Bolt Tightening Using Impact Wrench Based on Neural Networks

Toru Fujinaka; Hirofumi Nakano; Michifumi Yoshioka; Sigeru Omatu

doi:10.20965/jrm.2000.p0706

single-rb.php

« previous

JRM Vol.12 No.6 pp. 706-711

doi: 10.20965/jrm.2000.p0706

(2000)

Paper:

Views over last 60 days: 330

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.

Data files:

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.