Distribution & Access For Publication Downloads News About Us Contact Us
Paper Titles
Modeling the Property Variation of Diamond Composite and its Impact on the Reliability of Cutting Tools
p.448
Cutting Forces and Tool Wear in Dry Milling of Ti6Al4V
p.454
Development of Al2O3/TiCN Ceramic Composite Cutting Tool and Research on its Milling Performance
p.460
Ball-End Milling of Cr12MoV Die Steel Using Ceramic Tool and Cements Carbide Tool
p.466
An End-Milling Condition Decision Support System Using Data-Mining for Difficult-to-Cut Materials
p.472
Research on the Drilling Temperature Field Model of the Unidirectional Carbon Fiber Epoxy Composites
p.478
Chip Formation Characteristics in High-Speed Machining of Hardened AISI1045 Steel
p.484
Mechanical Behavior of Single Crystal Copper for Different Shearing Directions
p.490
Machinability Study on Hard Milling of Ultra-High Strength Steel 30Cr3SiNiMoVA
p.496

An End-Milling Condition Decision Support System Using Data-Mining for Difficult-to-Cut Materials

Article Preview

Abstract:

We proposed the data-mining methods using hierarchical and non-hierarchical clustering methods to help engineers decide appropriate end-milling conditions. The aim of our research is to construct a system that uses clustering techniques and tool catalog data to support the decision of end-milling conditions for difficult-to-cut materials. We used variable cluster analysis and the K-means method to find tool shape parameters that had a linear relationship with the end-milling conditions listed in the catalog. We used the response surface method and significant tool shape parameters obtained by clustering to derive end-milling condition. Milling experiments using a square end mill under two sets of end-milling conditions (conditions derived from the end-milling condition decision support system and conditions suggested by expert engineers) for difficult-to-cut materials (austenite stainless steel) showed that catalog mining can be used to derive guidelines for deciding end-milling conditions.

You might also be interested in these eBooks
Advances in Abrasive Technology XV View Preview

Info:

Periodical:

Advanced Materials Research (Volume 565)

Pages:

472-477

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.565.472

Citation:

Cite this paper

Online since:

September 2012

Authors:

Hiroyuki Kodama, Masatoshi Shindou, Toshiki Hirogaki, Eiichhi Aoyama, Keiji Ogawa

Keywords:

Catalog Data, Data Mining (DM), Difficult-to-Cut Material, End-Milling, Hierarchical Clustering, JIS SUS310S, Non-Hierarchical Clustering, Response Surface Method (RSM)

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] B. Ozcelik et al: Int. J. of Advanced Manufacturing Technology, Vol. 27, (2005), pp.234-241.

Google Scholar

[2] M. Rahman et al: JSME International Journal, Series C, Vol. 49, No. 1 (2006), pp.11-20.

Google Scholar

[3] P. Huang et al: Int. J. of Advanced Manufacturing Technology, Vol. 55, (2011), pp.153-160.

Google Scholar

[4] Y. Yamane et al: J. of Precision Engineering, Vol. 70, No. 3, (2004), pp.407-411 (in Japanese).

Google Scholar

[5] H. Kodama et al: Int. J. of Automation Technology, Vol. 6, No. 1 (2012), pp.61-74.

Google Scholar

[6] H. Kodama et al: Advanced Materials Research, Vol. 325, (2011), pp.345-350.

Google Scholar

Scientific.Net is a registered brand of Trans Tech Publications Ltd
© 2024 by Trans Tech Publications Ltd. All Rights Reserved