Reconstructing ultrasonic images and flaw detection in time-frequency domain by matching a-scan inspections
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Time-frequency techniques are applied for mixing signals from a material inspected by a multiple sensor detection system. Ultrasonic sensors are located at the perimeter of a rectangular shaped material evaluated in a pulse-echo scheme. The resulting mixed signal becomes a high-resolution time-frequency image of the material. Different kinds of classification techniques can be applied to this image in order to obtain the defects in the material. In this paper, a simulation and experimental evaluation of the proposed approach are presented. Several time-frequency transforms and the fuzzy c-means are used. In the simulations, backscattering of the material grain is modelled by using Gaussian and K distributions with different signal to noise ratio parameters. The validity of the presented method is assessed through the detection and spatial location of artificial defects in a material with a rectangular shape. The performance of the classification technique in discerning defects buried in the backscattering from the material grain microstructure, is also discussed.Keywords:
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Advances in model-based software for simulating ultrasonic immersion inspections of metal components
Under the sponsorship of the National Science Foundation's Industry/University Cooperative Research Center at ISU, an effort was initiated in 2015 to repackage existing research-grade software into user-friendly tools for the rapid estimation of signal-to-noise ratio (SNR) for ultrasonic inspections of metals. The software combines: (1) a Python-based graphical user interface for specifying an inspection scenario and displaying results; and (2) a Fortran-based engine for computing defect signals and backscattered grain noise characteristics. The later makes use the Thompson-Gray measurement model for the response from an internal defect, and the Thompson-Margetan independent scatterer model for backscattered grain noise. This paper, the third in the series [1-2], provides an overview of the ongoing modeling effort with emphasis on recent developments. These include the ability to: (1) treat microstructures where grain size, shape and tilt relative to the incident sound direction can all vary with depth; and (2) simulate C-scans of defect signals in the presence of backscattered grain noise. The simulation software can now treat both normal and oblique-incidence immersion inspections of curved metal components. Both longitudinal and shear-wave inspections are treated. The model transducer can either be planar, spherically-focused, or bi-cylindrically-focused. A calibration (or reference) signal is required and is used to deduce the measurement system efficiency function. This can be "invented" by the software using center frequency and bandwidth information specified by the user, or, alternatively, a measured calibration signal can be used. Defect types include flat-bottomed-hole reference reflectors, and spherical pores and inclusions. Simulation outputs include estimated defect signal amplitudes, root-mean-square values of grain noise amplitudes, and SNR as functions of the depth of the defect within the metal component. At any particular depth, the user can view a simulated A-, B-, and C-scans displaying the superimposed defect and grain-noise waveforms. The realistic grain noise signals used in the A-scans are generated from a set of measured "universal" noise signals whose strengths and spectral characteristics are altered to match predicted noise characteristics for the simulation at hand.
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The received signals from eddy current testing (ET) sensors are dependent on a large number of variables. These include conductivity, permeability, geometry, and defects in the material being tested, as well as sensor liftoff and orientation. In order to isolate the effects of any one of these properties from the others, multiple inspection frequencies are often used. This paper describes novel adaptations of a standard technique for combining (mixing) the data from multiple frequencies in order to isolate signals of interest. The adaptations were designed for the optimization of signal-to-noise ratio (SNR), where the "signal" is the information of interest and the "noise" is information from other system variables. For example, for detecting cracks in the presence of geometrical changes, the "signal" is the crack information and the "noise" is the information from the geometrical changes.
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An ultrasonic sensors system is commonly used to measure the wall thickness of buried pipelines in the transportation of oil and gas. The key of the system is to precisely measure time-of-flight difference (TOFD) produced by the reflection of ultrasonic on the inner and outer surfaces of the pipelines. In this paper, based on deep learning, a novel method termed Wave-Transform Network is proposed to tackle the issues. The network consists of two parts: part 1 is designed to separate the potential overlapping ultrasonic echo signals generated from two surfaces, and part 2 is utilized to divide the sample points of each signal into two types corresponding to before and after the arrival time of ultrasonic echo, which can determine the time-of-flight (TOF) of each signal and calculate the thickness of pipelines. Numerical simulation and actual experiments are carried out, and the results show satisfactory performances.
