Abstract
Improving manufacturing quality is an important challenge in various industrial settings. Data mining methods mostly approach this challenge by examining the effect of operation settings on product quality. We analyze the impact of operational sequences on product quality. For this purpose, we propose a novel method for visual analysis and classification of operational sequences. The suggested framework is based on an Iterated Function System (IFS), for producing a fractal representation of manufacturing processes. We demonstrate our method with a software application for visual analysis of quality-related data. The proposed method offers production engineers an effective tool for visual detection of operational sequence patterns influencing product quality, and requires no understanding of mathematical or statistical algorithms. Moreover, it enables to detect faulty operational sequence patterns of any length, without predefining the sequence pattern length. It also enables to visually distinguish between different faulty operational sequence patterns in cases of recurring operations within a production route. Our proposed method provides another significant added value by enabling the visual detection of rare and missing operational sequences per product quality measure. We demonstrate cases in which previous methods fail to provide these capabilities.
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References
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the international conference on large databases, pp. 478–499.
Barnsley M. (1988) Fractals everywhere. Academic Press, Boston
Barnsley M., Hurd L. P. (1993) Fractal image compression. A. K. Peters, Boston
Ben-Gal I., Shmilovici A., Morag G. (2003) Context-Based statistical process control: A monitoring procedure for state-dependant processes. Technomatrix 45: 293–311
Cavner, W. B., & Trenkle, J. M. (1994). n-gram based text categorization. In Proceedings of the third annual symposium on document analysis and information retrieval, pp. 261–169.
Da Cunha C., Agard B., Kusiak A. (2006) Data mining for improvement of product quality. International Journal of Production Research 44(18–19): 4027–4041
Hand D. (1998) Data Mining—Reaching beyond statistics. Research in Official Statistics 1(2): 5–17
Jeffrey H. J. (1990) Chaos game representation of genetic sequences. Nucleic Acids Research 18: 2163–2170
Keim D. A. (2002) Information visualization and visual data mining. IEEE Transactions of Visualization and Computer Graphics 7(1): 100–107
Quinlan J. R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo
Rokach L., Maimon O. (2006) Data mining for improving the quality of manufacturing: A feature set decomposition approach. Journal of Intelligent Manufacturing 17(3): 285–299
Rokach L. (2008) Mining manufacturing data using genetic algorithm based feature set decomposition. IJISTA 4(1): 57–78
Rokach, L., Romano, R. & Maimon, O. (2008). Mining manufacturing databases to discover the effect of operational sequence on the product quality. Journal of Intelligent Manufacturing.
Weiss C. H. (2008) Visual analysis of categorical time series. Statistical Methodology 5: 56–71
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Ruschin-Rimini, N., Maimon, O. & Romano, R. Visual analysis of quality-related manufacturing data using fractal geometry. J Intell Manuf 23, 481–495 (2012). https://doi.org/10.1007/s10845-010-0387-2
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DOI: https://doi.org/10.1007/s10845-010-0387-2