The Influence of Pearlite Volume Fraction on Rayleigh Wave Propagation in A572 Grade 50 Steel

The acoustoelastic effect is the interaction between ultrasonic wave velocity and stress. To estimate the stress a perturbation signal is introduced and the shift in time of flight is measured at the receiving location. In addition to the stress, the wave velocity can be affected by the amount of phases in the material’s microstructure. This study investigates the changes in Rayleigh wave velocity for A572 grade 50 steel as a function of stress and pearlite phase volume fraction. In order to obtain different amounts of pearlite the samples are heat treated at 970 °C for time durations of 30 min, 1 hour, 2 hours and 4 hours and then furnace cooled. The acoustoelastic coefficient for 0.5 and 1 MHz perturbation frequency is calculated by uniaxial loading of each heat treated plate while measuring ultrasonic wave velocity. The results are compared for pearlite phase volume fraction obtained from optical microscopy and hardness measurements

Acoustic Emission Signal of \u3cem\u3eLactococcus lactis\u3c/em\u3e before and after Inhibition with NaN\u3csub\u3e3\u3c/sub\u3e and Infection with Bacteriophage c2

The detection of acoustic emission (AE) from Lactococcus lactis, ssp lactis is reported in which emission intensities are used to follow and define metabolic activity during growth in nutrient broths. Optical density (OD) data were also acquired during L. lactis growth at 32°C and provided insight into the timing of the AE signals relative to the lag, logarithmic, and stationary growth phases of the bacteria. The inclusion of a metabolic inhibitor, NaN3, into the nutrient broth eliminated bacteria metabolic activity according to the OD data, the absence of which was confirmed using AE data acquisition. The OD and AE data were also acquired before and after the addition of Bacteriophage c2 in L. lactis containing nutrient broths during the early or middle logarithmic phase; c2 phage m.o.i. (Multiplicity of infection) was varied to help differentiate whether the detected AE was from bacteria cells during lysis or from the c2 phage during genome injection into the cells. It is proposed that AE measurements using piezoelectric sensors are sensitive enough to detect bacteria at the amount near 104 cfu/mL, to provide real time data on bacteria metabolic activity and to dynamically monitor phage infection of cells

Steel bridge fatigue crack detection with piezoelectric wafer active sensors

Piezoelectric wafer active sensors (PWAS) are well known for its dual capabilities in structural health monitoring, acting as either actuators or sensors. Due to the variety of deterioration sources and locations of bridge defects, there is currently no single method that can detect and address the potential sources globally. In our research, our use of the PWAS based sensing has the novelty of implementing both passive (as acoustic emission) and active (as ultrasonic transducers) sensing with a single PWAS network. The combined schematic is using acoustic emission to detect the presence of fatigue cracks in steel bridges in their early stage since methods such as ultrasonics are unable to quantify the initial condition of crack growth since most of the fatigue life for these details is consumed while the fatigue crack is too small to be detected. Hence, combing acoustic emission with ultrasonic active sensing will strengthen the damage detection process. The integration of passive acoustic emission detection with active sensing will be a technological leap forward from the current practice of periodic and subjective visual inspection, and bridge management based primarily on history of past performance. In this study, extensive laboratory investigation is performed supported by theoretical modeling analysis. A demonstration system will be presented to show how piezoelectric wafer active sensor is used for acoustic emission. Specimens representing complex structures are tested. The results will also be compared with traditional acoustic emission transducers to identify the application barriers

The Development of Low-Cost Integrated Composite Seal for SOFC: Materials and Design Methodologies

This report summarizes the work conducted by UConn SOFC seal development team during the Phase I program and no cost extension. The work included composite seal sample fabrication, materials characterizations, leak testing, mechanical strength testing, chemical stability study and acoustic-based diagnostic methods. Materials characterization work revealed a set of attractive material properties including low bulk permeability, high electrical resistivity, good mechanical robustness. Composite seal samples made of a number of glasses and metallic fillers were tested for sealing performance under steady state and thermal cycling conditions. Mechanical testing included static strength (pull out) and interfacial fracture toughness measurements. Chemically stability study evaluated composite seal material stability after aging at 800 C for 168 hrs. Acoustic based diagnostic test was conducted to help detect and understand the micro-cracking processes during thermal cycling test. The composite seal concept was successfully demonstrated and a set of material (coating composition & fillers) were identified to have excellent thermal cycling performance

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

The Influence of Pearlite Volume Fraction on Rayleigh Wave Propagation in A572 Grade 50 Steel

The acoustoelastic effect is the interaction between ultrasonic wave velocity and stress. To estimate the stress a perturbation signal is introduced and the shift in time of flight is measured at the receiving location. In addition to the stress, the wave velocity can be affected by the amount of phases in the material’s microstructure. This study investigates the changes in Rayleigh wave velocity for A572 grade 50 steel as a function of stress and pearlite phase volume fraction. In order to obtain different amounts of pearlite the samples are heat treated at 970 °C for time durations of 30 min, 1 hour, 2 hours and 4 hours and then furnace cooled. The acoustoelastic coefficient for 0.5 and 1 MHz perturbation frequency is calculated by uniaxial loading of each heat treated plate while measuring ultrasonic wave velocity. The results are compared for pearlite phase volume fraction obtained from optical microscopy and hardness measurements.</p

Combined Damage Index to Detect Plastic Deformation in Metals Using Acoustic Emission and Nonlinear Ultrasonics

Understanding the amount of degradation using nondestructive evaluation (NDE) methods provides an effective way of determining the fitness to service and the residual life of structural components. Due to uncertainties introduced by the single NDE method, a combined damage index using multi-sensor data increases the reliability of damage assessment. In this paper, the outputs of three NDE methods including acoustic emission (AE), linear ultrasonics (LUT), and nonlinear ultrasonics (NLUT) are merged to identify the amount of plastic deformation in aluminum 1100. The sensitivities of individual and combined methods to microstructural changes are evaluated. The coupon samples are loaded up to different strain levels and then unloaded. AE data is recorded in real time and ultrasonic data is recorded from the unloaded samples. The major features combined in the damage index are cumulative AE absolute energy and nonlinear coefficient. The microstructural state is verified with microscopic analysis and hardness testing. The developed damage index can nondestructively assess the amount of plastic deformation with higher reliability