Quality Prediction and Control of Injection Molding Process using Multistage MWGRNN Method

Author

Guo, Xiao-ping ; Wang, Fu-li ; Wang, Shu

Author_Institution

Sch. of Inf. Eng., Shenyang Inst. of Chem. Technol.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3095

Lastpage

3100

Abstract

A multistage moving window generalized regression neural network (GRNN) was demonstrated to injection molding batch process. Firstly analyzing the changes of process correlation can lead to effective division of a process into several "operation" stages, in good agreement with process knowledge. Then the nonlinearly and dynamic relationship between process variables and final qualities was made at different stages, and a multistage online quality prediction model was built. In addition, a closed-loop quality control system is proposed. Application has demonstrated that this method can not only give a valid quality prediction, but also effectively carry on quality closed-loop control

Keywords

batch processing (industrial); closed loop systems; injection moulding; neural nets; principal component analysis; production engineering computing; quality control; regression analysis; statistical process control; closed-loop quality control system; injection molding batch process; multistage moving window generalized regression neural network method; multistage online quality prediction model; quality control; quality prediction; Artificial neural networks; Chemical industry; Chemical technology; Cybernetics; Fault detection; Information science; Injection molding; Machine learning; Multiprotocol label switching; Neural networks; Plastics; Predictive models; Quality control; Injection molding; Multistage batch process; generalized regression neural network (GRNN); quality prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258373

Filename

4028596