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JRM Vol.12 No.6 pp. 706-711
doi: 10.20965/jrm.2000.p0706
(2000)

Paper:

Bolt Tightening Using Impact Wrench Based on Neural Networks

Toru Fujinaka, Hirofumi Nakano, Michifumi Yoshioka, and Sigeru Omatu

Division of Computer and Systems Science, Graduate School of Engineering Osaka Prefecture University, 1-1 Gakuen-cho, sakai, Osaka 599-8531, Japan

Received:
Octeober 23, 2000
Accepted:
December 8, 2000
Published:
December 20, 2000
Keywords:
Impact wrench, Neural network, Back propagation method, Clamping force
Abstract

A method for controlling the tightening operation of bolts using an impact wrench is proposed, where the neural network is employed for achieving proper clamping force. The characteristics of the clamping force depend on the kind of work to which bolts are tightened, thus a neural network is used for classification of the work. The clamping force, which can only be measured during the test run, is estimated online, using another neural network. Then appropriate input to the actuator of the impact wrench is determined, based on the estimated value of the clamping force.

Cite this article as:
Toru Fujinaka, Hirofumi Nakano, Michifumi Yoshioka, and Sigeru Omatu, “Bolt Tightening Using Impact Wrench Based on Neural Networks,” J. Robot. Mechatron., Vol.12, No.6, pp. 706-711, 2000.
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