Visual analysis of quality-related manufacturing data using fractal geometry
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.
KeywordsData mining Sequence mining Quality engineering Chaos game representation Iterated function system
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- 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.Google Scholar
- Barnsley M. (1988) Fractals everywhere. Academic Press, BostonGoogle Scholar
- Barnsley M., Hurd L. P. (1993) Fractal image compression. A. K. Peters, BostonGoogle Scholar
- 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.Google Scholar
- Hand D. (1998) Data Mining—Reaching beyond statistics. Research in Official Statistics 1(2): 5–17Google Scholar
- Keim D. A. (2002) Information visualization and visual data mining. IEEE Transactions of Visualization and Computer Graphics 7(1): 100–107Google Scholar
- Quinlan J. R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San MateoGoogle Scholar
- 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.Google Scholar