J. Mou, A METHOD OF USING NEURAL NETWORKS AND INVERSE KINEMATICS FOR MACHINE-TOOLS ERROR ESTIMATION AND CORRECTION, Journal of manufacturing science and engineering, 119(2), 1997, pp. 247-254
A method using artificial neural networks and inverse kinematics for m
achine tool error correction is presented. A generalized error model i
s derived, by using rigid body kinematics, to describe the error motio
n between the cutting tool and workpiece at discrete temperature condi
tions. Neural network models are then built to track the time-varying
machine tool errors at various thermal conditions. The output Of the n
eural network models can. be used to periodically modify, using invers
e kinematics technique, the error model's coefficients as the cutting
processes proceeded. Thus, the time-varying positioning errors at othe
r points within the designated workspace can be estimated. Experimenta
l results show that the time-varying machine tool errors can be estima
ted and corrected with desired accuracy. The estimated errors resulted
from the proposed methodology could be used to adjust the depth of cu
t on the finish pass, or correct the probing data for process-intermit
tent inspection to improve the accuracy of workpieces.