The processing quality prediction based on the OHIF Elman neural network

Jie Yang; Guixiong Liu

doi:10.1117/12.889145

26 May 2011 The processing quality prediction based on the OHIF Elman neural network

Jie Yang, Guixiong Liu

Proceedings Volume 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering; 79973P (2011) https://doi.org/10.1117/12.889145
Event: Fourth International Seminar on Modern Cutting and Measuring Engineering, 2010, Beijing, China

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.

Citation Download Citation

Jie Yang and Guixiong Liu "The processing quality prediction based on the OHIF Elman neural network", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973P (26 May 2011); https://doi.org/10.1117/12.889145

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