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IJAT Vol.10 No.6 pp. 882-890
doi: 10.20965/ijat.2016.p0882
(2016)

Paper:

Improvement of Machining Performance of Small-Diameter End Mill by Means of Micro- and Nanometer-Scale Textures

Noritaka Kawasegi*1,†, Hiroshi Sugimori*2, Noboru Morita*3, and Toru Sekiguchi*4

*1Central Research Institute, Toyama Industrial Technology Center
150 Futagami-machi, Takaoka-shi, Toyama 933-0981, Japan

Corresponding author,

*2Machinery and Electronics Research Institute, Toyama Industrial Technology Center, Toyama, Japan

*3Graduate School of Engineering, Chiba University, Chiba, Japan

*4NACHI-FUJIKOSHI CORP., Toyama, Japan

Received:
May 9, 2016
Accepted:
August 4, 2016
Published:
November 4, 2016
Keywords:
small-diameter end mill, micrometer- and nanometer-scale texture, femtosecond laser, friction, cutting force, micromachining, Aluminum alloy
Abstract

The purpose of this study is to develop novel cutting tools with micro- or nanoscale textures on their surfaces. Texturing micro- or nanoscale features on a surface allows us to control the tribological characteristics of the tool. For this research, textures were applied to end mills with a diameter of 0.5 mm using a femtosecond laser, and milling experiments were conducted on aluminum alloy to evaluate the developed tools. The applied texture decreased the cutting forces. This effect depends on the shape of the texture: groove textures are more effective for reducing friction and the resultant cutting forces. Periodic textures fabricated through the interference of the laser were effective at reducing the adhesion of the work material. A larger effect was obtained for shallow and large pitch textures. The results indicate that the proposed method is effective at improving the machining performance of small-diameter end mills.

Cite this article as:
N. Kawasegi, H. Sugimori, N. Morita, and T. Sekiguchi, “Improvement of Machining Performance of Small-Diameter End Mill by Means of Micro- and Nanometer-Scale Textures,” Int. J. Automation Technol., Vol.10, No.6, pp. 882-890, 2016.
Data files:
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Last updated on Dec. 18, 2018