Machine tool positioning error compensation using artificial neural networks

Authors:
John M. Fines;Arvin Agah
Affiliations:
Honeywell FM&T, Kansas City, MO 64141, USA;Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66049, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

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Abstract

This paper is a study of the application of artificial neural networks to the problem of calculating error compensation values for axis positioning on a machine tool. The primary focus is on the development of a neural network-based system that could be implemented and integrated into the open architecture control system of an actual machine. A number of neural network architectures were examined for applicability to the problem and one was selected and implemented on the actual machine. Positioning error compensation capabilities were evaluated using industry standard equipment and procedures, and the results obtained were compared with the capabilities of standard error compensation routines in machine tool controls.