Machining Parameter Optimization of Aero-Engine Blade in Electrochemical Machining Based on BP Neural Network

Abstract:

Article Preview

Because the process of blade in electrochemical machining(EMC) can be effected by many factors, such as blade shapes, machining electrical field, electrolyte fluid field and anode electrochemical dissolution, different ECM machining parameters maybe result in great affections on blade machining accuracy. Regard some type of aero-engine blade as research object, five main machining parameters, applied voltage, initial machining gap, cathode feed rate, electrolyte temperature and pressure difference between electrolyte inlet and outlet, have been evaluated and optimized based on BP neural network technique. From 3125 possible machining parameter combinations, 657 optimized parameter combinations are discovered. To verify the validity of the optimized ECM parameter combination, a serial of machining experiments have been conducted on an industrial scale ECM machine, and the experiment results demonstrates that the optimized ECM parameter combination not only can satisfy the manufacturing requirements of blade fully but has excellent ECM process stability.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

893-899

Citation:

Z. Y. Li et al., "Machining Parameter Optimization of Aero-Engine Blade in Electrochemical Machining Based on BP Neural Network", Advanced Materials Research, Vols. 121-122, pp. 893-899, 2010

Online since:

June 2010

Export:

Price:

$38.00

[1] Brusilovski, Z, Adjustment and readjustment of electrochemical machines and control of the process parameters in machining shaped surfaces[J]. Journal of Materials Processing Technology Vol 196, pp.311-320 (2008).

DOI: https://doi.org/10.1016/j.jmatprotec.2007.05.058

[2] N. Smets � S. Van Damme � D. De Wilde, Comment on 'Numerical model for predicting the efficiency behaviour during pulsed electrochemical machining of steel in NaNO3', [J], International Journal of Electrochem, Vol40, pp.205-207. (2010).

DOI: https://doi.org/10.1007/s10800-009-9964-5

[3] Nilanjan Das Chakladar, Ranatosh Das and Shankar Chakraborty, A digraph-based expert system for non-traditional machining processes selection [J], The International Journal of Advanced Manufacturing Technology, Vol43 (3-4), pp.127-132. (2009).

DOI: https://doi.org/10.1007/s00170-008-1713-0

[4] Li Zhiyong, Niu Zongwei, Convergence analysis of the numerical solution for cathode design of aero-engine blades in electrochemical machining, Chinese Journal of Aeronautics, Vol 20(6). pp.570-576. (2007).

DOI: https://doi.org/10.1016/s1000-9361(07)60084-3

[5] Y.C. Lin, Jun Zhang, Jue Zhong, Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel, Computational Materials Science, Vol43 pp.752-758. (2008).

DOI: https://doi.org/10.1016/j.commatsci.2008.01.039

[6] Young-Don Ko, Pyung Moon, Chang Eun Kim, Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms, Expert Systems with Applications, Vol36, pp.061-4066. (2009).

DOI: https://doi.org/10.1016/j.eswa.2008.03.010