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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

42 ENGINEERING↗
DOE OSTI2024

Frequency‐Locked Wireless Multifunctional Surface Acoustic Wave Sensors

Abstract Surface acoustic waves (SAWs) have shown great potential for developing sensors for structural health monitoring (SHM) and lab‐on‐a‐chip (LOC) applications. Existing SAW sensors mainly rely on measuring the frequency shifts of high‐frequency (e.g., >0.1 GHz) resonance peaks. This study presents frequency‐locked wireless multifunctional SAW sensors that enable multiple wireless sensing functions, including strain sensing, temperature measurement, water presence detection, and vibration sensing. These sensors leverage SAW resonators on piezoelectric chips, inductive coupling‐based wireless power transmission, and, particularly, a frequency‐locked wireless sensing mechanism that works at low frequencies (e.g., <0.1 GHz). This mechanism locks the input frequency on the slope of a sensor's reflection spectrum and monitors the reflection signal's amplitude change induced by the changes of sensing parameters. The proof‐of‐concept experiments show that these wireless sensors can operate in a low‐power active mode for on‐demand wireless strain measurement, temperature sensing, and water presence detection. Moreover, these sensors can operate in a power‐free passive mode for vibration sensing, with results that agree well with laser vibrometer measurements. It is anticipated that the designs and mechanisms of the frequency‐locked wireless SAW sensors will inspire researchers to develop future wireless multifunctional sensors for SHM and LOC applications.

Bo, Luyu↗
DOE OSTI2020

Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

42 ENGINEERING↗
DOE OSTI2022

Material Characterization of Grade 91 Steel Welds using, Micro-resolution Ultrasonic Imaging System

Grade 91 (9Cr-1Mo-V) steel is a common structural material for fossil-fuel fired and nuclear power plants because of its creep strength and oxidation resistance at high temperatures. In this research, we studied the feasibility of correlating the high-resolution images obtained from the immersion ultrasonic imaging technique to microstructure regions in Grade 91 steel plate weldments. The high-resolution ultrasonic images presented in this paper were generated using a 20-MHz focused transducer as the transmitter, and a laser vibrometer as the receiver in the through transmission configuration to produce longitudinal and shear waves. This nondestructive evaluation (NDE) method was used to identify the Fusion Zone (FZ), Heat-Affected Zone (HAZ), and Base Metal (BM) regions of Grade 91 cold metal transfer (CMT) and flux core arc welds (FCAW). The ultrasonic imaging results were analyzed using beam propagation amplitudes and homogeneity, which in turn were used to relate the different microstructure regions of the FCAW and CMT welded samples. With this study, we were able to identify highly susceptible microstructures in the weld that could potentially promote creep type failures.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

High-Speed and High-Quality Field Welding Repair Based on Advanced Non-Destructive Evaluation and Numerical Modeling

