Research on Predicting the Stability of Partially Stabilized Zirconia Based on SVM

Abstract:

The stability is one most important product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), and the stability of PSZ is always fluctuating in the commercial process, so how to accurately, quickly and easily predict the stability of PSZ in the preparation process is very important. In the present paper, a new mathematical model to predict the stability of PSZ was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from commercial process were collected for the training samples and test samples. Then testing and analyzing was done. The results showed that the max relative error was 1.80%, the least relative error was 0%, and the average relative error was 0.58%. It is accurate and reliable to predict the stability of PSZ by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ is provided for commercial application.

• Periodical: Advanced Materials Research (Volumes 146-147)
• Pages: 460-465
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.146-147.460
• Online since: October 2010
• Authors: Sheng Hui Guo, Dong Bo Li, Li Jun Liu, Jin Hui Peng, Li Bo Zhang, Guo Chen
• Keywords: Partially Stabilized Zirconia, Predict, Stability, Support Vector Machine (SVM)

References

[1] KURUMADA M, HARA H, IGUCHI E. Acta Materialia, 2005, 53(18): 4839-4846.

DOI: 10.1016/j.actamat.2005.06.027

[2] PAWLOWSKI Andrzej, BUCKO Miroslaw M, PEDZICH Zbigniew. Mater Res Bull, 2002, 37(3): 425-438.

[3] Xinmeng Yu, Baokui Xu, Fade Yuan. Rare Metals Letters, 2007, 26(1): 28-32. In Chinese.

[4] T. Sakuma. Trans. Jpn. Inst. Met, 1988, 29(11): 879-893.

[5] Vapnik, V. Statistical Learning Theory. 1998, Wiley, New York.

[6] Vapnik V N, The Nature of Statistical Learning Theroy. New York: Springer Verlag, (1995).