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| Prediction of wear in mechanism with revolute joint clearances based on neural network |
| Received:April 25, 2014 Revised:August 24, 2014 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx201504006 |
| KeyWord:BP neural network clearance contact pressure wear prediction bushing |
| Author | Institution |
| 邓培生 |
西安理工大学 机械与精密仪器工程学院, 西安 |
| 原大宁 |
西安理工大学 机械与精密仪器工程学院, 西安 |
| 刘宏昭 |
西安理工大学 机械与精密仪器工程学院, 西安 |
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| Abstract: |
| A nonlinear wear model was presented for studying and quantifying the wear phenomenon in revolute clearance joints.The model of relationship between wear rate and contact pressure,sliding velo-city and material hardness was established based on BP neural network.The test results have indicated that the model can accurately reflect the inherent wear law of the samples,and has a higher prediction precision.In the process,a simple slider-crank mechanism with a clearance in revolute joints was utilized.The evaluation of the contact forces developed was based on the elastic-damping contact force model and modified Coulomb friction model.The contact pressure and relative sliding velocity of the revolute clea-rance joint were obtained by numerical simulations.Then,the trained BP neural network model was employed to predict the wear of clearance joint.Through repeated iterative prediction,it can be found that the wear depth occurred in the joint surface is non-uniform,owing to the fact that the large contact force and impact force between the bushing and pin occurs frequently in some special range of crank angle. |
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