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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.

The propagation of hole machining defects in pin-loaded carbon/epoxy laminates subjected to uniaxial tension fatigue loading was investigated using two non-destructive examination (NDE) techniques. The KTH-method, a new method which gives defect-free1 holes, was used to machine holes in specimens. For comparison, holes were also machined using two traditional methods (DIXI and Dagger drills) causing varying extent of damage. X-ray radiography and computerised tomography were used to give two- and three-dimensional images, respectively, of defect propagation after 50,000-250,000-500,000-cycles, and after failure. Permanent hole elongation was monitored during cyclic loading. The results showed that the propagation of defects occurred in a controlled and stable phase until a sharp fatigue limit was reached (about 400,000 cycles for the DIXI specimen and about 500,000 cycles for both the KTH and Dagger specimens). During the stable phase, the global structural response of the specimens was affected very little by the damage propagation. At the fatigue limit, extensive damage development and defect propagation occurred, resulting in similar large permanent hole elongations in all specimens.

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The casting processes are characterized by complex relationships between predictors and responses. It is the fundamental understanding of these complex relationships that often involves hundreds of factors, which improves quality without losing productivity and raising cost. In this work, cast solid solution strengthened ferritic spheroidal graphite irons GJS-500-14 and GJS-600-10 (EN 1563:2012) have been evaluated. These materials offer stronger components with good machinability owing to their even hardness properties. In this case the predictors are chemical composition, gating layout, foundry set-up, testing procedure and equipment etc. and the responses are the tensile properties (Rp0.2, Rm, A5). Here 200 tensile specimens compiled from industrial foundry melts from over 30 years of research have created a state-of-the-art platform for statistical engineering in order to perform Exploratory Data Analysis (EDA) and data visualization. This statistical platform has provided new insight on how foundries should treat complex relationships between predictors and responses in order to identify sources of variation and interaction effects.