılarak Doğru Akım Motor Sürücüsü için Referans Model Temelli Uyarlanabilir Bulanık Denetleyici”. Politeknik Dergisi, 26(2), 593-602. (2023).