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Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 244-247

Machining Accuracy Prediction of Aero-engine Blade in Electrochemical Machining Based on BP Neural Network

Zhiyong Li and Hua Ji

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In electrochemical machining (ECM) process, various machining parameters, such as applied voltage, current density, feed rate of tool cathode, electrolyte concentration and composition, machining gap, can result in the changes of machining accuracy of ECM process. Thus machining accuracy prediction is one of the most difficult problems in ECM. Utilizing an aero-engine blade as the research object, BP neural network is employed to predict the machining accuracy of the aero-engine blade in ECM. In prediction model, five main process parameters are involved. The prediction results demonstrate that the proposed BP neural network model is valuable and the prediction accuracy errors along the selected blade profiles can be less than 8%.

Index Terms

accuracy prediction, electrochemical machining, BP neural network, aero-engine blade

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