The development and manufacturing of composite structures often requires great effort to derive optimal process parameters and engineering requirements to ensure required quality at reasonable costs. Process tolerances are defined based on profound experience from previous developments considering existing engineering procedures, manufacturing capabilities, material requirements and required conservatisms. Yet, due to rather high sensitivities of materials and processes some uncertainties still remain. This often leads to inevitable manufacturing defects, resulting in additional effort and costs for non-added value rework.
This paper describes new procedures and analysis methods for in-situ structural assessment during composite layup and curing. By this potentials for significant reduction of rework are identified. In detail the overall procedure is structured into following steps: Prior to manufacturing typical manufacturing features such as locally varying fibre architecture or varying cure temperatures are studied by means of probabilistic process simulation. Further, the effects of features on the structural properties are investigated for the expected parameter ranges. So-called surrogate models and databases are derived based on this knowledge enabling full probabilistic analysis and fast process optimization, while taking into account robustness requirements. During manufacturing the real detected features are provided by online monitoring systems. This information in turn serves for an in-situ structural evaluation of their effect on the overall structural performance directly during manufacturing. Moreover, an in-situ decision making for potential process adjustment is derived to reduce costly rework after manufacturing. For verification and validation of this integrated manufacturing concept tests are performed by manufacturing representative coupon structures as well as industrial demonstrators. Thus, key technologies are presented to maximize process efficiency at reduced cost and time while maintaining structural requirements.