Volume No. :   10

Issue No. :  4

Year :  2019

ISSN Print :  0976-2973

ISSN Online :  2321-581X


Registration

Allready Registrered
Click to Login

Regression modeling and Neural Computing for predicting the Ultimate Tensile Strength of Friction Stir Welded aerospace Aluminium Alloy



Address:   Akshansh Mishra1, Jonathan Ve Vance2, Anish Dasgupta3, Saravanan M4
1Founder and Project Scientific Officer, Stir Research Technologies, Uttar Pradesh, India
2Department of Engineering Design, IIT Madras, India
3Technical Officer, Stir Research Technologies, Uttar Pradesh, India
4Head Design Engineer, Stir Research Technologies, Uttar Pradesh, India
*Corresponding Author
DOI No: 10.5958/2321-581X.2019.00028.X

ABSTRACT:
AA7075 is an aluminum alloy that's almost as strong as steel, yet it weighs just one third as much. Unfortunately its use has been limited, due to the fact that pieces of it couldn't be securely welded together by the traditional welding process. Friction Stir Welding (FSW) process overcomes the limitations of conventional welding process. The aim of our present is to compare the predicted results of the Ultimate Tensile Strength (UTS) of Friction Stir welded similar joints through Regression modeling and Artificial Neural Network (ANN) modeling. It was observed that the linear regression algorithm is able to make more accurate predictions compared to neural network algorithm for small dataset.
KEYWORDS:
Artificial Neural Network; Regression Model; Friction Stir Welding.
Cite:
Akshansh Mishra, Jonathan Ve Vance, Anish Dasgupta, Saravanan M. Regression modeling and Neural Computing for predicting the Ultimate Tensile Strength of Friction Stir Welded aerospace Aluminium Alloy. Research J. Engineering and Tech. 2019;10(4):163-171.
[View HTML]     



Visitor's No. :   207727