The potential of using artificially simulated neural networks as intelligent, adaptive process-monitoring devices is discussed. The investigation is considered as a method for automatic, intelligent exception reporting for quality control applications. The technique is also compared with the conventional statistical approaches of principal component analysis and Kohonen's feature map. The applications of the technique in aerospace and manufacturing environments are presented and a possible extension of the method to incorporate a diagnostic function is discussed.
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Seconded from Cheltenham and Gloucester College of Higher Education as a Royal Society/SERC Research Fellow at Smith's Industries Aerospace and Defence Systems, Bishop's Cleeve, Cheltenham, UK.
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Skitt, P.J.C., Javed, M.A., Sanders, S.A. et al. Process monitoring using auto-associative, feed-forward artificial neural networks. J Intell Manuf 4, 79–94 (1993). https://doi.org/10.1007/BF00124982
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DOI: https://doi.org/10.1007/BF00124982