Composite structures have increasingly emerged for aerospace and other applications because of their high strength-to-weight ratio, good resistance to harsh environments, and great performance reliability. Because composite structures have nonlinear, anisotropic, and compliant properties as well as inherent manufacturing variability, conventional process modeling and quality control methodologies for metal structure assembling are not adequate to composite components. This award supports fundamental research that integrates physics-guided and machine-learning models to advance ultra-high precision assembly of aerospace composite structures. The research involves seamless integration of physical and digital product connections, data science, advanced statistics, and comprehensive manufacturing knowledge. The research has the potential to minimize the material loss, decrease the production flow time and achieve high productivity and quality control for aerospace manufacturing. The scientific findings from this project may also be extended to other composites demanding industries, e.g., automotive, spacecraft and solar energy, and thus, increase the global competitiveness of the U.S. industry. The interdisciplinary nature of this project will provide students with unique educational and research experiences and cultivate a diverse and qualified workforce cognizant with combined manufacturing and data analytics abilities. In addition, the project will develop new learning modules for an undergraduate core course, recruit and mentor underrepresented students, offer industrial short courses, and develop open-source software for precision assembly, all potentially leading to profound impacts to the society.
This research aims to develop fundamental knowledge and transformative technologies for ultra-high precision assembly of large complex-shaped composite structures by advancing physics-guided machine learning. Specific research activities include: (1) developing digital twin for ultra-high precision assembly of composite structures, (2) conducting physics-constrained active learning with safe exploration and efficient exploitation for experimental design and predictive modeling, (3) studying sparse machine learning for an optimal actuating strategy in composite structures assembly, and (4) analyzing theoretical properties of the methodologies. The project will research small-sized key aerospace structures as well as large-scale carbon fiber reinforced composite fuselage of ultra-high precision, e.g., less than 0.2 mm for a diameter of about 5 m. The methodologies will be computationally and experimentally evaluated in the verification and validation phase. The research outcomes will (1) expand the scientific understanding of engineering-driven data analytics and ultra-high precision quality control theory, (2) bridge the knowledge gap between predictive modeling, active learning, sparse learning, and composite structures assembly, and ultimately, (3) realize the effective all-inclusive integration of machine learning methodologies with advanced manufacturing.
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.
