Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: II. with Hybrid GA-BP Method

Jing Jie Zhang; Chong Hai Xu; Ming Dong Yi; Hui Fa Zhang; Xing Hai Wang

doi:10.4028/www.scientific.net/AMR.154-155.1091

Paper Titles

Multiscale Simulation of Uniaxial Tension Deformation for Single Crystal Cu under Various Crystallographic Orientations
p.1073

Tests on High-Velocity Forming of AZ31 Magnesium Alloy by Explosive-Impulsive Pressure（Part I）
p.1077

Study on the Brush Plating Technology of 2A11 Duralumin Alloy Joining
p.1081

Control of PLC for Paving Stock Mechanism of New-Type Machine Tool about Rapid Prototyping Manufacturing for Ceramic Parts
p.1085

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: II. with Hybrid GA-BP Method
p.1091

Interface Behavior of Joint between CoCr Coating and Steel 35CrMo Substrate Produced by Electro-Spark Deposition
p.1096

Mechanics Behavior Analysis on Laser Cladding under Repeated Impacting Load
p.1100

Preparation of WC-Low Chromium Cast Iron Composite Material by Cast-Infiltration
p.1104

Study on the Spraying Technique of Ultra-High Strength Parts Made by 30CrMnSiNi2A Steel
p.1110

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 154-155Optimization of the Hot Pressing Parameters of...

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: II. with Hybrid GA-BP Method

Abstract:

In this paper, the two hybrid algorithms of back propagation artificial neural network and genetic algorithm were used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. Compared with the BP algorithm, the predicted results of hybrid algorithm indicated that the combination algorithm can offer a robust and efficient way for the fabrication process design of ceramic tool and die materials.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 154-155)

Pages:

1091-1095

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.154-155.1091

Citation:

Cite this paper

Online since:

October 2010

Authors:

Jing Jie Zhang, Chong Hai Xu, Ming Dong Yi, Hui Fa Zhang, Xing Hai Wang

Keywords:

BP Neural Network (BPNN), Ceramic Tool, Die Material, Genetic Algorithm (GA), Hot Pressing Parameter, Mechanical Property

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.N.D. Gupta, R. S: Sexton, Omega Vol. 27 (1999), p.679.

Google Scholar

[2] Bo Yang, Xiao-Hong Su, Ya-Dong Wang: Machine Learning and Cybernetics, 2002. Proceedings.

Google Scholar

[3] S.H. Mousavi Anijdan, H.R. Madaah-Hosseini, A. Bahrami: Materials and Design Vol. 28 (2007), p.609.

Google Scholar

[4] M. Yazdanmehr, S.H. Mousavi Anijdan, A. Bahrami: Computational Materials Science Vol. 44 (2009), p.1218.

Google Scholar

[5] Fu Zemin, Mo Jianhua, Chen Lin: Materials and Design Vol. 31 (2010), p.267.

Google Scholar

[6] J.J. Zhang, C.H. Xu, M.D. Yi, H.F. Zhang, X.H. Wang: submitted to The International Conference on Advanced in Materials and Manufacturing Processes (2010).

Google Scholar

[7] D.Q. Zhu, H. Shi: The principle and application of artificial neural networks(Science Publications, China 2006)(in Chinese).

Google Scholar