Monitoring of slowly progressing deterioration of computer numerical control machine axes

Author:

Uhlmann E1,Geisert C1,Hohwieler E2

Affiliation:

1. Fraunhofer Institute for Production Systems and Design Technology (IPK), Berlin, Germany

2. Department of Manufacturing Technology, Technische Universität Berlin, Berlin, Germany

Abstract

The feed axes of computer numerical control (CNC) grinding machine tools are among the most mechanically stressed components of machine tools owing to the high process forces and rough manufacturing environment which they encounter. The resulting wear and tear depends strongly on the product range and the manner of machine operation. To counteract a functional deficiency of these central machine units, the current usual approach is preventive maintenance. The manual inspection of feed axes is complex and time consuming. A complicating matter is that the deterioration normally progresses very slowly and depends on the position of the stress along the axis. Existing approaches to automated estimation of the ‘health status’ of feed axes do not take this factor into account. This paper presents a procedure that addresses this gap. During simple test routines, the drive current, axis position, and feed rate are recorded. With the help of additional machine data, characteristic values are computed directly at the computer of the human—machine interface (HMI). The results are then transferred to and stored on a database server at the machine manufacturer. This approach enables the service technician to trace the progression of the axes’ ‘health status’ over a long time. This approach makes it possible to detect trends in the characteristic values at an early point in time. This leads to a better planning of necessary maintenance actions adapted to the remaining lifetime of the wearing component.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Reference28 articles.

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2. Lee J., Abujamra R., Jardine A. K. S., Lin D., Banjevec D. An integrated platform for diagnostics, prognostics and maintenance optimization. Proceedings of the Intelligent Maintenance Systems 2004, 15–27 July 2004, Arles, France.

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