An Artificial Intelligence based Approach to Determine the Elongation % and Ultimate Tensile Strength of Friction Stir Welded Dissimilar Marine Grade Aluminium Alloy Joints
Abstract
Neural networks are a new generation of information processing
paradigms designed to mimic some of the behaviours of the human
brain. These networks have gained tremendous popularity due to their
ability to learn, recall and generalize from training data. A number of
neural network paradigms have been reported in the last four decades,
and in the last decade the neural networks have been refined and widely
used by researchers and application engineers. This study focuses on the
prediction of the elongation % and Ultimate Tensile Strength (UTS) of
the dissimilar Friction Stir Welded joints of aluminium alloys by training
the Neural Network on Quasi Newton Algorithm.
How to cite this article:
Mishra A, Singh A, Saravanan M et al. An Artificial
Intelligence based Approach to Determine the
Elongation % and Ultimate Tensile Strength of
Friction Stir Welded Dissimilar Marine Grade
Aluminium Alloy Joints. J Adv Res Appl Arti Intel
Neural Netw 2019; 3(1): 1-21.
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