Creep strength-enhanced ferritic (CSEF) steels such as Grade 91 (9Cr-1Mo-V) and Grade 92 (Fe-9Cr-2W-0.5Mo) steels are widely used in the fossil-fuel-fired and nuclear power plants. The weld integrity of these steels is crucial for power plants' safe and reliable operations. Due to harsh service conditions, the steel weld can become susceptible to environmental degradation. Field welding repair is used to restore the degraded weld’s performance where a controlled temper-bead welding technique is commonly used to temper the freshly formed martensite during welding. However, knowledge of weld repairability is limited and experimental trial and error optimization to achieve desired microstructure and joint properties is expensive and time-consuming. Many existing computational models, e.g., finite element models, are limited to solving heat conduction equation and ignoring convective heat transfer due to molten metal flow. These models can result in over-prediction of peak temperatures of weld pool and heat-affected zone (HAZ), which in turn can affect the accuracy of tempering prediction. Moreover, these finite element models require an input of the deposit profiles in advance and thus limits the usability of these models. Here, a molten pool-based, multi-pass multi-layer model has been developed based on computational fluid dynamics (CFD) approach with the Volume of Fluid (VOF) method. The model calculates the bead formation, thereby eliminating the need for pre-determined bead profiles required by finite element models. For computational efficiency, a coordinate system attached to the moving heat source is utilized. A subroutine is developed to convert the temperature profiles in the reference frame stationary to the heat source to that stationary to the workpiece. The converted thermal cycles are then imported into a microstructure model to compute the tempering kinetics and resultant hardness using a Johnson-Mehl-Avrami-Kolmogorov (JMAK), and modified Grange-Baughman parameter. The modeling approach is first developed and validated on single- and multi-pass deposition of stainless steel filler metal onto a SA-533 high strength steel substrate. The models are then applied to a multi-pass V-groove repair weld of Grade 91 steel plate as well as directed energy deposition of Grade 92 steel. Non-destructive characterization of microstructures was performed on Grade 91 and 92 steel welds. Two welding processes, cold metal transfer (CMT) and flux-cored arc welding (FCAW), were investigated for the Grade 91 steel weld samples. For the Grade 92 weld samples, three different heat inputs (low, medium, and high) of gas tungsten arc welding (GTAW) were utilized to replicate traditional field welding processes. The non-destructive evaluation (NDE) method used for this research was immersion ultrasonic testing (UT) using a micro-resolution ultrasonic imaging methodology specifically designed to operate in the through-transmission configuration operating at 20 MHz of frequency. The system used a focused ultrasonic beam spot size diameter between 250-300 μm, and a 6 μm laser vibrometer spot size for detection, to produce highly defined images with longitudinal and mode-converted shear waves. From the micro-resolution ultrasonic C-scan images, three microstructural regions, i.e., weld metal (WM), HAZ, and base metal (BM), were clearly identifiable. Various levels of ultrasonic amplitudes distributed over the three regions were correlated with electron beam backscattered diffraction (EBSD) images using grain size, grain boundaries, and dislocation densities. The results showed that areas with relatively higher ultrasonic amplitude levels were associated with smaller grains and higher dislocation densities, while areas with lower amplitude levels were associated with larger grains and lower dislocation densities. In addition, ultrasonic velocity data obtained across the three different weld microstructural regions of Grade 91 test samples were correlated with optical metallographic images and hardness measurements. The results showed distinctive decreases in ultrasonic velocity and hardness over the HAZ region, where weld failures often occur during service.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessment of Weld Microstructure Through Sound Velocity Measurements for Power Plant Applications

Here, this research study employed an immersion-based micro-resolution ultrasonic velocity measurement technique (MUVT) to evaluate Grade 91 (9Cr-1Mo-V) steel welds. This process utilized a 20 MHz focused ultrasonic beam with a spot size of 250 µm, a 6 µm laser vibrometer spot size for detection, and an automated 3-axis raster scanner to accurately collect ultrasonic velocity data from two Grade 91 steel welds fabricated using cold metal transfer (CMT) and flux-cored arc welding (FCAW) processes. By analyzing the MUVT data, the Poisson’s ratio (υ) and modulus of elasticity (E), which are important mechanical properties for weldments, were calculated. The correlation between mechanical properties and ultrasonic velocity was examined. The results indicated that longitudinal ultrasonic velocity could effectively identify base metal, heat-affected zone (HAZ), and weld metal areas, showing a strong correlation with hardness. In particular, the distinctive V-shaped dip in velocity and hardness data across the HAZ areas of both samples highlighted changes in the microstructure of creep strength–enhanced ferritic steel welds. Importantly, the immersion-based MUVT demonstrated high accuracy in small sections of the weld, surpassing the conventional contact ultrasonic testing method in spatial resolution detection.

arc welding↗
DOE OSTI2022

In-process, layer-by-layer non-destructive testing of additive manufactured components using linear and nonlinear vibrational resonance

Provided is an additive manufacturing system capable of performing in-process, layer-by-layer non-destructive testing, the additive manufacturing system including: a plate for producing a part thereon; a transducer attached to the plate and configured to induce vibrations in the part; and a laser vibrometer configured to detect a vibrational response of the part to determine whether defect and/or anisotropy exists within the part.

