A BP-ANN Based Surrogate Modeling for Predicting Engineering Analysis of Forging Press

Peng Fei Bao; Wei Dong Miao; Rong Xie; Yan Jun Shi

doi:10.4028/www.scientific.net/AMR.915-916.987

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 915-916A BP-ANN Based Surrogate Modeling for Predicting...

A BP-ANN Based Surrogate Modeling for Predicting Engineering Analysis of Forging Press

Abstract:

Engineering analysis and simulation are time-consuming, and often trapped to computational burden, such as analyzing forging press. We herein employ surrogate modeling to reduce such computation cost while keeping high precision. This paper use a BP neural networks to building the surrogate model (BPNN-SM for short), and predicting the analysis results of mechanical structures with this model. The predicting process include confining design variables, sampling, building finite element model with business software ANSYS, constructing surrogate model to replace the original model and finally predicting data with the new model. In such process, we build a back-propagation neural network, and train it with sampling data from ANSYS results. We tested our methods with a mechanical structure design of hydraulic forging press. The experimental results verified the surrogate modeling.

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Periodical:

Advanced Materials Research (Volumes 915-916)

Pages:

987-991

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.915-916.987

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Online since:

April 2014

Authors:

Peng Fei Bao, Wei Dong Miao, Rong Xie, Yan Jun Shi*

Keywords:

Artificial Neural Network (ANN), Computational Burden, Engineering Analysis, Surrogate Modeling

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References

[1] Wang, G.G. and S. Shan, Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design. 129(2007): p.370.

DOI: 10.1115/1.2429697

Google Scholar

[2] Simpson, T.W., et al., Metamodels for computer-based engineering design: survey and recommendations. Engineering with computers. 17(2): (2001) pp.129-150.

DOI: 10.1007/pl00007198

Google Scholar

[3] Jin, R., W. Chen, and T.W. Simpson, Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization. 23(1): (2001) pp.1-13.

DOI: 10.1007/s00158-001-0160-4

Google Scholar

[4] Szewczyk, Z. and P. Hajela, Neural network approximations in a simulated annealing based optimal structural design. Structural optimization. 5(1993): pp.159-165.

DOI: 10.1007/bf01743352

Google Scholar

[5] Salajegheh, E. and S. Gholizadeh, Optimum design of structures by an improved genetic algorithm using neural networks. Advances in Engineering Software. 36(11) (2005): pp.757-767.

DOI: 10.1016/j.advengsoft.2005.03.022

Google Scholar