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

doi:10.4028/www.scientific.net/MSF.880.128

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 proceeding › Conference contribution

8 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 language	English
Title of host publication	4th Asia Conference on Mechanical and Materials Engineering
Publisher	Trans Tech Publications Ltd
Pages	128-131
Number of pages	4
ISBN (Print)	9783038357445
DOIs	https://doi.org/10.4028/www.scientific.net/MSF.880.128
Publication status	Published - 01-01-2017
Event	4th Asia Conference on Mechanical and Materials Engineering, ACMME 2016 - Kuala Lumpur, Malaysia Duration: 14-07-2016 → 18-07-2016

Publication series

Name	Materials Science Forum
Volume	880
ISSN (Print)	0255-5476

Conference

Conference	4th Asia Conference on Mechanical and Materials Engineering, ACMME 2016
Country/Territory	Malaysia
City	Kuala Lumpur
Period	14-07-16 → 18-07-16

All Science Journal Classification (ASJC) codes

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

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10.4028/www.scientific.net/MSF.880.128

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

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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.",

author = "Shettigar, {Arun Kumar} and Subramanya Prabhu and Rashmi Malghan and Shrikantha Rao and Mervin Herbert",

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Shettigar, AK, 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. Materials Science Forum, vol. 880, Trans Tech Publications Ltd, pp. 128-131, 4th Asia Conference on Mechanical and Materials Engineering, ACMME 2016, Kuala Lumpur, Malaysia, 14-07-16. https://doi.org/10.4028/www.scientific.net/MSF.880.128

Application of neural network for the prediction of tensile properties of friction stir welded composites. / Shettigar, Arun Kumar; Prabhu, Subramanya; Malghan, Rashmi et al.

4th Asia Conference on Mechanical and Materials Engineering. Trans Tech Publications Ltd, 2017. p. 128-131 (Materials Science Forum; Vol. 880).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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

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