Defect detection using the phased-array laser ultrasonic crack diffraction enhancement method

Identification of nonregular indication according to change of grain size/surface geometry in nuclear power plant (NPP) reactor vessel (RV)-upper head alloy 690 penetration

Signal quality enhancement using higher order wavelets for ultrasonic TOFD signals from austenitic stainless steel welds

Surface impulse ground-penetrating radar (GPR) and ultrasonic time-of-flight diffraction (TOFD) are recent innovations in the respective geophysical remote sensing and non-destructive testing industries. Both techniques have proved highly versatile and valuable applications. This paper provides a brief description of the time-of-flight diffraction (TOFD) signals and the surface impulse ground penetrating radar (GPR) signals. The similarities between the two techniques are highlighted and it is shown how processing techniques developed for GPR data processing may be adapted for use with TOFD data.

Background/Objectives: Grain structure of Austenitic Stainless Steel (ASS) weld causes difficulty in defect detection while inspecting using Time of Flight Diffraction (TOFD) method. This study aims at comparing segmentation methods to overcome this difficulty based on defect characterization. Methods/Statistical Analysis: This study makes use of TOFD and Radiographic images of ASS weld pads fabricated with defined linear and volumetric defects. The Region-Based Level Set algorithm and Discontinuity Based Segmentation algorithm were explored for achieving flaw segmentation and quantitative characterization and validation of the result with that of standard radiographic results. Findings: The efficiency of the algorithms was analyzed by comparing and validating the size of defect with that of standard radiographic results in the form of error percentage. The consistency of error percentage in defect sizing (up to 11%) achieved by RegionBased Level Set algorithm for all the test images given in the database indicate that, this algorithm is the best as compared to Discontinuity Based Segmentation algorithm(error percentage up to 47%) for defect segmentation and characterization in TOFD images. Application/Improvements: The segmentation algorithm enables automation of measurement process and enhanced detection and characterization of defects at the initial stage. Further, it reduces human fatigue caused by operator while defect detection as the volume of data increases. The future direction is to fully automate the system in order to save time for interpretation and to modify the algorithm to segment images with multiple defects. Keywords: Defect Characterization, Discontinuity Based Segmentation, Flaw Segmentation, Region-Based Level Set Segmentation, Time of Flight Diffraction (TOFD), Weld Defect

Metal additive manufacturing (AM) technology enables the direct fabrication of complex components. However, the formation of internal defects remains a significant challenge, limiting the consistent production of high-quality key parts. Online monitoring of internal defect formation during the manufacturing process is critical for ensuring quality control and enhancing the practical application of AM products. Laser ultrasound has demonstrated significant potential for rapid defect scanning and detection, owing to its non-contact and multimodal capabilities. Additionally, the layer-by-layer accumulation process characteristic of additive manufacturing (AM) provides a flat detection surface during the printing of each layer, effectively addressing the challenges posed by the limited detection space in complex components. Compared to other non-destructive testing (NDT) methods, ultrasonic testing (UT) further enables the correlation of online monitoring data with offline inspection results. However, this approach faces certain limitations. The rough surfaces and complex geometries of additive samples lead to high attenuation, low signal-to-noise ratio (SNR) in ultrasonic signals, and interference from structural waves. Under these conditions, extracting effective defect signals and visually displaying internal defects remain challenging. We propose an ultrasonic online monitoring method that employs a bottom piezoelectric sensor to generate excitation signals and constructs a scanning array on the surface using a laser Doppler vibrometer. This approach enables the timely acquisition of effective ultrasonic signals across varying printing layer thicknesses during the additive manufacturing process while mitigating interference from structural waves. To address the challenges of high ultrasonic attenuation and low signal-to-noise ratio (SNR) caused by the complex geometries of additive samples, the interaction between ultrasound and defects was simulated using the finite element method. Defect characteristic signals were identified and integrated into a proposed signal classification and focusing imaging algorithm, facilitating defect signal extraction and reconstruction. 

Fully noncontact inspection of closed surface crack with nonlinear laser ultrasonic testing method

In order to improve signal-to-noise ratio (SNR) of air-coupled ultrasonic signal in the detection of lamination defects in molded composite, the pulse compression and wavelet filtering hybrid signal processing method is proposed. The selection principle of parameters of the hybrid signal processing method is studied. The actual detection results of molded composite show that the hybrid method is very effective in improving the SNR of air-coupled ultrasonic signal when selecting reasonable parameters (The experiment results demonstrate that the optimal parameters are 13-bit Barker code sequences signal with three-cycle per sub-pulse, db9 wavelet, six decomposition levels, and soft threshold function.). An improvement in SNR up to 18.81 dB is attained compared with the original received signal. The quantitative accuracy of defects in C-scan image based on the hybrid method is also very high, and defects as small as $$\emptyset $$ 5 mm can be easily identified.

Frontiers in NDE research nearing maturity for exploitation to ensure structural integrity of pressure retaining components

Young’s modulus and Poisson’s ratio of SiC ceramics at temperatures >1400°C were obtained using a laser ultrasonics method that included a Fabry‐Pérot interferometer (LUFP). At temperatures <1000°C, Young’s modulus and Poisson’s ratio measured using the LUFP method agreed well with those measured using standard contact methods, such as the resonance method and the ultrasonic pulse method. These results showed that the LUFP method is a powerful tool for measuring high‐temperature elastic properties of advanced ceramics in a noncontact manner.

