Quasi-static compression test dataset of avian bone-inspired energy absorbers with gyroid infill produced from carbon-fiber-reinforced polylactic acid (PLA-CF) by fused deposition modeling (FDM)
This dataset provides experimental quasi-static axial compression data for 30 avian bone-inspired structures (ABIS) fabricated by fused deposition modeling (FDM) from carbon-fiber-reinforced polylactic acid (PLA-CF). Each specimen combines a tapered hollow tube with gyroid internal reinforcement. The configurations were generated using Latin hypercube sampling (LHS) across three design variables, namely taper angle, infill density, and number of walls, alongside a nominal ABIS configuration (N) and a simple tube (ST) adopted as baseline designs and tested in triplicate. Included in the dataset are PLA-CF tensile characterization data, design parameters, STL geometry files, project files defining the complete print settings, axial force–displacement responses, sequential deformation images, and net specimen masses with derived crashworthiness indicators. These data support surrogate modeling, machine learning (ML) applications, optimization, and the validation of numerical models for FDM-printed fiber-reinforced polymer energy absorbers.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.