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Study on the influence of process parameters on the synthesis of AlON-TiN composites by using support vector regression

Wen Yu-Feng Cai Cong-Zhong Pei Jun-Fang Zhu Xing-Jian Xiao Ting-Ting Wang Gui-Lian

Study on the influence of process parameters on the synthesis of AlON-TiN composites by using support vector regression

Wen Yu-Feng, Cai Cong-Zhong, Pei Jun-Fang, Zhu Xing-Jian, Xiao Ting-Ting, Wang Gui-Lian
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  • Received Date:  15 January 2009
  • Accepted Date:  20 April 2009
  • Published Online:  21 December 2009

Study on the influence of process parameters on the synthesis of AlON-TiN composites by using support vector regression

  • 1. 重庆大学应用物理系,重庆 400044

Abstract: According to the experimental dataset on the bending strength of AlON-TiN composite synthesized by hot pressing sintering approach under different processing parameters, i.e., mass fraction of TiN, sintering temperature and soaking time, the support vector regression (SVR) approach combined with particle swarm optimization for its parameter optimization, is proposed to simulate the relationship between the bending strength and hot pressing sintering synthesis parameters of AlON-TiN composites. The optimization of process parameters and the multi-factor analysis are also carried out. The prediction result demonstrates that the estimation error of the SVR model is less than that of the artificial neural network(ANN) model under the identical training and test samples and reveales that the generalization ability of SVR model surpasses that achieved by the ANN model. The optimal synthesis parameters are obtained numerically under TiN content 13.5%, sintering temperature 1863.5 ℃ and soaking time 5.8 h. The maximum bending strength is estimated to be 555.452 MPa while the AlON-TiN composite is synthesized at the optimal synthesis parameters. These results suggest that SVR can provide an important theoretical and practical guidance to the research and development of AlON-TiN composite possessing ideal bending strength.

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