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Dergiler / Mathematical and Computational Applications / 2004 / Cilt: 9 - Sayı: 3

Determination of friction coefficient at journal bearings by experimental and by means of artificial neural networks method

Bekir Sadık ÜNLÜ
Kaçar Hülya DURMUŞ
Cevdet MERİÇ
Enver ATİK
Sayfa
399–408
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In experiments, effects of bearings have been examined at dry and lubricated conditions and at different loads and velocities.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Knowing friction coefficient is important for determination of wear loss conditions at journal bearings.
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Temel Bulgular & Veri:In this study, friction coefficient at CuSnl0 Bronze radial bearings has been determined by a new approach as experimental and artificial neural networks method.
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Sonuç & Katkı:In experiments, effects of bearings have been examined at dry and lubricated conditions and at different loads and velocities.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Knowing friction coefficient is important for determination of wear loss conditions at journal bearings. Tribological events that influence wear and its variations affect experimental results. In this study, friction coefficient at CuSnl0 Bronze radial bearings has been determined by a new approach as experimental and artificial neural networks method. In experiments, effects of bearings have been examined at dry and lubricated conditions and at different loads and velocities.

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Bu Makaleye Atıf Yap

ÜNLÜ, B. S., DURMUŞ, K. H., MERİÇ, C., ATİK, E. (2004). Determination of friction coefficient at journal bearings by experimental and by means of artificial neural networks method. Mathematical and Computational Applications, Cilt: 9 - Sayı: 3, 399–408. https://ajindex.com/articles/280464

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Kaynakça (2)

  1. I.M. Hutchings, Tribology: Friction and Wear of Engineering Materials, 1992,
  2. Arnold, London. 2.B. Bhushan, Principles and Applications of Tribology, John Willey&Songs, Inc., Newyork, 1999. 3.G. Amontons, De La Resistance Caus'ee Dans Les Machines, Mem. Facad. Roy. A;257-282, 1699. 4.P.J. Blau, The Significance and Use of The Friction Coefficient, Tribology International, 34 (2001), Pp: 585-591. 5.G. Karadere, Effects to Friction Coefficient Performance of Characteristic at Sintered Bronze Journal Bearing, Journal of Engineering Sciences, 7/1, Pp: 11-16, 2001, Denizli/Turkey. 6.E. Durak, H.İ. Koruca, C. Kurbanoğlu, Additional With Additive Decreasesof Friction at Journal Bearing, Symposium of Mechanical Engineering, 721-728, 1999, Denizli/Turkey. 7.D. Coupard, M.C. Castro, J. Coleto, A. Garcia, J, Goni, J.K. Palacios, Wear Behaviour of Copper Matrix Composites, Key Engineering Materials, Vols. 127-131 (1997), Pp: 1009-1016. 8.T. Malinova, S. Malinov, U.N. Pantev, Simulation of Microhardness Profiles for Niirocarburized Surface Layers By Artificial Neural Network, Surface And Coatings Technology, 135, 2001, 258-267. 9.E. Özkaya, M. Pakdemirli, 1999, Non-Linear Vibrations of a Beam-Mass System With Both Ends Clamped, Journal Of Sound And Vibration, 221(3):491-503). 10.E. Atik, C. Meriç, B. Karlık, Determination of Yield Strength of 2014 Aluminum Alloy Under Ageing Conditions by Means of Artificial Neural Networks Method, Mathematical & Computational Applications, 1,2,16-20,1996. 11.C. Meriç, N.S. Koksal, B. Karlık, An Investigation of Deep Drawing of Low Carbon Steel Sheets and Applications in Artificial Neural Networks Method, Mathematical & Computational Applications, 213, 119-125, 1997.