论文成果
Prediction of surface quality and parameter in bearing convex raceway finishing
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- 论文类型：会议论文
- 发表时间：2007-11-05
- 收录刊物：EI、CPCI-S、Scopus
- 文献类型：A
- 卷号：24-25
- 页面范围：361-370
- 关键字：BP neural network; bearing raceway; Electrochemical Abrasive-belt
Grinding (ECABG)
- 摘要：Electrochemical abrasive belt grinding (ECABG) technology, which has the advantage over conventional stone super-finishing, has been applied in bearing raceway super-finishing. However, the finishing effect of ECABG is dominated by many factors, which relationship is so complicated that appears non-linear behavior. Therefore, it is difficult to predict the finishing results and select the processing parameters in ECABG. In this paper, Back-Propagation (BP) neural network is proposed to solve this problem. The non-linear relationship of machining parameters was established based on the experimental data by applying one-hidden layer BP neural networks. The comparison between the calculated results of the BP neural network and experimental results under the corresponding conditions was carried out, and the results indicates that it is feasible to apply BP neural network in determining the processing parameters and forecasting the surface quality effects in ECABG.
