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A review of neural networks for statistical process control

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Abstract

This paper aims to take stock of the recent research literature on application of Neural Networks (NNs) to the analysis of Shewhart's traditional Statistical Process Control (SPC) charts. First appearing in the late 1980s, most of the literature claims success, great or small, in applying NNs for SPC (NNSPC). These efforts are viewed in this paper as useful steps towards automatic on-line SPC for continuous improvement of quality and for real-time manufacturing process control. A standard NN approach that can parallel the universality of the traditional Shewhart charts has not yet been developed or adopted, although knowledge in this area is rapidly increasing. This paper attempts to provide a practical insight into the issues involved in application of NNs to SPC with the hope of advancing the use of NN techniques and facilitating their adoption as a new and useful aspect of SPC. First, a brief review of control chart analysis prior to the introduction of NN technology is presented. This is followed by an examination and classification of the NNSPC existing literature. Next, an extensive discussion of implementation issues with reference to significant research papers is presented. Finally, after summarising the survey, a set of general guidelines for future applications of NNs to SPC is outlined.

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Authors and Affiliations

  1. Department of Manufacturing Engineering and Operations Management, University of Nottingham, Nottingham, NG7 2RD, UK

    F. Zorriassatine & J. D. T. Tannock

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  1. F. Zorriassatine
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  2. J. D. T. Tannock
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Zorriassatine, F., Tannock, J.D.T. A review of neural networks for statistical process control. Journal of Intelligent Manufacturing 9, 209–224 (1998). https://doi.org/10.1023/A:1008818817588

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