Remillieux, Marcel↗
DOE OSTI2021

Dispersion curve regression – assisted wideband local wavenumber analysis for characterizing three-dimensional (3D) profile of hidden corrosion damage

Corrosion as common damage in civil, petrochemical, nuclear, and aerospace structures affects the integrity and safety of the structures and may lead to catastrophic failures. This paper presents dispersion curve regression-assisted local wavenumber analysis method, which can analyze the time–space wavefield containing wideband information of wave-damage interaction and further extract the structural information carried by such wavefield for characterizing hidden corrosion damage in an isotropic plate. To acquire the time–space wavefield, a noncontact scanning laser Doppler vibrometer is used. In our analysis method, the acquired time–space wavefield is firstly processed to generate a local wavenumber function. The derived wavenumber function is further analyzed with a dispersion curve regression step, which searches in a set of theoretical frequency-wavenumber dispersion curves for different plate thicknesses and identifies the optimal plate thickness whose theoretical curve best matches the frequency-wavenumber relation contained in the local wavenumber function. By this means, a 3D profile including both in plane and thickness dimensions of the structure can be constructed for corrosion visualization and quantification. The experimental study demonstrates that our method can quantify the profile of hidden uniform corrosion in metal plates in terms of its in-plane shape and size as well as its out-of-plane depth in the subwavelength scale. Moreover, compared to previous Lamb wave-based corrosion inspection methods, our technique allows for noncontact 3D characterization of hidden corrosion from a far distance to the structure. Finally, we believe this work will inspire new 3D damage quantification methods that are based on wavefield analysis as well as enable potential applications for the quantification of hidden corrosion in civil, petrochemical, nuclear, and aerospace structures.

42 ENGINEERING↗
DOE OSTI2022

Laser Doppler vibrometry for piezoelectric coefficient ($d_{33}$) measurements in irradiated aluminum nitride

Sensors used for experiments in advanced reactors must survive in harsh environments. Few material systems can be used to construct sensors viable for extreme conditions. Aluminum nitride (AlN) is one such material because it has high thermal stability and radiation resistance and sustains good piezoelectric and dielectric properties at high temperatures. In this work, the piezoelectric coefficient $d_{33}$ of the AlN single-crystal with thermal and irradiation damage was investigated using an indirect method with a laser Doppler vibrometer (LDV). Surface electrodes were deposited on the AlN samples, and the vibration response of the samples to an applied voltage was monitored using the LDV as a function of the excitation frequency. The $d_{33}$ estimation was based on the excitation voltage and the thickness-mode displacement extracted from the LDV measurements. Further, six AlN substrates were irradiated with 8 MeV Al 2+ at three fluences (10 15 , 10 16 , and 10 17 ions/cm 2 ) and two temperatures (300 °C and 500 °C). The $d_{33}$ for the six irradiated samples and one pristine sample were measured, and the measurement uncertainty was estimated based on five repeated tests. All samples were also measured by a commercial piezometer for comparison. The experimental results demonstrate that the piezoelectric coefficients obtained by LDV were about 0.8–1.16 pm/V lower than those obtained by the piezometer. With the compensation of the clamping effect, the corrected LDV values are similar to the piezometer results. Both show similar trends in all samples, which validates the feasibility of the proposed method for $d_{33}$ measurement. Based on the LDV results, the irradiated samples show a 12%–22% decrease in $d_{33}$ compared with the pristine samples. The samples irradiated under the same fluence at a higher temperature (500 °C) demonstrated a lower $d_{33}$ than those at 300 °C. The effect of the retro-reflective tape, sample temperature, and sample size on the $d_{33}$ measurement were also studied.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting Local Material Thickness from Steady-State Ultrasonic Wavefield Measurements Using a Convolutional Neural Network

Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field, ultrasonic non-destructive evaluation (NDE) technique used to locate and characterize defects in plate-like structures. ASSESS generates a steady-state, single-tone ultrasonic excitation in a structure and a scanning laser Doppler Vibrometer (LDV) measures the resulting full-field surface velocity response. Traditional processing techniques for ASSESS data rely on wavenumber domain analysis. This paper presents the alternative use of a convolutional neural network (CNN), trained using simulated ASSESS data, to predict the local plate thickness at every pixel in the wavefield measurement directly. The defect detection accuracy of CNN-based thickness predictions are shown to improve for defects of greater size, and for defects with higher thickness reductions. The CNN demonstrates the ability to predict thickness accurately in regions where Lamb wave dispersion relations are complex or unknown, such as near the boundaries of a test specimen, so long as the CNN is trained on data that accounts for these regions. The CNN also shows generalizability to ASSESS experimental data, despite an entirely simulated training dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Exploring 3D elastic-wave scattering at interfaces using high-resolution phased-array system

