A new ANN model and its application in pattern recognition of control charts

Author

Le, Qinghong ; Gao, Xinghai ; Teng, Lin ; Zhu, Mingquan

Author_Institution

Flight Autom. Control Res. Inst., Xi´´an, China

Volume

2

fYear

2004

fDate

15-19 June 2004

Firstpage

1807

Abstract

Pattern recognition of abnormal control charts can provide clues to reveal potential quality problems in manufacturing process. This paper aims to realize the automatic recognition of abnormal patterns of control charts in a statistical process control (SPC) system. A new neural network model named regional supervised feature mapping (RSFM) network was proposed to recognize the control chart patterns, which include six basic patterns and their mixed patterns. The performance of network was studied, and its parameters were optimized. Euclid-distance-discriminance approach was developed to recognize mixed abnormal patterns. Numerical results show this network possesses advantages of quick training and good recognition performance, which is fit for pattern recognition of control charts in a real time SPC system.

Keywords

control charts; manufacturing processes; neural nets; pattern recognition; statistical process control; ANN model; Euclid-distance-discriminance approach; control charts; manufacturing process; neural network model; pattern recognition; regional supervised feature mapping network; statistical process control system; Artificial neural networks; Automatic control; Character recognition; Control charts; Expert systems; Manufacturing processes; Mechatronics; Neural networks; Neurons; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1340986

Filename

1340986