Hierarchical Computational Model for Digital Design and Fabrication of Biomimetic Armor Surfaces 2
2014 Katia Zolotovsky, Jorge Duro-Royo, Laia Mogas-Soldevila, Swati Varshney, Neri Oxman, Mary C. Boyce, Christine Ortiz, EURO Bio-inspired Materials 2014, March 18-21, 2014, Potsdam, Germany
This poster presents a composite material system inspired by the mineralized and articulated exoskeleton of Polypterus senegalus, a prehistoric armored fish. The organism’s exoskeleton achieves diverse functions (e.g. toughness, flexibility, strength) through spatial variation in materials and morphometry across organizational hierarchies with precise interfacial control. Our goal is to characterize the geometric design rules underlying the kinematics of anisotropic flexibility, and to present a bio-inspired design methodology to translate these rules to synthetic material systems. We integrate design tools into scientific analysis ones to translate the P. senegalus exoskeleton assembly to a synthetic, hybrid, flexible prototype, and experimentally assess its anisotropic flexibility. Geometric abstraction and computational modeling is applied to reconstructed tomography data of the scales to generate an articulated, flexible surface fabricated via multi-material 3D printing. We quantified the prototype’s anisotropic flexibility and introduced parametric variation to show geometry-based tunability of mechanical behavior. We then constructed a novel hierarchical computational model, MetaMesh, based on the design principles of the exoskeleton that adapts a segmented ‘armor’ system to fit complex host surfaces. MetaMesh operates in three levels of resolution: (i) the local scale constructs a segment unit geometry based on the shape parameters of scales in the P. senegalus exoskeleton, (ii) the regional scale encodes connection guides to adapt units with their neighbors to regional direction schema, and (iii) the global scale applies the extended unit assembly over curved host surfaces through pre-processing of global mesh optimization with a functional coefficient gradient. This study suggests a generative computational framework for design and fabrication of functionally graded material systems with tunable local performance that adapts to a host mesh surface.