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Proceedings Paper

Experimental analysis and modeling of the dynamic performance of machine tool spindle-bearing systems
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Paper Abstract

In this paper, an analysis of the dynamic characteristics of machine tool spindle-bearing systems is presented. The research utilized the force impact-response testing method. The results are applied to the analysis and modeling of the dynamic performance of machine tool spindle-bearing systems. As an indicator of dynamic performance, the impulse response matrices are experimentally obtained. Two types of impulse response matrices are considered: (1) with respect to (wrt) acceleration; which describes the space-coupled relationship between the vectors of the force (impact) and measured acceleration (response) and (2) wrt displacement; which describes the space-coupled relationship between vectors of the force and simulated displacement. The results indicate an interrelation between different directions of displacements, and lay a foundation for the dynamic modeling of spindle-bearing systems in view of the transfer matrix with nonzero non-diagonal elements. From an engineering point of view, the transfer function matrix can be considered a `dynamic imprint', or `signature' of system performance. As a practical example, the dynamic properties (the impulse and frequency response matrices) of the spindle-bearing system of a Barer-Proteo D/94 high precision machining center are obtained, identified and investigated. The developed approach for modeling and parameter identification appears promising for a wide range of industrial applications, including rotary systems.

Paper Details

Date Published: 2 February 2001
PDF: 12 pages
Proc. SPIE 4191, Sensors and Controls for Intelligent Manufacturing, (2 February 2001); doi: 10.1117/12.417240
Show Author Affiliations
Evgueni V. Bordatchev, National Research Council Canada (Canada)
Peter E. Orban, National Research Council Canada (Canada)
Adam Rehorn, Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 4191:
Sensors and Controls for Intelligent Manufacturing
Peter E. Orban; George K. Knopf, Editor(s)

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