Optimization of Sheet Metal Forming Process Parameters by Artificial Neural Network and Orthogonal Test Method

DOI

10.2991/icemie-16.2016.48How to use a DOI?

Keywords

Artificial Neural Network (ANN); orthogonal experiment; technological parameter; optimization

Abstract

Combined with artificial neural network of good features, using the orthogonal experiment data obtained as the training sample of neural network we established a neural network model of the input for the process parameters, output for the springback amount, and the accuracy of the ANN model was verified by the sample, so as to shorten the time of setting the process parameters. Within the scope of the process parameter selection, the ANN model instead of CAE software simulation test, combined with orthogonal experiment method, to further optimize the process parameters. Results show that the neural network combined with orthogonal test, numerical simulation was applied to parameter optimization of sheet metal forming, can shorten the time of the optimization of process parameters, and improve the efficiency of process design.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wenqiong Zhang
AU  - Dongwei Wang
PY  - 2016/04
DA  - 2016/04
TI  - Optimization of Sheet Metal Forming Process Parameters by Artificial Neural Network and Orthogonal Test Method
BT  - Proceedings of the 2016 International Conference on Electrical, Mechanical and Industrial Engineering
PB  - Atlantis Press
SP  - 194
EP  - 196
SN  - 2352-5401
UR  - https://doi.org/10.2991/icemie-16.2016.48
DO  - 10.2991/icemie-16.2016.48
ID  - Zhang2016/04
ER  -