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Proceedings Paper

The processing quality prediction based on the OHIF Elman neural network
Author(s): Jie Yang; Guixiong Liu
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Paper Abstract

Quality prediction and control methods are crucial in acquiring safe and reliable operation in process quality control. Considering The standard Elman neural network model only effective for the low-level static system, then a new OHIF Elman is proposed in this paper, three different feedback factor are introduced into the hidden layer, associated layer, and output layer of the Elman neural network. In order to coordinate the efficiency of prediction accuracy and prediction, LM-CGD mixed algorithm is used for training the network model. The simulation and experiment results show the quality model can effectively predict the characteristic values of process quality, and it also can identify abnormal change pattern and enhance process control accuracy.

Paper Details

Date Published: 26 May 2011
PDF: 6 pages
Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973P (26 May 2011); doi: 10.1117/12.889145
Show Author Affiliations
Jie Yang, South China Univ. of Technology (China)
Guangdong Univ. of Technology (China)
Guixiong Liu, South China Univ. of Technology (China)


Published in SPIE Proceedings Vol. 7997:
Fourth International Seminar on Modern Cutting and Measurement Engineering
Jiezhi Xin; Lianqing Zhu; Zhongyu Wang, Editor(s)

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