Abstract

Multiple fastener reduced-order models and fitting strategies are used on a multiaxial dataset and these models are further evaluated using a high-fidelity analysis model to demonstrate how well these strategies predict load-displacement behavior and failure. Two common reduced-order modeling approaches, the plug and spot weld, are calibrated, assessed, and compared to a more intensive approach — a “two-block” plug calibrated to multiple datasets. An optimization analysis workflow leveraging a genetic algorithm was exercised on a set of quasistatic test data where fasteners were pulled at angles from 0° to 90° in 15° increments to obtain material parameters for a fastener model that best capture the load-displacement behavior of the chosen datasets. The one-block plug is calibrated just to the tension data, the spot weld is calibrated to the tension (0°) and shear (90°), and the two-block plug is calibrated to all data available (0°–90°). These calibrations are further assessed by incorporating these models and modeling approaches into a high-fidelity analysis model of the test setup and comparing the load-displacement predictions to the raw test data.

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