Investigation of End-Milling Condition Decision Methodology Based on Data Mining for Tool Catalog Database
Hiroyuki Kodama*, Toshiki Hirogaki**, Eiichi Aoyama**,
and Keiji Ogawa***
*Graduate School of Engineering, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan
**Faculty of Science and Engineering, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan
***Department of Mechanical Systems Engineering, The University of Shiga Prefecture, 2500 Hassaka-cho, Hikone-shi, Shiga 522-8533, Japan
Data mining supports decision making about reasonable end-milling conditions. Our research objective is to excavate new knowledge with mining effect by applying data mining techniques to a tool catalog. We use hierarchical and nonhierarchical clustering data mining with catalog data by applying multiple regression analysis and focusing on the catalog data shape element. We visually grouped end-mills on the basis of tool shape, considering the ratio of tool shape dimensions, by employing the K-means method. We found that factors related to blade length and full length ratio are effective in for making end-milling condition decisions. These factors have not previously been singled out through background knowledge or expert knowledge, but they were noticed as a data mining effect.
and Keiji Ogawa, “Investigation of End-Milling Condition Decision Methodology Based on Data Mining for Tool Catalog Database,” Int. J. Automation Technol., Vol.6, No.1, pp. 61-74, 2012.
-  U. Fayyad et al., “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, pp. 37-54, 1996.
-  R. Jonathan, M. Hosking et al., “A Statistical Perceptive on Data Mining,” Future Generation Computer Systems, Vol.13, pp. 117-134, 1997.
-  G. Clark et al., “Statistical Inference and Data Mining,” Communications of the ACM, Vol.39, No.11, pp. 35-41, 1996.
-  G. Christine and D. Alan, “Knowledge Discovery from Industrial Databases,” J. of Intelligent Manufacturing, Vol.15, pp. 29-37, 2004.
-  O. Keiji, H. Toshiki, A. Eiichi, I. Tadayuki, N. Hiromichi, and Y. Katsutoshi, “Improving Machining Quality Using Data Mining (Application to Micro-Drilling of PWBS),” Procs. of JUSFA2004, JL007, pp. 1-6, 2004.
-  H. Toshiki, A. Eiichi, O. Keiji, and M. Kenichi, “Application of Data Mining to Factor Analysis of Micro-Drilled Hole Quality for Multi-Layer PWBS,” Procs. of IDETC/CIE2005, pp. 189-196, 2005.
-  O. Keiji, H. Toshiki, A. Eiichi, M. Shinji, I. Hisahiro, and K. Tsutao, “DataMining of Factors Affecting Circuit Connection Reliability on Laser-Drilled Micro Blind via Holes in Multi-Layer PWBs,” JSME Int. J., Series A, Vol.49, No.4, pp. 522-528, 2006.
-  O. Keiji, H. Toshiki, A. Eiichi, O. Miho, and K. Tasuku, “Proposal on Decision Methodology of End-Milling Conditions Using Data Mining,” Procs. of ISFA2008, JL035, pp. 1-7, 2008.
-  M. Tukamoto et al., “Concept Formation Model Using Heuristics,” Trans. of Information Processing Society of Japan, 1377, 1988.
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