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IJAT Vol.10 No.3 pp. 447-454
doi: 10.20965/ijat.2016.p0447
(2016)

Paper:

Machine Bed Support with Sliding Surface for Improving the Motion Accuracy

Yusaku Shirahama, Ryuta Sato, Yusuke Takasuka, Hidenori Nakatsuji, and Keiichi Shirase

Department of Mechanical Engineering, Kobe University
1-1 Rokko-dai, Nada, Kobe 657-8501, Japan

Corresponding author, E-mail: 158t329t@stu.kobe-u.ac.jp

Received:
February 1, 2016
Accepted:
April 1, 2016
Published:
May 2, 2016
Keywords:
NC machine tools, machine bed, motion accuracy, tracking motion, vibration, mathematical model
Abstract
The purpose of this study is to develop a new machine bed support mechanism for reducing the vibration generated during the high-speed tracking motion of numerical control machine tools. In order to achieve this, the frequency response and motion trajectory of a machine tool with the proposed machine bed, which has a sliding surface, are measured and compared with that of the conventional support. Based on the modal analysis of the machine tool structure, a mathematical model representing the influence of the machine bed characteristics on the vibration is also developed. The model consists of a bed, saddle, table, column, and spindle head. Every component has three degrees of freedom for each of the translational and rotational axes. In order to evaluate the characteristics of the machine bed, the mathematical model determines the stiffness and damping along the X-, Y-, and Z-axis between the bed and the ground. The frequency response curves simulated by using the mathematical model are compared with that of the measured ones. From the results of the experiments and simulations, it is confirmed that the vibration generated during high-speed tracking motions can be reduced by using the proposed machine bed with a sliding surface.
Cite this article as:
Y. Shirahama, R. Sato, Y. Takasuka, H. Nakatsuji, and K. Shirase, “Machine Bed Support with Sliding Surface for Improving the Motion Accuracy,” Int. J. Automation Technol., Vol.10 No.3, pp. 447-454, 2016.
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