Monday  |  Trinity A  |  05:10 PM–05:30 PM

#14428–Rapid, Approximate Multi-Axis Vibration Testing

Sequential single-axis vibration testing strategies frequently produce overtesting when qualifying system hardware. Rarely does the test article experience equivalent cumulative vibration response between laboratory and service environments when utilizing traditional single-axis testing methodologies. Multi-axis excitation techniques can simulate realistic service environments, but the hardware and testing-strategies required to do so tend to be costly and complex. Test engineers instead must test sequentially on single-axis shaker tables to excite each degree of freedom, which the previous two decades of vibration testing literature has shown to cause extensive overtesting when considering cross-axis responses in assessing the severity of the applied test environments. Traditional assessments assume that the test article responds only in the axis of excitation, but often significant response occurs in the off-axes as well. This paper proposes a method to address the overtesting problem by approximating a simultaneous multi-axis test using readily-available single-axis shaker tables. By optimizing the angle of excitation and the boundary condition through dynamic test fixture design, the test article can be rapidly and inexpensively tested using a Single-Input, Multiple-Output (SIMO) test in a way that approximates a Multiple-Input, Multiple-Output (MIMO) test. This paper demonstrates the proposed methodology with a finite-element beam model attached to springs with variable stiffness. The results include quantified test quality assessment metrics with comparison to standard sequential testing. The proposed methodology enables access to multi-axis testing using existing hardware, thereby reducing the over-conservatism of sequential single-axis tests and requisite over-design of systems producing lighter and less expensive hardware.

Ethan Cramer   University of North Texas/Los Alamos National Laboratory
Dustin Harvey   Los Alamos National Laboratory
Richard Zhang   University of North Texas