Modelling and characterisation of geometric errors on 5-axis machine-tool
This research work deals with the geometric modelling of 5-axis machine tool based on a standardised parameterisation of geometric errors with the aim to decrease the volumetric error in the workspace. The identification of the model’s parameters is based on the development of a new standard thermo-...
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Published in | Mechanics & industry : an international journal on mechanical sciences and engineering applications Vol. 20; no. 6; p. 605 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Villeurbanne
EDP Sciences
2019
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Subjects | |
Online Access | Get full text |
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Summary: | This research work deals with the geometric modelling of 5-axis machine tool based on a standardised parameterisation of geometric errors with the aim to decrease the volumetric error in the workspace. The identification of the model’s parameters is based on the development of a new standard thermo-invariant material namely the Multi-Feature Bar. Thanks to its calibration and a European intercomparison, it now provides a direct metrological traceability to the SI metre for dimensional measurement on machine tool in a hostile environment. The identification of three intrinsic parameters of this standard, coupled with a measurement procedure ensures a complete and traceable identification of motion errors of linear axes. An identification procedure of location and orientation errors of axes is proposed by probing a datum sphere in the workspace and minimising the time drift of the structural loop and the effects of the previously identified motion errors. Finally, the developed model partially identified, allows the characterisation of 95% of the measured volumetric error. Therefore, the mean volumetric error not characterised by the model only amounts to 8 μm. |
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Bibliography: | publisher-ID:mi190006 href:https://www.mechanics-industry.org/articles/meca/abs/2019/06/mi190006/mi190006.html ark:/67375/80W-N0PN3V14-9 istex:03B9D2EAEE3E74F50143F00E0E68784DB6575D04 dkey:10.1051/meca/2019034 |
ISSN: | 2257-7777 2257-7750 |
DOI: | 10.1051/meca/2019034 |