Advances in the semiconductor industry have led the wafer inspection technology to the limit of nanometer-scale defect detection, which is far beyond the diffraction limit. In this regime, the signal-to-noise ratio (SNR) is the figure-of-merit to determine whether the optical system can detect a single nanometer-scale defect. In this paper, we investigated the SNR properties of various line defects using the dark-field inspection with tailored polarized illumination by simulation and experiment. Conventional crossed Nicols configuration with linear polarized illumination and crossed analyzer can minimize background scattering noise and maximize line defect signal only for a specific kind of line defect such as gap or bridge due to strong polarization dependence on a line and space (L/S) pattern. The nulling elliptically polarized illumination is optimized to suppress background scattering noise moderately and maintain defect signal intensity at the same time. We confirmed SNR improvement for both 10 nm open and bridge defects on 40 nm line and space silicon pattern with 40 nm depth. There was a good agreement between our simulation results and experimental results. We experimentally confirmed SNR ~ 4 for both line gap and bridge defect detection on 40 nm L/S patterned wafer with the fixed nulling illumination.

Delamination is one of the most common types of defects for carbon fiber reinforced plastic (CFRP) composites. The application of laser techniques to detect delamination faces difficulties with ultrasonic wave excitation because of its low thermal conductivity. Much of the research that can be found in the literature has only focused on the detection of a single delamination. In this study, aluminum foil was pasted onto the surface of the composite so that it was vulnerable to ablation and could acquire a usable signal. Using a fully noncontact system with laser excitation at a fixed point and a scanning laser sensor, the effects of different aluminum foil sizes and shapes on the wavefield were studied for the composites; we decided to use a rectangle with 3 mm length and 5 mm width for laser excitation experiments. Wavefield characteristics of the composite plates were analyzed with single- and multi-layered Teflon inserts. Taking the time window for standard ultrasonic testing as a reference, the algorithms for localized wave energy with appropriate time windows are presented for the detection of single and multiple defects. The appropriate time window is meaningful for identifying each delamination defect. The algorithm performs well in delamination detection of the composites with one or multiple Teflon inserts.

In this paper, we describe the optimization of endoluminal planar coils for high-resolution magnetic resonance imaging of gastrointestinal walls. For maximizing the coil performances, electromagnetic parameters of planar rectangular radio frequency (RF) coils were simulated using the finite element method. The eddy currents were fully computed to determine the electromagnetic losses in both wires and surrounding environment. Geometric parameters of the coils (length, conductive layer section, number of layers, and turns number) were varied. Based on simulations, five loop RF coil prototypes with planar geometry were designed to fit in a 5-mm inner diameter catheter. In the immediate vicinity of single-loop coils, the signal-to-noise ratio (SNR) decreases with the length of the coil, whereas penetration depth increases with it. The double-loop coil offers a greater penetration depth in comparison to the same length single-loop coil. The multilayer coil preserves the RF field B/sub 1/ by inducing a reduction in the electrical resistance of the conductor, therefore resulting in an increase in SNR. Experimental verifications were performed on a 1.5 T clinical scanner. Simulation results were found to be in good agreement with that of MR experiments. Developed prototypes provided a dramatic increase in SNR at the region of interest.

In recent publications, our group has presented simulation studies that investigated the applicability of five ultraviolet inspection wavelengths for defect detection using a signal-to-noise ratio (SNR) based defect metric. All computations were performed using our in-house finite-difference time-domain (FDTD) Maxwell’s equations solver. Initial simulations contained no perturbations except for the defect. In the absence of noise, the defects are readily apparent, and we therefore added Poisson noise both to the defect and no-defect simulated images that yield the differential image. For these noisy differential images, signal and noise were separated to calculate the SNR with two additional variables: an area threshold A_min and a noise threshold σ. A wavelength dependent A_min (λ) that scaled linearly with wavelength was required to enable comparison across all five wavelengths. All types of defects were best resolved using an inspection wavelength of λ=47 nm assuming ideal conditions (i.e. no variation in numerical aperture or source intensity as a function of wavelength.) In this work, we add line edge roughness as “wafer noise” on both defect and no-defect geometries to test this SNR approach in a more realistic way. Various permutations of defect types, polarizations, illumination angles, focal planes and wavelengths affect the differential images produced differently. For every combination of the above mentioned experimental setups we individually maximize the SNR as a function of A_min and σ to improve defect detection, and trends across wavelengths are assessed. Finally, we present an alternative approach to the defect detection that is based on a neural network. We address questions of feature selection and its relation to the previous used SNR approach.

Magnetic flux leakage analysis and compensation of high-frequency planar insulated core transformer

Additive Manufacturing (AM) of parts used in nuclear power plants can solve many issues like those related to obsolescence. Of the gap limiting the use of AM parts in nuclear is the need of reliable non-destructive inspection capable to meet the qualification requirements. Recently, efforts in this direction have been made worldwide within several research projects, like the EU Horizon 2020 NUCOBAM. In the framework of NUCOBAM, this article presents the activity related to the inspection of 316-L AM nuclear parts produced by L-PBF and inspected via advanced ultrasonic (UT) methods, like MultiPoint Focusing (MPF) and Total Focusing Method (TFM). Multiple UT array probes are used, linear, matrix and annular. Emphasis is dedicated to the inspection of classified valve bodies produced with known internal seeding flaws. The analysis of the results shows the effect of AM induced anisotropy on the propagation of the ultrasonic wave characteristics, the sound velocity increased with 3% when the sound beam deviated 15° against the perpendicular axis. The TFM method contributed significantly regarding defect detection, Signal to Noise Ratios (SNR) increased with at least 9 dB compared to the Multi-Point Focusing method. Smaller errors were noticed when examination frequency was increased and TFM was applied. The combination of an annular array with TFM and mechanical scanning demonstrated to be the best approach.