During multidisciplinary design of welded aircraft components, designs are principally optimized upon component performance, employing well-established modelling and simulation techniques. On the contrary, because of the complexity of modelling welding process phenomena, much of the welding experimentation relies on physical testing, which means welding producibility aspects are considered after the design has already been established. In addition, welding optimization research mainly focuses on welding process parameters, overlooking the potential impact of product design. As a consequence, redesign loops and welding rework increases product cost. To solve these problems, in this article, a novel method that combines the benefits of design of experiments (DOE) techniques with welding simulation is presented. The aim of the virtual design of experiments method is to model and optimize the effect of design and welding parameters interactions early in the design process. The method is explained through a case study, in which weld bead penetration and distortion are quality responses to optimize. First, a small number of physical welds are conducted to develop and tune the welding simulation. From this activity, a new combined heat source model is presented. Thereafter, the DOE technique optimal design is employed to design an experimental matrix that enables the conjointly incorporation of design and welding parameters. Welding simulations are then run and a response function is obtained. With virtual experiments, a large number of design and welding parameter combinations can be tested in a short time. In conclusion, the creation of a meta-model allows for performing welding producibility optimization and robustness analyses during early design phases of aircraft components.

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The way parts are located in relation to each other or in fixtures is critical for how geometrical variation will propagate and cause variation in critical product dimensions. Therefore, more emphasis should be put on this activity in early design phases in order to avoid assembly and production problems later on. In earlier literature, locator positions have been defined using optimization to reach a robust locating scheme. This implies that the total robustness of a part is optimized by placing the locators in an optimal way. Sometimes there are areas on parts that are more sensitive to variation than others. Therefore, this paper suggests an approach for optimizing the positions of locators in a locating scheme to maximize robustness in defined critical dimensions. A formulation of an optimization problem is presented, and an algorithm solving this in a heuristic approach is developed. Finally, this algorithm is applied on an industrial example.