Collaborative Research: Process-Specific Topology Optimization for 3D-Printed Hierarchical Composites and Structures

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A close look at almost any biological structure reveals an elegant and complex hierarchical arrangement of materials at different length scales that imparts desirable mechanical robustness and function to the structure with minimal wasted material. Prior to the development of additive manufacturing technologies, the creation of components with analogous complexity and hierarchy using engineering materials has been prohibitively expensive or entirely impossible. Now, additive manufacturing technologies, which build materials and structures from the ground up using incremental deposition of material, have opened the doors to designing and creating entirely new classes of structures with unprecedented complexity, hierarchy, and performance. However, design tools do not currently exist that can identify the optimal combinations of materials, hierarchy, and shape for any given application, nor do engineers yet understand exactly when, how, and why hierarchy leads to superior performance. This award will improve our understanding of how structural hierarchy in mechanical components leads to superior performance and create a novel method for designing such structures. By generating this knowledge, design engineers will be better able to make decisions about if, when, and how to utilize hierarchy to create stronger, stiffer, lighter, more efficient components for applications ranging from implants, prosthetics, and sporting equipment, to shipping and aerospace. The project includes activities to educate high school students about advanced manufacturing and dissemination of results directly to industry.This research will focus on fused filament fabrication and direct-ink writing, since these additive technologies are the most widely accessible and have seen rapid recent development in new high strength, lightweight composite feedstocks. The specific scope of the project is comprised of a series of coupled experimental/numerical research tasks. These tasks build from assessing the effects of 3D printing and hierarchy in simple, classic topology optimization benchmark problems to designing, fabricating, and evaluating designs that utilize cellular infill with graded shape and density. By incorporating the unique features that result from material extrusion additive manufacturing (i.e., perimeter, infill, and material anisotropy) into topology optimization schemes, structures will be optimized specifically for the combination of feedstock material and type of hierarchy desired. While additive manufacturing technology has become ubiquitous in schools, universities, and industry, systematic approaches to teach "design for additive manufacturing" require further development. This project will also help address this national imperative through hands-on additive manufacturing and topology optimization demonstrations, industry educational seminars, and outreach programs for underrepresented minorities, women, and persons with disabilities that will inspire the next generation Science, Technology, Engineering, and Math workforce.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.