High performance piezoelectric crystal cut designed in LiNbO3 for high temperature acoustic emission sensing application

Lithium niobate (LiNbO3, LN) is a kind of multifunctional crystal material. In this study, an optimum piezoelectric crystal cut (XZt/28°) with central resonance frequency of 150 kHz and stable electro-elastic...

Analysis of the Optical Quartz Lens Centering Process Based on Acoustic Emission Signal Processing and the Support Vector Machine

A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of <2 μm, a lens circularity error of <0.01 mm, a crack length of <E0.1, and an optical axis error of <150 μrad.

Identification of the Wear Process of a Silver-Plating Layer by Dual Acoustic Emission Sensing

A method based on acoustic emission (AE) sensing in which two AE sensors are used to measure the tribological characteristics of two interacting friction materials simultaneously in real time was assessed for the in situ measurement and evaluation of the wear process of silver plating. AE sensors were attached to a silver-plated pin and a silver-plated plate, and the two AE signals were measured simultaneously on a pin-on-plate-type reciprocating sliding tester. The resulting changes in the AE signal could be classified into three phases. Surface observations and energy-dispersive X-ray spectroscopy analyses showed that the wear of the silver-plating layer progressed in Phase I, the nickel intermediate layer was exposed and wear of the nickel progressed in Phase II, and the contact electrical resistance increased and the copper substrate was exposed in Phase III. In summary, the wear process of a silver-plating layer, which cannot be identified from the changes in the frictional resistance or the contact electric resistance, can be detected from changes in the dual AE signals. Furthermore, changes in the wear state of both the pin and plate specimens can be identified from differences in the amplitudes of the AE signals and the timing of their detection.

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist—another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz–20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen–Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems.