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Calculations of fluid flow and heat transfer in the weld pool are strongly influenced by the values of effective thermal conductivity and effective viscosity of the liquid metal. The values of these variables are uncertain since the welding conditions and the fluid flow characteristics within the weld pool influence them. Following an inverse modeling approach, the present work develops a smart model that embodies a multivariable optimization scheme within the framework of a phenomenological heat transfer and fluid flow model to estimate the uncertain parameters necessary for weld pool modeling. The optimization scheme considers the sensitivity of the calculated weld geometry with respect to the unknown parameters. To avoid unrealistic optimized solutions, the smart model is internally guided to look for only the physically significant solutions. The model could estimate the effective thermal conductivity and effective viscosity for conduction mode laser welding as a function of nondimensional heat input from six sets of experimental measurements of weld pool depth and width.
De et al. (Wed,) studied this question.