Recognizing control chart patterns with neural network and numerical fitting

Control chart has been widely used to determine whether the state of machining process is stable or not, and pattern recognition technology is often used to automatically judge the changing modes of control chart. It is because that the abnormal patterns of a control chart can reveal the potential p...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2009-12, Vol.20 (6), p.625-635
Main Authors: Jiang, Pingyu, Liu, Daoyu, Zeng, Zhijian
Format: Article
Language:English
Subjects:
Back propagation
Business and Management
Control
Control charts
Machines
Manufacturing
Mechatronics
Methods
Monte Carlo simulation
Neural networks
Pattern recognition
Process controls
Processes
Production
Robotics
Statistical process control
Studies
Training
Trends
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Summary:Control chart has been widely used to determine whether the state of machining process is stable or not, and pattern recognition technology is often used to automatically judge the changing modes of control chart. It is because that the abnormal patterns of a control chart can reveal the potential problem of machining quality. In order to improve the recognition rate and efficiency of control chart patterns, a neural network-numerical fitting (NN-NF) model is proposed to recognize different control chart patterns. A back propagation (BP) network is first used to recognize control chart patterns preliminarily. And then, numerical fitting method is adopted to estimate the parameters and specific types of the patterns, which is different from the traditional neural network-based control chart pattern recognition methods. Based on this, Monte Carlo simulation is used to generate training and testing data samples. The results of simulated experiment show that training time of this NN-NF model can be reduced. At the same time, the recognition rate can also be improved. At last, a real example is also provided to illustrate the NN-NF model.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-008-0152-y