Prediction and Analysis of Forged Workpiece’s Precision in Hydraulic Press Based on BP Neural Network and Genetic Algorithm

Yan Cong Li; Lian Hong Zhang; Chun Zhang

doi:10.4028/www.scientific.net/AMR.97-101.2598

Paper Titles

Dynamic Simulation of Position Servo System of Pneumatic Manipulator Based on AMESim and Simulink
p.2580

Finite Element Simulation of Temperature Field in Fused Deposition Modeling
p.2585

The Study on Suppression of Dimples in Digital Multi-Point Stretching Process
p.2589

Flexible Multibody Simulation Approach in the Analysis of Friction Winder
p.2594

Prediction and Analysis of Forged Workpiece’s Precision in Hydraulic Press Based on BP Neural Network and Genetic Algorithm
p.2598

Analysis of Assembling and Working Stress of Compound Rings High-Pressure Vessel of 5GPa
p.2603

Virtual Machining on Helical Bevel Gear with New Type Heavy Duty Helical Bevel Gear Milling Machine Tool
p.2607

Effects of Opening Hole Geometric Parameters on Hole-Edge Stress Field of Plates According to a Complex Variable Function Model
p.2614

Simulation and Optimization on the Injection Molding for Auto-Bumper
p.2618

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 97-101Prediction and Analysis of Forged Workpiece’s...

Prediction and Analysis of Forged Workpiece’s Precision in Hydraulic Press Based on BP Neural Network and Genetic Algorithm

Abstract:

Workpiece’s precision is an important indicator of hydraulic press. In order to accurately predict the accuracy of the part, a method that combined the genetic algorithm and neural network is put out. Design of orthogonal experiment (DOE) is used to determine the input samples of neural network training and testing samples. The output samples are obtained by finite element analysed method (FEA). Through optimizing weights and thresholds of BP neural network using genetic algorithms, prediction model of workpiece’s precision is established. The established predict model overcomes the shortcomings of slow to convergence and easy to fall into the local minimum point of BP neural network model . By comparing the neural network forecast result with FEA ‘s results, it can be seen that the established prediction model has good fitting and generalization ability. So the model can be used to predict the workpiece’s precision.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 97-101)

Pages:

2598-2602

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.97-101.2598

Citation:

Cite this paper

Online since:

March 2010

Authors:

Yan Cong Li, Lian Hong Zhang, Chun Zhang

Keywords:

Genetic Algorithm (GA), Hydraulic Machine, Neural Network (NN), Precision

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Y. C. Li, L. H. Zhang, C. Zhang: Mechanical Science and Technology, Vol. 27(5)(2008), pp.571-574. (in Chinese).

Google Scholar

[2] H. X. Li, X.H. Zhao, H. Y. Chi: Journal of Tianjin University, Vol. 42(l)(2009), pp.60-64. (in Chinese).

Google Scholar

[3] Y. S. Cheng, Y. Sun, G. Q. Yan: Ship Building of China, Vol. 49(4) (2008), pp.81-86 (in Chinese).

Google Scholar

[4] L. Wu, Z. H. Jiang, W. Gong: Journal of Northeastern University(Natural Science), 2(12) (2008), pp.1725-1728. (in Chinese).

Google Scholar

[5] C.C. Zhou G.F. Yin X.B. Hu, etc: Computer Integrated Manufacturing Systems, Vol. 14(5) (2008), pp.1024-1035. (in Chinese).

Google Scholar

[6] S. F . Wu, S. M. Nie, M. Jin: Journal of Plasticity Engineering, Vol. 14(2) (2007), pp.77-79, 93. (in Chinese).

Google Scholar

[7] Y.M. Kuo, C. W. Liu , K. H. Lin: Water Research，Vol. 38(1) (2004), pp.148-158．(In Taiwan).

Google Scholar

[8] K .J. Larson，H. Basagaog1u，M . A. Marifio: Journal of Hydrology, 2001, 242(1/2): 79-102.

Google Scholar