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Nature-inspired design and implicit modeling for additive manufacturing: Advancing lattice structures for multidisciplinary applications
Summary
Researchers developed a nature-inspired 'Dual Curved Cubic' lattice structure for additive manufacturing, demonstrating via quasi-static compressive testing that its curved interwoven design outperformed conventional BCC and Octet lattices by up to 98.68% in compressive strength, while also exploring lattice architectures as a potential strategy for capturing microplastic pollutants.
This thesis presents the development and evaluation of a nature-inspired lattice structure, the development of an algorithm for the implicit modeling of various lattice architectures, and the exploration of lattice structures as a potential solution for addressing microplastic pollution. The nature-inspired lattice, termed the Dual Curved Cubic (DCC) lattice, draws from the phenomenon of inosculation—where plant branches intergrow to form interwoven load paths. The DCC lattice was designed and implicitly modeled with curved struts to mimic this interwoven behavior. The fabricated lattice, produced via stereolithography (SLA), was subjected to quasi-static compressive testing to assess its strength, energy absorption, and deformation behavior. Experimental and numerical results showed that the DCC lattice outperformed conventional Body-Centered Cubic (BCC) and Octet lattices by 98.68% and 45.08%, respectively, in compressive strength, while also offering tunable energy absorption characteristics through adjustments in curvature and relative density. Furthermore, the implicit modeling approach was extended into a coordinate-driven framework, StrutGen, using Signed Distance Functions (SDFs) to generate, export, and homogenize advanced lattice structures. This tool enhances existing open-source capabilities by enabling the modeling of curved, hollow, hybrid, and platetype lattices, as well as field-driven grading for functionally tailored properties. Lastly, this thesis explores the application of lattice structures in the design of MicroTRAP, a microfiber filtration device aimed at reducing microplastic emissions from laundry effluents. Experimental validation demonstrated a microfiber removal efficiency of 99.9%, underscoring the interdisciplinary potential of lattice-based design. Overall, this work advances the field of computational design for additive manufacturing by introducing a biomimetic lattice structure, a robust and scalable implicit modeling methodology, and a demonstrated application spanning both engineering and environmental domains.