A Sub-stage Moving Window GRNN Quality Prediction Method for Injection Molding Processes

For injection molding process, a typical multistage batch process, the final product qualities are usually available at the end of the batch, which make it difficult for on-line quality control. A sub-stage moving window generalized regression neural network (GRNN) is proposed for dedicating to reveal the nonlinearly and dynamic relationship between process variables and final qualities at different stages. Firstly, using an clustering arithmetic, PCA P-loading matrices of time-slice matrices is clustered and the batch process is divided into several operation stages, the most relevant stage to the quality variable is defined, and then applying moving windows to un-fold stage data according to time, and sub-stage GRNN models are developed for every windows for on-line quality prediction. For comparison purposes a sub-MPLS quality model of every moving window was establish. The results prove the effectiveness of the proposed quality prediction method is supervior to sub- MPLS quality prediction method.

Authors and Affiliations

1. Information Science and Engineering School, Northeastern University, Shenyang, China

   Xiao-Ping Guo, Fu-Li Wang & Ming-Xing Jia

2. Information Engineering School, Shenyang Institute of Chemical Technology, Shenyang, China

   Xiao-Ping Guo

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

   Jacek M. Zurada

4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

   Bao-Liang Lu

5. School of Electrical and Electronic Engineering, University of Manchester, UK

   Hujun Yin

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