Fuzzy Hierarchical Clustering Applied for Quality Estimation in Manufacturing System
This paper develops a quality estimation method with
the application of fuzzy hierarchical clustering. Quality estimation is
essential to quality control and quality improvement as a precise
estimation can promote a right decision-making in order to help
better quality control. Normally the quality of finished products in
manufacturing system can be differentiated by quality standards. In
the real life situation, the collected data may be vague which is not
easy to be classified and they are usually represented in term of fuzzy
number. To estimate the quality of product presented by fuzzy
number is not easy. In this research, the trapezoidal fuzzy numbers
are collected in manufacturing process and classify the collected data
into different clusters so as to get the estimation. Since normal
hierarchical clustering methods can only be applied for real numbers,
fuzzy hierarchical clustering is selected to handle this problem based
on quality standards.
[1] Dudek-Burlikowska, M. (2005). Quality estimation of process with
usage control charts type XR and quality capability of process Cp, Cpk
Journal of materials processing technology 162: 736-743.
[2] L. A. Zadeh, "Fuzzy set", Information and Control 8: 338-353, 1965.
[3] Ho, G. T. S., H. C. W. Lau, et al. (2006). "An intelligent production
workflow mining system for continual quality enhancement." The
International Journal of Advanced Manufacturing Technology 28(7):
792-809.
[4] Lau, H. C. W., E. N. M. Cheng, et al. (2008). "A fuzzy logic approach to
forecast energy consumption change in a manufacturing system." Expert
Systems with Applications 34(3): 1813-1824.
[5] Lau, H. C. W., G. T. S. Ho, et al. (2009). "Development of an intelligent
quality management system using fuzzy association rules." Expert
Systems with Applications 36(2, Part 1): 1801-1815.
[6] Yaqiong, L., L. K. Man, et al. "Fuzzy theory applied in quality
management of distributed manufacturing system: A literature review
and classification." Engineering Applications of Artificial Intelligence.
[7] Dubois, D. and H. Prade (1978). "Operations on fuzzy numbers."
International Journal of Systems Science 9(6): 613-626.
[8] S. Heilpern, Representation and application of fuzzy numbers, Fuzzy
Sets andSystems 91 (1997)259-268.
[9] Defuzzification: criteria and classification, from the journal Fuzzy Sets
and Systems, Van Leekwijck and Kerre, Vol. 108 (1999), pp. 159-178
[10] Chen, S. H., S. T. Wang, et al. (2006). "Some Properties of Graded
Mean Integration Representation of LR Type Fuzzy Numbers." Tamsui
Oxford Journal of Mathematical Sciences 22(2): 185.
[11] Chen, S. H. and C. C. Wang (2006). Fuzzy distance of trapezoidal fuzzy
numbers. Proceedings of the 9th Joint Conference on Information
Sciences,.
[12] Lv, Y. Q. and C. K. M. Lee Fuzzy hierarchical clustering based on fuzzy
dissimilarity. Industrial Engineering and Engineering Management
(IEEM), 2011 IEEE International Conference on, IEEE.
[1] Dudek-Burlikowska, M. (2005). Quality estimation of process with
usage control charts type XR and quality capability of process Cp, Cpk
Journal of materials processing technology 162: 736-743.
[2] L. A. Zadeh, "Fuzzy set", Information and Control 8: 338-353, 1965.
[3] Ho, G. T. S., H. C. W. Lau, et al. (2006). "An intelligent production
workflow mining system for continual quality enhancement." The
International Journal of Advanced Manufacturing Technology 28(7):
792-809.
[4] Lau, H. C. W., E. N. M. Cheng, et al. (2008). "A fuzzy logic approach to
forecast energy consumption change in a manufacturing system." Expert
Systems with Applications 34(3): 1813-1824.
[5] Lau, H. C. W., G. T. S. Ho, et al. (2009). "Development of an intelligent
quality management system using fuzzy association rules." Expert
Systems with Applications 36(2, Part 1): 1801-1815.
[6] Yaqiong, L., L. K. Man, et al. "Fuzzy theory applied in quality
management of distributed manufacturing system: A literature review
and classification." Engineering Applications of Artificial Intelligence.
[7] Dubois, D. and H. Prade (1978). "Operations on fuzzy numbers."
International Journal of Systems Science 9(6): 613-626.
[8] S. Heilpern, Representation and application of fuzzy numbers, Fuzzy
Sets andSystems 91 (1997)259-268.
[9] Defuzzification: criteria and classification, from the journal Fuzzy Sets
and Systems, Van Leekwijck and Kerre, Vol. 108 (1999), pp. 159-178
[10] Chen, S. H., S. T. Wang, et al. (2006). "Some Properties of Graded
Mean Integration Representation of LR Type Fuzzy Numbers." Tamsui
Oxford Journal of Mathematical Sciences 22(2): 185.
[11] Chen, S. H. and C. C. Wang (2006). Fuzzy distance of trapezoidal fuzzy
numbers. Proceedings of the 9th Joint Conference on Information
Sciences,.
[12] Lv, Y. Q. and C. K. M. Lee Fuzzy hierarchical clustering based on fuzzy
dissimilarity. Industrial Engineering and Engineering Management
(IEEM), 2011 IEEE International Conference on, IEEE.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54649", author = "Y. Q. Lv and C.K.M. Lee", title = "Fuzzy Hierarchical Clustering Applied for Quality Estimation in Manufacturing System", abstract = "This paper develops a quality estimation method with
the application of fuzzy hierarchical clustering. Quality estimation is
essential to quality control and quality improvement as a precise
estimation can promote a right decision-making in order to help
better quality control. Normally the quality of finished products in
manufacturing system can be differentiated by quality standards. In
the real life situation, the collected data may be vague which is not
easy to be classified and they are usually represented in term of fuzzy
number. To estimate the quality of product presented by fuzzy
number is not easy. In this research, the trapezoidal fuzzy numbers
are collected in manufacturing process and classify the collected data
into different clusters so as to get the estimation. Since normal
hierarchical clustering methods can only be applied for real numbers,
fuzzy hierarchical clustering is selected to handle this problem based
on quality standards.", keywords = "Quality Estimation, Fuzzy Quality Mean, Fuzzy
Hierarchical Clustering, Fuzzy Number, Manufacturing system", volume = "6", number = "4", pages = "789-4", }