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There are many ultrasonic measurement methods that are used in nondestructive testing applications. Some typical applications include material property determination, microstructural characterization. and flaw detection. Ultrasonic parameters such as velocity and attenuation are most commonly required in these applications. The accuracy and repeatability of testing results are dependent on both the hardware used to generate and receive the ultrasonic waves and on the analysis software for calculating these parameters. In this study, five analysis algorithms were implemented on a computer for measuring wave speed in a pulse echo. immersion testing configuration. In velocity measurements comparisons were made between the overlap. cross-correlation. Fourier transform. Hilbert transform, wavelet transform algorithms. Velocity measurement was applied to an isotropic steel sample using the five analysis algorithms. Frequency-dependent phase/group velocity and attenuation were also measured using the Fourier transform and wavelet transform algorithms on a composite laminate containing voids.
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Two signal processing approaches are presented to improve imaging resolution in ultrasonic NDT of concrete structures.When low-frequency B-scan data are processed by Synthetic Aperture Focusing Technique(SAFT),wavepacket will be further elongated because low-frequency detection signal covers a relative long period.Wavepacket Decomposition Technique (WDT) is introduced here to solve this problem.This method uses a few parameters to describe the original signal so that it can avoid the processing of the whole time history of signals.In practical measurement,ringing of commercial ultrasonic transducers also affects the discrimination of the detected signal.A method based on digital filtering is proposed to build the compensation model of transducer system.By eliminating the ringings with the filter,the spatiotemporal resolution of ultrasonic imaging is improved.The efficiency of the methods has been proved by numerical simulation and experimental results.Imaging resolution is improved obviously and the embedded object in a test specimen is located accurately.
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This paper addresses high-resolution ultrasonic image reconstruction from Full Matrix Capture (FMC) data in the context of nondestructive testing (NDT). In order to reduce the numerical complexity, the time-domain data and ultrasonic model are projected into the image domain through a linear beamforming procedure. The resulting model is interpreted as a shift-variant convolution process, affected by non-stationary and colored noise. An interpolation procedure is built in order to account for the spatial variations of the resulting point spread function. Under the same methodological framework, an approximate whitening filter is proposed and incorporated in the forward model. Both constructions then allow fast computations and limited memory storage. Deconvolution is performed by minimizing the least-squares data misfit error, with a penalization term favoring sparsity and spatial continuity of the output images. Results with synthetic data show that the proposed approach gives performances close to the inversion of raw FMC data, while being computationally much more efficient. The method is finally applied to laboratory data for the inspection of a stainless steel block containing closely spaced and small side-drilled holes. Successful detection and separation is achieved for flaws with diameters six times smaller than the wavelength, and distant from each other by three times less than the resolution limit given by the Rayleigh criterion.
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Ultrasonic phased array Total Focus Method (TFM) is used to process the signal with the data obtained by the Full Matrix Capture(FMC) mode, which has the advantages of high imaging accuracy and high signal-to-noise ratio(SNR). However, the frequency dispersion of the Lamb wave largelyaffected the imaging accuracy of the TFM. Therefore, the wavelet analysis time domain filtering in multi-band method was proposed to decompose an ultrasonic wide frequency band signal into several narrow band ones for suppressing dispersion effect, furthermore, the damage signal is extracted without reference signal and thus the damage is imaged in frequency domain, which improves the imaging accuracy. In this paper also analyzes the whole process of ultrasonic excitation, the interaction between the incident wavefield and damage as well as the sensor receiving signal, and establishes a quantitative imaging method of inverse scattering model with the reflectivity of the damaged surface as the index,further improve the imaging accuracy. Moreover, a numerical model and an experimental platform for prefabricated artificial damage of metal aluminum plate were established to compare the imaging effects of TFM in frequency domain and TFM with inverse scattering model in frequency domain. The results show that new algorithm is more accurate in locating damage and has stronger characterization ability.
Ultrasonic imaging
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Several techniques of signal processing were introduced in ultrasonics NDT field. In thin samples the reflected signals are overlapping thus making detection of defects in these samples and accurate measurements impossible. It is thus necessary to enhance the visibility of the defect echo by signal processing techniques. In this context, we develop signal processing tools allowing detecting and locating the imperfections present in these materials. In this paper, we contribute by the development of some signal processing techniques based on time frequency and high resolution algorithms in order to enhance the resolution of flaw detection and to measure thin materials thickness. 1- We propose to implement temporal versions of methods known as high resolution like MUSIC, Root MUSIC and Eigen vectors method. These methods allow frequencies extraction in the case of the complex signals drowned in noise. 2- We apply time-frequency algorithms based on STFT, Wigner-Ville, Gabor transform on thin materials thickness measurement. A comparative study is carried out between all of these algorithms and is applied in separation of closer flaw echoes and thin materials thickness measurement. Satisfactory results are obtained with Gabor transform in measurement of few tenth (0.1) mm.
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