Application of neural network for the prediction of tensile properties of friction stir welded composites

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dc.contributor.authorShettigar, A.K.
dc.contributor.authorPrabhu B, S.
dc.contributor.authorMalghan, R.
dc.contributor.authorRao, S.S.
dc.contributor.authorHerbert, M.
dc.date.accessioned2026-02-06T06:38:56Z
dc.date.issued2017
dc.description.abstractIn this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error. © 2017 Trans Tech Publications, Switzerland.
dc.identifier.citationMaterials Science Forum, 2017, Vol.880, , p. 128-131
dc.identifier.issn2555476
dc.identifier.urihttps://doi.org/10.4028/www.scientific.net/MSF.880.128
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/31995
dc.publisherTrans Tech Publications Ltd ttp@transtec.ch
dc.subjectComposite
dc.subjectError back propagation
dc.subjectFriction stir welding
dc.subjectNeural network
dc.titleApplication of neural network for the prediction of tensile properties of friction stir welded composites

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