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IJAT Vol.13 No.1 pp. 149-156
doi: 10.20965/ijat.2019.p0149
(2019)

Paper:

Utilization of Multi-Axis Positioning Repeatability Performance in Kinematic Modelling

Károly Szipka and Andreas Archenti

KTH Royal Institute of Technology
Brinellvägen 68, 10044 Stockholm, Sweden

Corresponding author

Received:
November 11, 2017
Accepted:
August 31, 2018
Published:
January 5, 2019
Keywords:
machine tool repeatability, uncertainty estimation, kinematic modelling
Abstract

Detailed description of the multi-axis repeatability performance and the modelling of non-systematic variations in the positioning performance of machine tools can support the understanding of root-causes of capability variations in manufacturing processes. Kinematic characterization is implemented through repeated measurements, which include variations related to the performance of the machine tool. This paper addresses the integration of the positional repeatability in kinematic modelling through the employment of direct measurement results. The findings of this research can be used to develop standardized approaches. The statistical population of random errors along the multi-axis travel first requires the proper management of experimental data. In this paper a methodology and its application are presented for the determination of repeatability under static and unloaded conditions as an inhomogeneous parameter in the work space. The proposed approach is demonstrated in a case study, where the component errors of a linear axis are investigated with repeated laser interferometer measurements to quantify the estimated repeatability and express it in the composed repeatability budget. The conclusions of the proposed methodology outline the sensitivity of kinematic models relying on measurement data, as the repeatability of the system can be in the same magnitude as the systematic errors.

Cite this article as:
K. Szipka and A. Archenti, “Utilization of Multi-Axis Positioning Repeatability Performance in Kinematic Modelling,” Int. J. Automation Technol., Vol.13, No.1, pp. 149-156, 2019.
Data files:
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Last updated on Apr. 23, 2019