CAREER: Design of Cellular Mechanical Metamaterials under Uncertainty with Physics-Informed and Data-Driven Machine Learning

The objective of this CAREER project is to engage and educate graduate and undergraduate students in materials design, with a particular focus on designing cellular mechanical metamaterials (CMMs) under the effects of fabrication-related material uncertainty. The plan includes physics-based computations, machine learning (ML), and design under uncertainty strategies, as well as the development of outreach activities. The underlying hypothesis is that the CMMs can be designed to achieve targeted mechanical properties and performance by developing a multi-scale computational framework that investigates the relationship between component-scale properties and underlying micro-scale architectures. The societal impacts of the project will be on the economy, with the promise of designing sustainable, lightweight, and high-performance materials. The gained knowledge will be disseminated to academia and industry through technical activities and open-access graphical software tools. Additional deliverables of the project include curriculum development at undergraduate and graduate levels, research experiences for students, and other outreach activities involving students and educators, with a special focus on individuals from underrepresented groups. The overarching goal of this project is to improve the current knowledge of CMM design and enhance the performance of 3-D printed products using a multi-scale framework that will explore complex and non-linear relationships between the microstructure and component by allowing non-periodically repeating microstructure designs and accounting for the fabrication-related uncertainty. This goal will be accomplished by developing a multi-scale design strategy driven by physics-based material models, data-driven and physics-informed ML, design optimization, and uncertainty quantification approaches. The ability to model non-periodical microstructure arrangements of CMMs will be essential to explore their true component-level mechanical performance, thereby substantially increasing their potential for use in new-generation engineering systems for hypersonics, structural applications, energy absorption, sensors, and soft robots. The findings of the project will also identify designs that improve mechanical performance and reliability by considering the effects of material uncertainty. In addition, the design methodology for CMMs will be extended to nature-inspired cellular materials, such as artificial bone structures, for designing such systems to achieve target mechanical performance under uncertainty. The activity will also promote teaching, training, and learning through the development of outreach activities, such as camps, programs, and workshops targeting both youths and teachers. The participation of underrepresented groups is guaranteed by specifically addressing outreach programs for female students, first-generation college students, students from underserved communities in Southwest Virginia, and other minorities. The project data and findings will be made publicly available at Virginia Tech’s open-access repository, VTechData. 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.