Deformation Prediction Based on BP Artificial Neural Network of Milling Thin-Walled Aluminum Alloy Parts

Hua Bing Ouyang

doi:10.4028/www.scientific.net/AMM.687-691.492

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Deformation Prediction Based on BP Artificial...

Deformation Prediction Based on BP Artificial Neural Network of Milling Thin-Walled Aluminum Alloy Parts

Abstract:

Thin-walled aluminum alloy parts are widely used in the aviation industry. In order to predict the deformation of milling aluminum alloy 7075-T7451 thin-walled parts, a deformation prediction method based on BP artificial neural network is presented. Firstly, the orthogonal experiment is designed to acquire the experimental data. Secondly, the BP neural network model of deformation prediction based on the experimental data is established. The comparison of the simulated values with the experimental results is performed to validate the proposed model. Lastly, the result shows that the proposed deformation prediction model is reasonable and can be used to predict the milling deformation.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

492-495

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.492

Citation:

Cite this paper

Online since:

November 2014

Authors:

Hua Bing Ouyang*

Keywords:

BP Neural Network (BPNN), Deformation Prediction, Milling Deformation, Milling Parameter, Thin-Wall Parts

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* - Corresponding Author

References

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