Bolt Tightening Using Impact Wrench Based on Neural Networks | Zendy

Toru Fujinaka | Zendy; Hirofumi Nakano | Zendy; Michifumi Yoshioka | Zendy; Sigeru Omatu | Zendy

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Bolt Tightening Using Impact Wrench Based on Neural Networks

Author(s) -

Toru Fujinaka,

Hirofumi Nakano,

Michifumi Yoshioka,

Sigeru Omatu

Publication year - 2000

Publication title -

journal of robotics and mechatronics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.257

H-Index - 19

eISSN - 1883-8049

pISSN - 0915-3942

DOI - 10.20965/jrm.2000.p0706

Subject(s) - wrench , clamping , artificial neural network , actuator , work (physics) , computer science , engineering , structural engineering , control theory (sociology) , artificial intelligence , mechanical engineering , control (management)

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.

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