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A robust level error estimation method for machine tool installation

delete2019-07-01
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PRE
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K
Kotaro Mori *
D
Daisuke Kono
A
Atsushi Matsubara
DOI:10.1016/j.precisioneng.2019.04.014delete
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Abstract

Abstract

En 中文
The accuracy of the machine tool is essential for precision manufacturing. Many machines with long bed are often require more than four supports to compensate the deformations of the bed. This leveling and alignment of machine bed guarantees the accuracies of the.machine tool. During the installation of these Machines, it is important to compensate the unevenness of floor at the installation site. This adjustment procedure is called level adjustment. Because of the circular changes, level adjustments are also regularly required to maintain accuracy. In conventional level adjustment methods, level adjustments are performed by skilled installers based on their experience utilizing measurement results with a trial-and-error method. For inexperienced technicians, the task of making a level adjustment in a short time is not easy. Thus, a model-based level adjustment method is demanded. However, the conventional model-based method was not suitable for practical environments. The effect of measurement noise is not considered enough. In this paper, a noise-robust level adjustment method is proposed, that utilizes a model between preload change of supports and leveling. It can estimate level errors from preload changes of supports. The consideration of preload measurement noises allows implementing this system with practical preload sensors. In this paper, the fundamentals of the proposed method are experimentally investigated on a test bench. Then, the feasibility of the method is investigated on a machining center with simulations.
Keywords:
Machine tool
Installation
Level adjustment
Preload measurement
Condition monitoring
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Journal

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Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology
IF:
3.7
Papers:
3.2K
Citations:
8.3K

Organization

K
Kyoto University
Scholars:
5.1W
Papers: 4.6W
Citations: 6.1W
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