Tailoring complex weld geometry through reliable heat-transfer and fluid-flow calculations and a genetic algorithm

Systematic tailoring of weld attributes based on scientific principles is an important goal in fabricating reliable welds. What is needed, and is not currently available, is the ability to systematically determine multiple welding-variable sets to achieve a target weld feature such as geometry. Here, we show how the transport phenomena-based models can be completely restructured to achieve this goal. First, the reliability of the heat-transfer and fluid-flow model predictions is increased by optimizing the values of uncertain input variables such as the arc efficiency from a limited volume of experimental data. Next, after the model predictions are made reliable, the numerical heat-transfer and fluid-flow model is coupled with a genetic algorithm (GA) to achieve bidirectionality of the model and to determine multiple pathways to achieve a specified weld attribute such as the weld geometry. The proposed approach is demonstrated in complex gas metal-arc (GMA) fillet welding of low-alloy steel, for which various sets of welding variables are computed to achieve a specified weld geometry. The model predictions are compared with appropriate independent experimental results. The modeling results, apart from providing definitive insight regarding the complex physics of welding, also provide hope that weld attributes can be tailored reliably through multiple routes based on heat-transfer and fluid-flow calculations and evolutionary algorithms.