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Monitoring defects of ceramic tiles using fuzzy subtractive clustering-based system identification method

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Abstract

In this paper, a subtractive clustering fuzzy identification method and a Sugeno-type fuzzy inference system are used to monitor tile defects in tile manufacturing process. The models for the tile defects are identified by using the firing mechanical resistance, water absorption, shrinkage, tile thickness, dry mechanical resistance and tiles temperature as input data, and using the concavity defect and surface defects as the output data. The process of model building is carried out by using subtractive clustering in both the input and output spaces. A minimum error model is developed through exhaustive search of clustering parameters. The fuzzy model obtained is capable of predicting the tile defects for a given set of inputs as mentioned above. The fuzzy model is verified experimentally using different sets of inputs. This study intends to examine and deal with the experimental results obtained during various stages of ceramic tile production during 90-day period. It is believed, that the results obtained from the present study could be considered in other ceramic tiles industries, which experienced similar forms of defects.

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Acknowledgments

The authors would like to gratefully acknowledge the International Ceramic Industrial Company which gave invaluable support and guidance for this work, Mafraq, Jordan. They also, believe that the obtained results from this attempt will be helpful to bridge and reinforce the relation between academy and industry in Jordon.

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Authors and Affiliations

  1. Industrial Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Mohammed T. Hayajneh, Adel Mahmood Hassan & Fatma Al-Wedyan

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  1. Mohammed T. Hayajneh
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  2. Adel Mahmood Hassan
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  3. Fatma Al-Wedyan
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Correspondence to Mohammed T. Hayajneh.

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Hayajneh, M.T., Hassan, A.M. & Al-Wedyan, F. Monitoring defects of ceramic tiles using fuzzy subtractive clustering-based system identification method. Soft Comput 14, 615–626 (2010). https://doi.org/10.1007/s00500-009-0430-4

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  • DOI: https://doi.org/10.1007/s00500-009-0430-4

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