A probabilistic model of weld penetration depth based on process parameters

Abstract

In welded structures using robotized metal active gas (MAG) welding, unwanted variation in penetration depth is typically observed. This is due to uncertainties in the process parameters which cannot be fully controlled. In this work, an analytical probabilistic model is developed to predict the probability of satisfying a target penetration, in the presence of these uncertainties. The proposed probabilistic model incorporates both aleatory process parameter uncertainties and epistemic measurement uncertainties. The latter is evaluated using a novel digital tool for weld penetration measurement. The applicability of the model is demonstrated on fillet welds based on an experimental investigation. The studied input process parameters are voltage, current, travel speed, and torch travel angle. The uncertainties in these parameters are modelled using adequate probability distributions and a statistical correlation based on the volt-ampere characteristic of the power source. Using the proposed probabilistic model, it is shown that a traditional deterministic approach in setting the input process parameters typically results in only a 50% probability of satisfying a target penetration level. It is also shown that, using the proposed expressions, process parameter set-ups satisfying a desired probability level can be simply identified. Furthermore, the contribution of the input uncertainties to the variation of weld penetration is quantified. This work paves the way to make effective use of the robotic welding, by targeting a specified probability of satisfying a desired weld penetration depth as well as predicting its variation.