Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

A Prediction Model for Thin-walled Duct Structure based with RBF Neural Network

Authors
Jianbing Wang, Wen Zhang, Yueqiang Zhang, Yingsheng Huang
Corresponding Author
Jianbing Wang
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.106How to use a DOI?
Keywords
RBF neural network, thin-walled duct structure design, forecasting design, ABAQUS.
Abstract
Component structure design is based on the finite element analysis technology, and the structure optimization is obtained by the iterative process of the boundary. However, this method is time-consuming and expensive to deal with the numerical simulation or complex engineering problems. In this paper, we combined the technology of experimental design and RBF neural network model technology, established a high accuracy analysis and prediction model of the structural shape by using a small number of sample points. We instituted the intrinsic part model and found the structural parameters within the constraints in the surrogate model. Consequently, we predicted the optimize structure of the part. Firstly, this paper described the design idea and theoretical basis of the method, and then illustrated the application process of the method with the example of the structure optimization of thin walled duct. The experimental results showed that this method can improve the computational efficiency and the design quality of the structure.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.106How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jianbing Wang
AU  - Wen Zhang
AU  - Yueqiang Zhang
AU  - Yingsheng Huang
PY  - 2016/03
DA  - 2016/03
TI  - A Prediction Model for Thin-walled Duct Structure based with RBF Neural Network
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 528
EP  - 532
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.106
DO  - https://doi.org/10.2991/icmmct-16.2016.106
ID  - Wang2016/03
ER  -