Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

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

doi:10.4028/www.scientific.net/AMR.154-155.1114

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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: I. with BP Neural Network Method

Abstract:

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

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Periodical:

Advanced Materials Research (Volumes 154-155)

Pages:

1114-1118

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.154-155.1114

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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, Hot Pressing Parameter

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References

[1] D.J. Scott, P.V. Coveney, J.A. Kilner, J.C.H. Rossing, and N.M. Alford: Journal of the European Ceramic Society Vol. 27 (2007), p.4425.

Google Scholar

[2] W. Sha, K.L. Edwards: Materials & Design. Vol. 28 (2007), p.1747.

Google Scholar

[3] D.M. Sun, L.H. Liu, H.W. Shi: Material Science and Technology Vol. 15 (2005), p.456.

Google Scholar

[4] J.J. Zhang, C.H. Xu, M.D. Yi, in: Prediction of the Mechanical Properties of Ceramic Die Material with Artificial Neural Network and Genetic Algorithm, edited by Chen Wen, Li Shaozi, Wang Yinglin, volume 1 of Progress in Intelligent Computing and Intelligent Systems, Institute of Electrical and Electronics Engineers, Inc. Publishers (2009).

DOI: 10.1109/icicisys.2009.5357604

Google Scholar

[5] N. Fan, X. Bo. Ze, Z.H. Gao: Materials Science and Technology Vol. 20 (2004), p.797.

Google Scholar

[6] J.Q. Yan, S.G. Shan, W.Q. Wu, et al: Bulletin of the Chinese Ceramic Society Vol. 3 (1999), p.56 (in Chinese).

Google Scholar

[7] N. Altinkok, R.N. Korker: Materials & Design. Vol. 25 (2004), p.595.

Google Scholar

[8] C.Z. Huang, L. Zhang, L. He, J. Sun, B. Fang and B. Zou: Journal of Materials Processing Technology Vol. 129(2002), p.399.

Google Scholar