The elastic-wave scattering at interfaces, such as cracks, is essential for nondestructive inspections, and hence, understanding the phenomenon is crucial. However, the elastic-wave scattering at cracks is very complex in three dimensions since microscopic asperities of crack faces can be multiple scattering sources. We propose a method for exploring 3D elastic-wave scattering based on our previously developed high-resolution 3D phased-array system, the piezoelectric and laser ultrasonic system (PLUS). We describe the principle of PLUS, which combines a piezoelectric transmitter and a 2D mechanical scan of a laser Doppler vibrometer, enabling us to resolve a crack into a collection of scattring sources. Subsequently, we show how the 3D elastic-wave scattering in the vicinity of each response can be extracted. Here, we experimentally applied PLUS to a fatigue-crack specimen. We found that diverse 3D elastic-wave scattering occurred in a manner depending on the responses within the fatigue crack. This is significant because access to such information will be useful for optimizing inspection conditions, designing ultrasonic measurement systems, and characterizing cracks. More importantly, the described methodology is very general and can be applied to not only metals but also other materials such as composites, concrete, and rocks, leading to progress in many fields.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Delamination imaging in composites using cross-correlation method by non-contact air-coupled Lamb waves

Abstract This paper presents a cross-correlation damage detection technique using damage scattered waves of all directions cross-correlate with the incident waves for delamination imaging in carbon fiber reinforced polymer (CFRP) composite plate. The presented imaging can not only detect the delamination but also precisely quantify the damage with locations and sizes. First, a CFRP composite sample with interlaminar delamination by inserting the Teflon layer is designed and manufactured in house using the hot-press machine. And a three-dimensional model of composite sample is simulated using the finite element method. Next, the cross-correlation imaging algorithm is introduced in detail, and the cross-correlation imaging proof-of-concept study is conducted with the simulated Lamb waves in the composite sample. Finally, a fully non-contact air-coupled transducer and scanning laser Doppler vibrometer system with a single-mode Lamb wave method is established to actuate and sense the interrogating Lamb waves in the structure. The imaging method is experimentally implemented for the one delamination and two delaminations imaging and quantification.

Instruments & Instrumentation↗
DOE OSTI2022

Nondestructive Evaluation of Stress Corrosion Cracking in a Welded Steel Plate Using Guided Ultrasonic Waves

Stress corrosion cracking (SCC) had occurred in early-generation high-level nuclear waste tanks constructed by welding carbon steel. This paper describes an ultrasonic inspection system and its fundamental ability to detect and quantify the length of SCC on thick welded steel plates. The finite element method (FEM) was applied to simulate the welding process to estimate the welding residual stress field. Growth of stress corrosion cracks is driven by crack stress intensities exceeding the subcritical cracking threshold intensity. The subject plate was experimentally inspected with ultrasonic nondestructive evaluation (NDE) techniques to characterize the extent of SCC. The NDE system uses a piezoelectric transducer to generate guided waves in the thick steel plate, and a scanning laser Doppler vibrometer (SLDV) to measure multidimensional time–space wavefield data over a user-defined scanning area in the plate surface. The measured wavefield data can show wave interactions in a localized area in the plate due to the presence of the discontinuities of the SCC. To generate an inspection image that can precisely show the crack’s location and/or the dimension, the wavefield data are further processed to generate inspection image that maps the entire sample plate so the crack can be clearly identified in the plate while its length can be readily estimated. In conclusion, the ultrasonic test results for crack length agree well with the visually estimated length and are close to that predicted by the FEM for cracks in the weld residual stress field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Electromechanical response of piezoelectric thin films subjected to large strain

