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The aircraft industry is continuously challenged to deliver more complex products in order to meet increasingly stricter regulations and growing customer demands, to provide better quality products in larger quantities, and to pay more attention to reusability and circularity issues. At the same time, the industry needs to work with lower costs, shorter lead and production times, less waste, delay, energy consumption, and emissions, and reduce its dependence on non-renewable materials. To keep up with these challenges, the industry automates and continuously innovates as much as is possible. The increasing use of composites in aircraft and aircraft components adds the extra challenge of rapidly bringing innovative manufacturing processes to the factory floor. Aircraft product design, analysis, testing, and production are increasingly being digitized and automated, and consequently involve, and produce, more and more data. Digital twinning is an important enabler for mastering and exploiting this ever-growing plethora of data. In this paper we discuss the potentials of digital twinning in innovative production processes in the aircraft industry, and describe first-hand results and experiences in developing and using digital twinning in the production of composite aircraft components, illustrated by concrete example applications.

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Engineers spend years studying physical models that are used to predict system behavior from input parameters. For example, an engineer may estimate the deflection in a beam based on the beam length, width, height, and modulus of elasticity. Typically, engineers are not taught to model the stochastic behavior of systems. Variability in design inputs is addressed through extreme techniques such as worst-case analysis and safety factors. This text presents the methods engineers may use to expand physical models to predict the variation in systems as a function of the variation of the input parameters. This prevents over designing and allows true system optimization including cost and performance of all functions. Variation in engineering designVariation is a part of the world we live in. The voltage measured at each of the outlets in your home will not be identical. The weight of each lug nut retaining the wheels on your car will vary. This variation makes life difficult for engineers. Engineers and process designers must understand and compensate for variation. The product engineers design may behave unexpectedly because the nominal values specified on drawings are difficult to achieve.A parameter diagram (P-diagram), shown in Figure 3.1, is an ideal document for identifying sources of variation. A P-diagram shows the inputs, outputs, error states, control factors, and noise factors for a system. The design engineer has the ability to assign values and tolerances to control factors. Noise factors may add variation to system outputs, but the engineer cannot define targets or tolerances for these factors.

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