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Vibratory assembly of prismatic parts using neural networkbased positioning error estimation

Published online by Cambridge University Press:  09 March 2009

E. S. Kang  and
H. S. Cho
E. S. Kang
Affiliation:
Department of Precision Engineering and Mechatronics, Korean Advanced Institute of Science and Technology, 373–1, Kusong-dong, Yusong-gu, Taejon, 305–701 (Korea)
H. S. Cho
Affiliation:
Department of Precision Engineering and Mechatronics, Korean Advanced Institute of Science and Technology, 373–1, Kusong-dong, Yusong-gu, Taejon, 305–701 (Korea)
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Summary

Despite its known effectiveness, a typical vibratory assembly method tends to generate adverse impact forces between mating parts commensurate with the relatively large vibratory motion required for reliably compensating positioning errors of arbitrary magnitude. To this end, this paper presents a neural network-based vibratory assembly method with its emphasis on reducing the mating forces for chamferless prismatic parts. In this method, the interactive force is effectively suppressed by reducing the amplitude of vibratory motion, while the greater part of the relative positioning error is estimated and compensated by a neural network. The estimation performance of the neural network and the overall performance of the assembly method are evaluated experimentally. Experimental results show that the assembly is efficiently accomplished with small reaction forces, and the possible insertion error range is also expanded

Keywords

Vibratory assemblyPrismatic partsNeuralnetworksError estimation
Type
Articles
Information
Robotica , Volume 13 , Issue 2 , March 1995 , pp. 185 - 193
Copyright
Copyright © Cambridge University Press 1995

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References

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