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Advanced sensors and novel concepts for intelligent and reliable processing in bonded repairs


The main limitation of the existing technology in bonding repair of aerospace structures lies in the inability of the repair control systems to assess the actual material state and integrate the available tools (simulations, measurements, experience and knowledge) into an intelligent material-based control system. To circumvent these limitations, the SENARIO project proposes the development of a revolutionary sensing systems linked to intelligent process control equipment and reliable methodologies of aerostructures component maintenance.

The proposed system consists of:
- a portable curing process console with blanket-mounted dielectric sensors;
- wireless non-intrusive dielectric sensors for monitoring adhesive state;
- reliable laser technology for increased automation;
- optical sensors complementing the dielectric sensors;
- thermomechanical monitoring set-up;
- intelligent process guidance system for co-cured components;
- integrated multi-zone repair control unit, and;
- application of bonded repairs with reliability, controlled quality and certification potential.

The proposed novel multi-zone integrated process control scheme gains full benefit from the use of multi-sensory devices and the deployment of existing process knowledge (cure and stress development). The scheme enables in situ monitoring of bonded repair and resulting adhesion quality through the repair process, while offering a potential tool for non-intrusive structural health monitoring.

The main innovations reside in:
- the use of durable dielectric sensors in the repair process for real-time assessment of in-patch conditions;
- the wire-free dielectric sensor capable of measuring remotely changes in adhesive electrical properties.
- intelligent process guidance algorithm for optimal repair process conditions in co-curing applications;
- the interface between thermomechanical process simulation tools, process actuators and composites multi-sensory monitoring tools.

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Participants (9)