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Decision of Lubricated Friction Conditions for Materials of Automobile Transmission Gear Using Neural Network  

Cho Yon-Sang (Department of Mechanical Engineering, Dong-A University)
Park Heung-Sik (Department of Mechanical Engineering, Dong-A University)
Publication Information
Journal of Mechanical Science and Technology / v.20, no.5, 2006 , pp. 583-590 More about this Journal
Abstract
It is hard to inspect the state of lubrication of an automobile transmission visually. Thus, it is necessary to develop a new inspection method. Wear debris can be collected from the lubricants of an operating transmission of an automobile, and its morphology will be directly related to the friction condition of the interacting materials from which the wear debris originated in the lubricated transmission. In this study, wear debris in lubricating oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative values of shape parameters of wear debris are calculated by digital image processing. These shape parameters are studied and identified by an artificial neural network algorithm. The results of the study may be applicable to the prediction and diagnosis of the operating condition of transmission gear.
Keywords
Transmission Gear; Wear Debris; Image Processing; Diagnosis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 0
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