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In this paper, a multi-frequency MEMS acoustic emission (AE) sensor is designed, characterized, and tested. The sensor includes sixteen individual resonators tuned in the range of 100 kHz to 700 kHz. The resonator frequencies are selected to form constructive interference when they are connected in parallel to increase the signal-to-noise ratio. Each resonator is comprised of a membrane that forms the mass and four beams that provide stiffness. The membrane size is kept the same for each resonator to have approximately the same sensitivity per frequency. The influence of spring elements on the resonant frequency and the sensitivity is numerically demonstrated. The sensor is manufactured using MEMSCAP PiezoMUMPs. The characterization experiments show a slight shift in the resonant frequency of individual resonators compared to the design values. The MEMS sensor is packaged using a custom-designed printed circuit board to improve the signal-to-noise ratio. The sensor performance is compared with a conventional AE sensor. The sensitivity and frequency bandwidth of the MEMS AE device is brought to a comparable level to bulky AE sensors.
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MEMS Acoustic Emission Sensors
This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators to improve the signal-to-noise ratio. The fundamental design variables of MEMS AE sensors include resonant frequency, bandwidth/quality factor and sensitivity. Micromachining methods have the flexibility to tune the sensor frequency to a particular range, which is important, as the frequency of AE signal depends on defect modes, constitutive properties and structural composition. This paper summarizes the properties of MEMS AE sensors, their design specifications and applications for detecting the simulated and real AE sources and discusses the future outlook.
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- Award ID(s):
- 2016444
- PAR ID:
- 10230401
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 10
- Issue:
- 24
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 8966
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app10248966
