Advances in Production Engineering & Management
Volume 8 | Number 1 | March 2013 | pp 13–24
http://dx.doi.org/10.14743/apem2013.1.149
Modeling and prediction of HAZ using finite element and neural network modeling
Edwin Raja Dhas, J.; Kumanan, S.
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A B S T R A C T
With the increasing demands for product variety and quality level the need to
eliminate human operator from the feedback path for welding process correction
is evident. Of the several manufacturing methods welding alone has defined
true automation. Success of automation depends on effective and efficient
decision making tools. Neural network is applied to intelligent weld
control. In Submerged Arc Welding (SAW), selecting appropriate values for
process variables is essential to control heat affected zone (HAZ) dimensions
and get the required bead size and quality. Also, conditions must be selected
that will ensure a predictable and reproducible weld bead. This paper proposes
the modeling and prediction of dimensions of Heat-Affected Zone for
SAW process using Finite Element Analysis (FEA) and Artificial Neural Network
(ANN). The dimensions of HAZ for SAW are modeled are simulated using
FEA using the process variables such as welding current, arc voltage, arc efficiency
and welding speed and the results are used as the learning file for ANN
model. The developed ANN model is forwarded to predict the dimensions of
HAZ and the results are compared with simulated FEA results. The developed
method is found to be time consuming, competent and cost effective.
A R T I C L E I N F O
Keywords • Heat-affected zone (HAZ), Finite element analysis, Artificial neural network, Submerged arc welding
Corresponding author • Edwin Raja Dhas, J.
Published on-line • 29 March 2013
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