Microelectromechanical system (MEMS) three-axis acceleration threshold sensors have been developed to measure acceleration threshold levels using voltage switching when the threshold is reached. Determining damping coefficients is important for categorizing how each threshold sensor or switch operates. Switches with different damping coefficients result in different mechanical impedances and response times. Analytical and numerical methods to model damping coefficient values based on empirical data are needed to characterize three-axis acceleration sensors; traditional methods use the displacement of an underdamped system to calculate the damping ratio.
Mechanical switches are single output devices that distinguish whether closure occurs or not, and lack a transduction mechanism to turn acceleration into a readable displacement signal.
Two damping measurement techniques were developed that are unique in their application, and cover all damping ratio values: underdamped and overdamped systems.
The first method, the acceleration table damping predictor, computed the damping coefficient at the time of specified voltage changes. MEMS acceleration switches developed for 60G fusing impact environments were used to evaluate this non-destructive method for damping measurement. The MEMS impact sensors are omnidirectional, consisting of two out-plane contacts (top and bottom) and one in-plane contact for lateral or side acceleration events.
In the second method, the harmonic excitation model damping predictor (HEMDP) method, underdamped 50-2000G MEMS impact acceleration sensors were tested. The sensors were omnidirectional; therefore, switch closure is dependent upon switch orientation during vibration.
The goal of the experimental approach was to empirically characterize MEMS acceleration switches on a linear impact table and an inductive shaker, and use these results to determine damping values. During an impact event, typically all modes are excited, resulting in a superposition of modal displacements. During harmonic excitation, a particular mechanical mode can be excited, generating a very simple motion. Damping values determined from harmonic excitation will be very specific to the mode, whereas damping values obtained from impact tests will be generalized.
A linear shock table was used to test the sensors, allowing the data acquisition device to plot acceleration and voltage change over time (Figure 1). An open switch indicated little or no voltage drop, but at switch closure, the short circuit caused a change in voltage. The system setup was arranged to analyze one sensor at a time. Four channels connected to the data acquisition device were used to collect data. Channel 1 was used for measuring acceleration of the table at impact and Channels 2, 3, and 4 measured the voltage changes for each contact (top, in-plane, bottom).
Six sensors were attached to a voltage divider circuit that was connected to the data acquisition device (Figure 2). The sensors were attached to a mounting plate in different orientations using screws and washers. Different pads were used to absorb the shock from the impact. Softer cushions were made of rubber, and the harder cushions were made of a harder plastic.
The linear impact table method used acceleration shock profile to induce MEMS acceleration switch closure. This method differs from the harmonic excitation method because when the MEMS switch is put into a transient shock environment, multiple modes are excited. The harmonic excitation method uses an inductive shaker to excite single modes of interest. The harmonic excitation method only works for an underdamped system, which is a limiting factor for this testing method. Both testing methods revealed nonlinearity to damping as a function of input amplitude.
Determining the damping values of an impact acceleration switch helps categorize how the switch functions. The novel method developed to measure damping by measuring closure time helps model designs to further improve design to more accurately close at the expected acceleration.
This work was done by Ryan Knight and Evan Cheng of the Army Research Laboratory. ARL-0175