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A Genetic Algorithm-Assisted Inverse Convective Heat Transfer Model for Tailoring Weld Geometry

DSpace at IIT Bombay

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Title A Genetic Algorithm-Assisted Inverse Convective Heat Transfer Model for Tailoring Weld Geometry
 
Creator BAG, S
DE, A
DEBROY, T
 
Subject fluid-flow
multivariable optimization
reliable calculations
gas tungsten
phase-change
arc
parameters
metal
pool
simulation
arc welding
convective heat transfer
genetic algorithm
inverse model
numerical optimization
 
Description Although heat transfer and fluid flow models have provided significant insight about the welding processes and welded materials, currently they are not widely used, mainly because of two difficulties. First, the model predictions do not always agree with experiments because the values of energy absorption efficiency and other parameters cannot be prescribed from scientific principles. Second, the available models are unidirectional and cannot currently predict welding variables necessary to attain a target weld attribute. Here we provide a rigorous proof that the heat transfer and fluid flow models can be combined with an appropriate genetic algorithm (GA) to enhance reliability of computational results and achieve inverse modeling capability. The new capability enables systematic tailoring of weld attributes based on scientific principles. In particular, the GA-based optimization of arc efficiency, arc radius, effective thermal conductivity, and effective viscosity using a limited volume of experimental data led to superior weld geometry computations for a wide variety of welding conditions. Furthermore, the inverse model's ability to calculate multiple combinations of arc current, voltage, and welding speed needed to achieve a target weld geometry was developed and rigorously tested by welding experiments.
 
Publisher TAYLOR & FRANCIS INC
 
Date 2011-08-30T16:09:38Z
2011-12-26T12:59:03Z
2011-12-27T05:49:55Z
2011-08-30T16:09:38Z
2011-12-26T12:59:03Z
2011-12-27T05:49:55Z
2009
 
Type Article
 
Identifier MATERIALS AND MANUFACTURING PROCESSES, 24(3), 384-397
1042-6914
http://dx.doi.org/10.1080/10426910802679915
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12386
http://hdl.handle.net/10054/12386
 
Language en