Investigation of Machining Condition for Barrel End Mill Based on Data-Mining Method for Tool Catalog Database

Abstract:

The recent development of computer-aided design/computer-aided manufacturing (CAD/CAM) systems has enabled unskilled workers to generate NC programs easily. However, determining the cutting conditions, which is crucial for machining, still relies on the knowledge and experience of the skilled workers. Therefore, this study aimed to discover tacit knowledge about cutting using data mining methods and construct a system to support unskilled workers. Given the recent progress in the practical use of barrel tools, this study attempts to predict the cutting conditions of barrel tools by utilizing catalog information on radius and ball end mills. First, the databases of all the tools were integrated. Next, new variables were introduced for highly accurate predictions. After verifying the validity of the new variables through cutting experiments, they were used to predict cutting conditions. It was found that the new variables could be used in the clustering process to achieve highly accurate predictions.

• Periodical: Key Engineering Materials (Volume 1008)
• Pages: 49-55
• DOI: https://doi.org/10.4028/p-68Ypy6
• Online since: March 2025
• Authors: Shu Uchida*, Natsuki Oyaizu, Masao Nakagawa, Toshiki Hirogaki, Eiichi Aoyama
• Keywords: Barrel Tool, Data Mining, K-Means, Machine Learning, Radius End Mill, Random Forest

References

[1] Kawakami, S., and Morishige, K., Development of Tool Path Interpolation Method for Five-Axis Control Machining with Barrel Tool, Journal of the Japan Society for Precision Engineering, Vol. 89, No. 11 (2023), pp.850-856(in Japanese).

DOI: 10.2493/jjspe.89.850

[2] Lee, M. H., Knowledge-based factory, Artificial Intelligence in Engineering, Vol.8 (1993), pp.109-125．

[3] Piatetsky-Shapiro, G., The Data Mining Industry Coming of Age, IEEE Intelligent Systems and their Applications Vol.14, No. 6, (1999), pp.32-34．

DOI: 10.1109/5254.809566

[4] Kodama, H., Hirogaki, T., Aoyama, E., Ogawa, K., Determination of end milling conditions using data mining, Journal of the Japan Society for Abrasive Technology, Vol. 56, No. 12 (2011), pp.42-47 (in Japanese).

[5] Kodama, H., Hirogaki, T., Aoyama, E. and Ogawa, K., Proposal of ball end-milling condition decision methodology using data-mining from tool catalog data, Journal of Japan Society for Precision Engineering, Vo.79, No.10 (2013), pp.964-969 (in Japanese).

DOI: 10.2493/jjspe.79.964

[6] Horii, R., Sasahara, H., and Endo, T., Cutting Characteristics of Mold Steel Using Barrel-Shaped End Mills, Proceedings of the 25th Graduation Research Presentation Conference for Student Members of the Japan Society for Precision Engineering (2018), pp.102-102 (in Japanese).

[7] E. Artetxea, G. Urbikaina, A. Lamikiza, L. N. López-de-Lacallea, R. Gonzálezb, P. Rodalc, A mechanistic cutting force model for new barrel end mills, Procedia Engineering, Vol.132, (2015), pp.553-560.

DOI: 10.1016/j.proeng.2015.12.532

[8] David N. Reshef, Yakir A. Reshef, Hilary K. Finucane, Sharon R. Grossman, Gilean McVean, Peter J. Turnbaugh, Eric S. Lander, Michael Mitzenmacher, Pardis C. Sabeti., Detecting novel associations in large data sets, Science, 334(2011), pp.1518-1524.

DOI: 10.1126/science.1205438

[9] Celebi, M. E., Kingravi, H. A., Vela, P. A., A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Systems with Applications, Vol.40, Issue 1 (2013), pp.200-210.

DOI: 10.1016/j.eswa.2012.07.021

[10] Breiman, L., Random Forests, Machine Learning, Vol.45, (2011), pp.5-32.