Abstract
This paper presents some results on geometrical error compensation using multilayer neural networks (NNs). It is the objective to attain higher compensation performance with less or comparable memory, using this approach. There are three main contributions. First, multilayer NNs are used to approximate the components of geometrical errors. This results in a significantly less number of neurons compared to the use of radial basis functions (RBFs). Secondly, the direction of motion is considered in the compensator. This is important as the geometrical errors can be quite distinct depending on the direction of motion due to backlash and other nonlinearities in the servo systems. Thirdly, the Abbe error is explicitly addressed in the compensator.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tan, K.K., Huang, S.N., Seet, H.L.: Geometrical Error Compensation of Precision Motion Systems Using Radial Basis Functions. IEEE Trans. on Instrument and Measurement 49, 984–990 (2000)
Love, W.J., Scarr, A.J.: The Determination of the Volumetric Accuracy of Multi Axis Machines. In: The 14th MTDR, pp. 307–315 (1973)
Bush, K., Kunzmann, H., Waldele, F.: Numerical Error Correction of Co-Ordinate Measuring Machines. In: Proc. Int. Symp. on Metrology for Quality Control in Production, Tokyo, pp. 270–282 (1984)
Hayati, S.A.: Robot Arm Geometric Link Parameter Estimation. In: Proceedings of the 22nd IEEE Conference on Decision and Control, pp. 1477–1483 (1983)
Veitschnegger, W.K., Wu, C.H.: Robot Accuracy Analysis Based On Kinematics. IEEE Journal of Robotics and Automation RA-2/3, 171–179 (1986)
Barron, A.R.: Approximation and Estimation Bounds for Artificial Neural Networks. In: Proc. 4th Ann. Workshop on Computational Learning Theory, San Mateo, pp. 243–249 (1991)
Zhang, G., Veale, R., Charlton, T., Hocken, R., Borchardt, B.: Error compensation of coordinate measuring machines. Annals of the CIRP 34, 445–448 (1985)
Duffie, N.A., Maimberg, S.J.: Error Diagnosis and Compensation Using Kinematic Models and Position Error Data. Annals of the CIRP 36, 355–358 (1987)
Weekers, W.G., Schellekens, P.H.J.: Assessment of Dynamic Errors Of CMMs for Fast Probing. Annals of the CIRP 44, 469–474 (1995)
British Standard: Coordinate Measuring Machines, Part 3. Code of practice, BS 6808, U.K. (1989)
Haykin, S.: Neural networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, K.K., Huang, S., Prahlad, V., Lee, T.H. (2005). Geometrical Error Compensation of Gantry Stage Using Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_142
Download citation
DOI: https://doi.org/10.1007/11427469_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)