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IJAT Vol.7 No.4 pp. 401-409
doi: 10.20965/ijat.2013.p0401
(2013)

Paper:

Surface Roughness Control Based on Digital Copy Milling Concept to Achieve Autonomous Milling Operation

Toshihiko Hirooka*1, Tomokazu Kobayashi*2, Atsushi Hakotani*3,
Ryuta Sato*1, and Keiichi Shirase*1

*1Graduate School of Engineering, Kobe University, 1-1 Rokko-dai, Nada, Kobe 657-8501, Japan

*2Nissan Motor Co., Ltd., 1-1 Morinosato-aoyama, Atsugi, Kanagawa 243-0123, Japan

*3Kawasaki Heavy Industries Ltd., 1-1 Kawasaki-cho, Akashi, Hyogo 673-8666, Japan

Received:
February 6, 2013
Accepted:
May 31, 2013
Published:
July 5, 2013
Keywords:
autonomous and intelligent machine tool, Digital Copy Milling (DCM), tool motion control, surface roughness control, finishing
Abstract

An autonomous and intelligent machine tool that performs machining operations by referring to CAD product data was developed in our previous study to solve fundamental issues with the conventional command method, which uses NC programs. A system, Digital Copy Milling (DCM), digitizing the principle of copymilling, was developed to generate tool paths during milling operations for dynamic tool motion control. In the DCM, the cutting tool is controlled dynamically to follow the surface of a CAD model corresponding to product shape, eliminating the need for the preparation of NC programs. Active tool motion controls were also realized to enhance the function of DCM. In this study, surface roughness control of the finished surface is realized as an additional enhanced function of DCM to achieve autonomous milling operations. This function allows the DCM to select cutting conditions and generates tool paths dynamically to produce the desired surface roughness: from rough, through semi-finished, to finished. The verification experiment is successfully carried out.

Cite this article as:
T. Hirooka, T. Kobayashi, A. Hakotani, <. Sato, and K. Shirase, “Surface Roughness Control Based on Digital Copy Milling Concept to Achieve Autonomous Milling Operation,” Int. J. Automation Technol., Vol.7, No.4, pp. 401-409, 2013.
Data files:
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Last updated on Dec. 05, 2019