Abstract

The thermal behavior of a machine tool is an important indicator for the grade of production accuracy and indirectly for the market success. The load-dependent temperature distribution and the resulting deformation of the machine tool are influenced by a variety of design and thermo-technical parameters. The main spindle of a machine tool is, without any doubt, the major heat source within the machine structure. The object of the scientific investigation presented in this article is the development of an approach to robust compensation of thermo-dependent machine tool deformations due to spindle load in consideration of rough machining. The focus of the work concentrates on the identification of the model with the highest compensation performance. The underlying concept for the compensation of thermo-dependent machine tool deformations is the indirect approach by using the speed and the effective power of the main spindle for the calculation of the Tool Center Point (TCP) displacement. The presented modeling approach requires the knowledge of the TCP displacement in X-, Y- and Z-direction depending on the speed and the effective power of the main spindle. As a tool for modeling the thermo-dependent behavior of a milling machine, a load test rig for repeatable, defined long-term loading of the main spindle has been developed. It simulates the cutting force depending on the spindle speed and the torque and applies load to the main spindle. The spindle speed and the spindle effective power can be taken directly from the numerical control of the machine tool. An important advantage of the presented compensation method is the fact that it does not require any external sensors. The displacement of the TCP has to be measured, but only during modeling. The relationship between the speed/power of the main spindle as a cause and the displacement of the TCP in X-, Y- and Z-direction as an effect can be determined by a transfer function. This paper compares the compensation results depending on the transfer function and identifies the model with the best compensation performance. The validation of the compensation method is executed by using the example of two different speed and power spectra of the main spindle.

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