A heat-transfer and fluid-flow-based model to obtain a specific weld geometry using various combinations of welding variables

Abstract

Numerical heat transfer and fluid flow models have provided significant insight into welding processes and welded materials that could not have been achieved otherwise. However, the use of these models has been limited by two major problems. First, the model predictions do not always agree with the experimental results because some input parameters such as the arc efficiency cannot be accurately prescribed. Second, and more important, these models cannot determine multiple pathways or sets of welding variables that can lead to a particular weld attribute such as the weld pool geometry, which is defined by an equilibrium temperature surface. Here we show that the computational heat transfer and fluid flow models of fusion welding can overcome the aforementioned difficulties by combining with a genetic algorithm. The reliability of the convective heat transfer model can be significantly improved by optimizing the values of the uncertain input parameters from a limited volume of the experimental data. Furthermore, the procedure can calculate multiple sets of welding variables, each leading to the same weld geometry. These multiple paths were obtained via a global search using a genetic algorithm within the phenomenological framework of the equations of conservation of mass, momentum, and energy. This computational procedure was applied to the gas tungsten arc welding of Ti–6Al–4V alloy to calculate various sets of welding variables to achieve a specified weld geometry. The calculated sets of welding parameters showed wide variations of values. However, each set of welding parameters resulted in a specified geometry showing the effectiveness of the computational procedure.