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Prediction of Surface Quality and Parameter in Bearing Convex Raceway Finishing

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Abstract:

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.

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Periodical:

Advanced Materials Research (Volumes 24-25)

Pages:

361-370

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.24-25.361

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Online since:

September 2007

Authors:

Bin Tao, Xu Yue Wang, H.Z. Zhen, Wen Ji Xu

Keywords:

Bearing Raceway, BP Neural Network (BPNN), Electrochemical Abrasive-Belt Grinding (ECABG)

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© 2007 Trans Tech Publications Ltd. All Rights Reserved

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