MIT PI: Brian L. Wardle, Department of Aeronautics and Astronautics
SkT PI: Sergey Abaimov, Center for Design, Manufacturing, and Materials
Multifunctional fusion of manufacturing & in-service functionalities is achieved via nanoengineering of structural prepreg-based composites, enhancing performance across the life cycle for light weighting applications. Experimental 3D morphology quantification across nm- to cm length scales will be enabled by artificial intelligence (AI)-driven approaches to create digital twins, enabling data-driven design. Firstly, the nano-engineered heaters remove the need for expensive, power consuming, and production-bottleneck autoclaves and ovens, by reduction of porosity due to nano capillarity (no need for high applied pressure) and economically delivering heat directly into laminate (Out-of-Oven, >100X energy savings) as proven by MIT. The heating functionality can also be utilized in service for de-icing or enhanced thermographic damage sensing. Secondly, the same system is a sensor measuring the degree of cure, allowing closed-loop spatial part quality control and residual stress management. This concept has been preliminarily demonstrated by MIT and will be operationally extended in this proposal. Thirdly, in-service structural health monitoring and damage sensing will extend the manufacturing quality control to performance management. In-service sensing allows detection of damage at early stages and provides input for need-for-maintenance. The nano-engineered system enhances the structural function, in striking contrast with the degradation of mechanical properties with traditional use of embedded sensors such as optical fiber sensors. The AI-driven quantification of the morphology on nano- (3D TEM) and micro-scale (3D XCT) lead to stochastic and deterministic digital twins at the cm-scale, using concepts demonstrated by Skoltech. Development of the nano-engineered system results in a first-ever prototype demonstrator with fused multifunctionality.