The use of piezoelectric thin sensors, when subjected to large static strain levels in ultrasonic applications is reported in this paper. A novel flipped beam bending method is proposed to induce static strain level nearing twice the incipient yield strain in the beam material (~0.39%). First, the effect of static large strain on ultrasonic guided wave propagation in the beam was studied using the velocity measurements obtained from a laser Doppler vibrometer. Next, the ultrasonic response of thin sensors is studied under compressive and tensile static strain (±0.72%) with the sensors symmetrically bonded on the lower and upper surfaces, and at the same axial location. The ultrasonic guided wave response to varying levels of static strain is recorded at different center frequencies (50–400 kHz) of the narrow banded ultrasonic excitation. As a case study, the variation of the ultrasonic guided wave response of polyvinylidene difluoride (PVDF) thin sensors with center frequencies at different static strain levels was analyzed and compared with that of lead zirconate titanate (PZT) thin sensors. PZT sensors show a higher variation of sensing performance with strain when compared to PVDF sensors. Apart from static strain, the possible effects on the change in performance of sensors, such as the change in the capacitance is discussed. The characterization method provides a reusable beam arrangement to study the ultrasonic sensor performance at large static strain levels. Characterization at large strain level allows the development of fail-safe sensors even under large strain and permanent plastic deformation of structure on which they are bonded, allowing accurate ultrasonic structural health monitoring even in the presence of damage progression. Piezopolymer sensor like PVDF are least susceptible to static strain due to large yield strain and low modulus and can serve as fail-safe sensors under large strain.

Rathod, Vivek↗
DOE OSTI2020

Lamb wave imaging with actuator network for damage quantification in aluminum plate structures

Lamb waves have been widely used for damage detection on plate-like structures. However, there are still considerable interests on quantifying damage with complex profile. In this article, quantification of complex damage in plate-like structures using a network of actuators and time-space Lamb wavefield is investigated. The actuator network inspection system is implemented with multiple PZT transducers for Lamb wave actuation in round robin pattern and scanning laser Doppler vibrometer for wavefield sensing. The PZT network is arranged in a way that the target area is fully enclosed and Lamb waves come to the damage from all directions. Waves induced by the damage are subsequently obtained through frequency-wavenumber filtering, using the experimentally acquired dispersion curves presented in the paper. The filtered waves from all wave actuators are then used to generate a synthetic image of the damage being inspected. Two cases of complex damage are evaluated on aluminum plates, mass loss with triangular profile and mass addition with a three-letter cluster profile. Our results show that the damages are not only detected but also their profiles are clearly outlined in the images. We believe the subject methods provide improved evaluation of damage profile for Lamb wavefield based damage quantification.

Materials Science↗
DOE OSTI2024

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Fatigue-Crack Detection and Monitoring through the Scattered-Wave Two-Dimensional Cross-Correlation Imaging Method Using Piezoelectric Transducers

Piezoelectric transducers are convenient enablers for generating and receiving Lamb waves for damage detection. Fatigue cracks are one of the most common causes for the failure of metallic structures. Increasing emphasis on the integrity of critical structures creates an urgent need to monitor structures and to detect cracks at an early stage to prevent catastrophic failures. This paper presents a two-dimensional (2D) cross-correlation imaging technique that can not only detect a fatigue crack but can also precisely image the fatigue cracks in metallic structures. The imaging method was based on the cross-correlation algorithm that uses incident waves and the crack-scattered waves of all directions to generate the crack image. Fatigue testing for crack generation was then conducted in both an aluminum plate and a stainless-steel plate. Piezoelectric wafer transducer was used to actuate the interrogating Lamb wave. To obtain the scattered waves as well as the incident waves, a scanning laser Doppler vibrometer was adopted for acquiring time-space multidimensional wavefield, followed with frequency-wavenumber processing. The proof-of-concept study was conducted in an aluminum plate with a hairline fatigue crack. A frequency-wavenumber filtering method was used to obtain the incident wave and the scattered wave wavefields for the cross-correlation imaging. After this, the imaging method was applied to evaluate cracks on a stainless-steel plate generated during fatigue loading tests. The presented imaging method showed successful inspection and quantification results of the crack and its growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