STTR Phase I: Manufacturing of Enhanced Composites via Interlaminar Incorporation of CNT/Epoxy Nanoscaffolds using Genetic Algorithm Assisted Machine Learning and Neural Networks

The broader impact/commercial potential of this Small Business Technology Transfer Program (STTR) Phase I project is to improve strength and stiffness of carbon fiber reinforced composites in aerospace, automotive, energy, and other applications. Carbon fiber reinforced polymer (CFRP) materials are used to reduce weight without compromising the strength of structural components, but because they are built layer by layer, they have certain weaknesses.

This project will advance a new approach to to enable production of higher performance automobiles, airplanes, turbines and more. The process can be highly automated, cost-effective, and seamlessly integrated with existing CFRP manufacturing. This can be integrated into existing processes.

The continued development of stronger and lighter composite parts will also have an impact in related fields including renewable energy resources. The technology holds the promise of reducing operating costs, improving fuel efficiency, and decreasing emissions. This will lead to significant impacts on national security in advanced manufacturing.

The strong technical innovation in the project is identifying the use of electrospinning to coat a prepreg CFRP roll with carbon nanotube (CNT)/epoxy nanofilaments that can form a CNT-reinforced bonding surface that is square meters in area and just tens of microns in thickness. This project will address the longstanding weak through-thickness interlaminar strength of CFRP prepreg laminates using a low materials-cost and highly automated process.

The project will expand on the primary innovation to develop a theoretical understanding of multiscale nanoscaffold-enhanced CFRP advanced composites through genetic algorithm assisted machine learning (GAML) and artificial neural networks (ANN). The objective is to establish an experimentally and computationally supported GAML framework for determining optimal processing parameters and structural features.

Results will lead to better optimization tools in the design of new composite materials, and the development of instrumentation supporting the emergence of new weight and cost saving opportunities in the composite industry for complex systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.