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RISS 인기검색어
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- 저자 : İkbal Eski (검색결과 2 건)
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Vibration analysis of drilling machine using proposed artificial neural network predictors
Đkbal Eski 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.10
Small tolerances are very important factors for drilling machines. Due to the mechanical friction on their moving parts, it is necessary to predict vibration effects. This investigation is focused on design of robust neural network predictors for analyzing vibration effects on moving parts of drilling machines. The research is divided into two parts; the first part is experimental investigation, the second part is simulation analysis with neural networks. Therefore, a real time drilling machine is used for vibrations under working conditions. The measured real vibration parameters are analyzed with neural network. As a result, simulation approaches show that radial basis neural network has superior performance to adapt real time parameters of drilling machines.
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Force analysis of bearing on a modified mechanism using proposed recurrent hybrid neural networks
SAHIN YILDIRIM,İkbal Eski,Menderes Kalkat 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.7
Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distribution on the bearing systems of mechanisms. A proposed neural network was developed and designed to analyze force distribution on the bearings of a four bar mechanism. The proposed neural network has three layers: input layer, output layer and hidden layer. The hidden layer consists of a recurrent structure to keep dynamic memory for later use. The mechanism is an extended version of a four-bar mechanism. Two elements, spring and viscous, are employed to overcome big force problem on the bearings of the mechanism. The results of the proposed neural network give superior performance for analyzing the forces on the bearings of the four-bar mechanism undergoing big forces and high repetitive motion tracking. This continuation of simulation analysis of bearings should be a benefit to bearing designers and researchers of such mechanisms.
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