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IJAT Vol.14 No.3 pp. 369-379
doi: 10.20965/ijat.2020.p0369
(2020)

Paper:

Impact of Model Complexity in the Monitoring of Machine Tools Condition Using Volumetric Errors

Kanglin Xing, J. R. R. Mayer, and Sofiane Achiche

Department of Mechanical Engineering, Polytechnique Montréal
2500 Chemin de Polytechnique, Montréal, Québec H3T 1J4, Canada

Corresponding author

Received:
August 17, 2019
Accepted:
October 7, 2019
Published:
May 5, 2020
Keywords:
machine tool, condition monitoring, volumetric error, SAMBA method
Abstract

The scale and master ball artefact (SAMBA) method allows estimating the inter- and intra-axis error parameters as well as volumetric errors (VEs) of a five-axis machine tool by using simple ball artefacts and the machine tool’s own touch-trigger probe. The SAMBA method can use two different machine error models named after the number of model parameters, i.e., the “13” and “84” machine error models, to estimate the VEs. In this study, we compare these two machine error models when using VE vector directions and values for monitoring the machine tool condition for three cases of machine malfunctions: 1) a C-axis encoder fault, 2) an induced X-axis linear positioning error, and 3) an induced straightness error simulated fault. The results show that the “13” machine error model produces more focused concentrated VE directions but smaller VE values when compared with the “84” machine error model; furthermore, although both models can recognize the three faults and are effective in monitoring the machine tool condition, the “13” machine error model achieves a better recognition rate of the machine condition. This paper provides guidelines for selecting machine error models for the SAMBA method when using VEs to monitor the machine tool condition.

Cite this article as:
K. Xing, J. Mayer, and S. Achiche, “Impact of Model Complexity in the Monitoring of Machine Tools Condition Using Volumetric Errors,” Int. J. Automation Technol., Vol.14, No.3, pp. 369-379, 2020.
Data files:
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Last updated on Dec. 01, 2020