Parameters' Correlation Analysis Based on Gaussian Process for Injection Molding

Abstract:

Based on Gaussian process (GP), a new parameters’ correlation analysis method for injection molding is proposed. Referred to the design idea of canonical correlation analysis (CCA), GP is used to extract accurate canonical correlation variables simultaneously from two data sets. And then the canonical correlation variables are used to analyze the correlation between parameters and design objectives. The cross member under windshield of a van is taken for a case. For the weld lines defects produced in injection process, the correlation of process parameters is analyzed to identify which parameters are more related to weld lines. The validity of this method is proved by the optimal result. And this provides strong theory and feasible algorithm for adaptive intelligent optimization and controlling of the parameters in injection process.

• Periodical: Advanced Materials Research (Volumes 154-155)
• Pages: 130-136
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.154-155.130
• Online since: October 2010
• Authors: Wei Xia, Sheng Hua Zhang, Xiao Ping Liao, Xue Lian Yan
• Keywords: Canonical Correlation Analysis, Gaussian Process (GP), Injection Molding, Intelligent Controlling, Intelligent Optimization, Parameters’ Correlation Analysis

References

[1] Wang Zhongyu, LiZhu and John.H. Wu: The Journal of Grey System, Aug. (1996).

[2] N. Lawrence: Journal of Machine Learning Research, Jun. 2005, pp.1783-1816.

[3] Z.K. Gou and C. Fyfe: Neural Networks, May. (2003).

[4] F.K. Wang and T.C.T. Du: The International Journal of Management Science, Oct. 2000, pp.185-194.

[5] R.M. Neal: Springer, New York, May. (2004).

[6] C.E. Rasmussen and C.K.I. Williams: Massachusetts Institute of Technology. July. (2006).

[7] C. K. I. Williams: Technical report, Aston University, Aug. (1997).

[8] H. Le: Statistics & Probability Letters, Sep. 2007, pp.669-674.

[9] Bojan Likar and Jus Kocijan: Computers & Chemical Engineering, May. 2006, pp.142-152.

[10] Tao Chen, Julian Morris and Elaine Martin: Chemometrics and Intelligent laboratory systems, Sep. 2006, pp.59-71.

[11] F. R. Bach and M. I. Jordan: Technical Report 688, Dept. of Statistics, University of California, Apr. (2005).

[12] P.L. Lai and C. Fyfe: International Journal of Neural Systems, Oct. 2001, pp.365-377.

[13] F. Colin and L. Gayle: Neurocomputing, July. 2008, pp.3077-3088.

[14] Malguarnera.S. C, Manisali A.I. and Riggs.D. C: Polymer Engineering and Science, Vol. 21, No. 17, pp.1149-1155.

[15] Leary S, Bhaskar A and Keane A: Journal of Applied Statistics, Vol. 30, No. 5, 2003, pp.585-598.