Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/7376
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dc.contributor.authorShettigar, A.K.
dc.contributor.authorPrabhu, S.
dc.contributor.authorMalghan, R.
dc.contributor.authorRao, S.
dc.contributor.authorHerbert, M.
dc.date.accessioned2020-03-30T09:58:57Z-
dc.date.available2020-03-30T09:58:57Z-
dc.date.issued2017
dc.identifier.citationMaterials Science Forum, 2017, Vol.880, , pp.128-131en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7376-
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.en_US
dc.titleApplication of neural network for the prediction of tensile properties of friction stir welded compositesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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