Paper Info
Title
An RBF neural network approach to geometric error compensation with displacement measurements only.
Abstract
A novel radial basis function (RBF) neural network-based geometric error compensation method with displacement measurements only is proposed in this paper. The individual geometric error components are formulated mathematically based on laser interferometer calibration with displacement measurements only and modeled using RBF neural network for error compensation in motion controller. Only 4 and 15 displacement measurements are required to identify the error components for XY and XYZ table, respectively. The experiment results on two XY tables illustrate the effectiveness of the proposed method. The overall errors can be reduced significantly after compensation, and different data intervals can be selected to reduce calibration time but maintain a high level of accuracy. The proposed methodology can be extended to other types of precision machine and is more suitable for precision machines requiring a relative low level of accuracy, but fast calibration like those used for acceptance testing and periodic checking.
Year
DOI
Venue
2017
10.1007/s00521-016-2486-2
Neural Computing and Applications
Keywords
Field
DocType
Radial basis function network, Error estimation, Error compensation, Precision machine
Radial basis function network,Radial basis function,Computer science,Control theory,Interferometry,Acceptance testing,Motion controller,Artificial neural network,Periodic graph (geometry),Calibration
Journal
Volume
Issue
ISSN
28
6
1433-3058
Citations 
PageRank 
References 
0
0.34
8
Authors
9
Name
Order
Citations
PageRank
Rui Yang1243.00
Kok Kiong Tan292399.57
Arthur Tay3489.53
Su-Nan Huang450561.65
Jie Sun512.78
Jerry Y. H. Fuh6629.50
Yoke San Wong710511.96
Chek-Sing Teo85412.65
Zidong Wang911003578.11