In the development of a process diagnostic system to monitor the condition of the frequency-trimming process in the production of crystal resonators, fuzzy logic can be applied in the recognition of unnatural statistical patterns in the control charts. The heuristics for reasoning are based on the principles behind statistical process control. Using expert experience and knowledge to troubleshoot the causes of problems, one can associate a characteristic chart pattern with a set of known physical causes. As these causes related to the unnatural statistical patterns are not independent of each other, it is difficult to precisely associate the chart distribution patterns with the known causes. Furthermore, as the trimming process is dynamic, the causes of problems dealt with will vary with time. Hence, by means of neural networks, it is possible to associate fuzzily deduced chart patterns with plausible causes to achieve optimum operating conditions.
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References
Grant, E. L. and Leavenworth, R. S. (1988) Statistical Quality Control (6th edn), Vol. 2, McGraw-Hill, New York, pp. 42–67.
Gwee, B. H. (1992) Self-adjusting fuzzy diagnostic system, Master's Thesis, Nanyang Technological University, Singapore.
Hayashi, I. (1991) Fuzzy control and its applications, in International Symposium on IC Design, Manufacture and Applications, ISIC-91, Singapore, pp. 54–65.
Kevin, S. (1990) Designing with fuzzy logic. IEEE Spectrum, 105, 42–44.
Kosko, B. (1992) Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall International, New Jersey.
Lim, M. H. and Ooi, T. H. (1990) Statistical process control using fuzzy logic, Condition Monitoring and Diagnostic Engineering Management, COMADEM-90, England, pp. 240–246.
Lim, M. H. and Takefuji, Y. (1990) Implementing fuzzy rule-based systems on silicon chips. IEEE Expert, February, 31–45
Lim, M. H., Gwee, B. H. and Goh, T. H. (1991) Cause associator network for fuzzily deduced conclusion in process control, in International Joint Conference on Neural Networks, IJCNN-91, Singapore, 2, pp. 1248–1253.
McClelland, J. L. and Rumelhart, D. E. (1988) Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises, MIT Press, pp. 121–159.
Neale, M. J. (1985) The benefit of condition monitoring, in Condition Monitoring of Machinery and Plant, Mechanical Engineering Publications, London, pp. 25–30.
Zadeh, L. A. (1978) A theory of approximate reasoning, in Machine Intelligence, Vol. 9, Elsevier, New York, pp. 149–194.
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Lim, M.H., Gwee, B.H. & Kawada, Y. Intelligent monitoring of a frequency-trimming process. J Intell Manuf 4, 375–383 (1993). https://doi.org/10.1007/BF00123951
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DOI: https://doi.org/10.1007/BF00123951