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

Arun Kumar Shettigar, Subramanya Prabhu, Rashmi Malghan, Shrikantha Rao, Mervin Herbert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In 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.

Original languageEnglish
Title of host publication4th Asia Conference on Mechanical and Materials Engineering
PublisherTrans Tech Publications Ltd
Pages128-131
Number of pages4
ISBN (Print)9783038357445
DOIs
Publication statusPublished - 01-01-2017
Event4th Asia Conference on Mechanical and Materials Engineering, ACMME 2016 - Kuala Lumpur, Malaysia
Duration: 14-07-201618-07-2016

Publication series

NameMaterials Science Forum
Volume880
ISSN (Print)0255-5476

Conference

Conference4th Asia Conference on Mechanical and Materials Engineering, ACMME 2016
CountryMalaysia
CityKuala Lumpur
Period14-07-1618-07-16

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All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

Shettigar, A. K., Prabhu, S., Malghan, R., Rao, S., & Herbert, M. (2017). Application of neural network for the prediction of tensile properties of friction stir welded composites. In 4th Asia Conference on Mechanical and Materials Engineering (pp. 128-131). (Materials Science Forum; Vol. 880). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/MSF.880.128