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An integrated model for the post-solidification shape and grain morphology of fusion welds

An integrated model for the post-solidification shape and grain morphology of fusion welds, Anton Kidess, Mingming Tong, Greg Duggan, David Browne, Ian Richardson, Sasa Kenjeres, and Chris Kleijn. International Journal of Heat and Mass Transfer 2015, 85 , 667–678.

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Abstract

Through an integrated macroscale/mesoscale computational model, we investigate the developing shape and grain morphology during the melting and solidification of a weld. In addition to macroscale surface tension driven fluid flow and heat transfer, we predict the solidification progression using a mesoscale model accounting for realistic solidification kinetics, rather than quasi-equilibrium thermodynamics. The tight coupling between the macroscale and the mesoscale distinguishes our results from previously published studies. The inclusion of Marangoni driven fluid flow and heat transfer, both during heating and cooling, was found to be crucial for accurately predicting both weld pool shape and grain morphology. However, if only the shape of the weld pool is of interest, a thermodynamic quasi-equilibrium solidification model, neglecting solidification kinetics, was found to suffice when including fluid flow and heat transfer. We demonstrate that the addition of a sufficient concentration of approximately 1 μm diameter TiN grain refining particles effectively triggers a favorable transition from columnar dendritic to equiaxed grains, as it allows for the latter to heterogeneously nucleate in the undercooled melt ahead of the columnar dendritic front. This transition from columnar to equiaxed growth is achievable for widely differing weld conditions, and its precise nature is relatively insensitive to the concentration of particles and to inaccurately known model parameters.

BibTeX

@article{Kidess2015Integrated,  author = {Kidess, Anton and Tong, Mingming and Duggan, Greg and Browne, David and Richardson, Ian and Kenjeres, Sasa and Kleijn, Chris},  citeulike-article-id = {13518738},  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ijheatmasstransfer.2015.01.144},  doi = {10.1016/j.ijheatmasstransfer.2015.01.144},  issn = {0017-9310},  journal = {International Journal of Heat and Mass Transfer},  keywords = {cet, welding},  month = jun,  Abstract = {Through an integrated macroscale/mesoscale computational model, we investigate the developing shape and grain morphology during the melting and solidification of a weld. In addition to macroscale surface tension driven fluid flow and heat transfer, we predict the solidification progression using a mesoscale model accounting for realistic solidification kinetics, rather than quasi-equilibrium thermodynamics. The tight coupling between the macroscale and the mesoscale distinguishes our results from previously published studies.    The inclusion of Marangoni driven fluid flow and heat transfer, both during heating and cooling, was found to be crucial for accurately predicting both weld pool shape and grain morphology. However, if only the shape of the weld pool is of interest, a thermodynamic quasi-equilibrium solidification model, neglecting solidification kinetics, was found to suffice when including fluid flow and heat transfer.    We demonstrate that the addition of a sufficient concentration of approximately 1 μm diameter TiN grain refining particles effectively triggers a favorable transition from columnar dendritic to equiaxed grains, as it allows for the latter to heterogeneously nucleate in the undercooled melt ahead of the columnar dendritic front. This transition from columnar to equiaxed growth is achievable for widely differing weld conditions, and its precise nature is relatively insensitive to the concentration of particles and to inaccurately known model parameters.},  pages = {667--678},  posted-at = {2015-02-16 21:09:33},  priority = {2},  title = {An integrated model for the post-solidification shape and grain morphology of fusion welds},  url = {http://dx.doi.org/10.1016/j.ijheatmasstransfer.2015.01.144},  volume = {85},  year = {2015}
}

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