Total Focusing Method Imaging Research Articles

A novel RCACycleGAN model is proposed for the high-precision reconstruction of sparse TFM images

The sparse total focusing method (TFM) has been shown to enhance the computational efficacy of ultrasound imaging but the image quality of ultrasound regrettably deteriorates with an increase in the sparsity rate of array elements. Deep learning has made remarkable advancements in image processing and cycle-consistent generative adversarial networks (CycleGANs) have been extensively employed to reconstruct diverse image categories. However, due to the incomplete extraction of image feature information by the generator and discriminator in a CycleGAN, high-quality sparse TFM images cannot be directly reconstructed using CycleGANs. There is also a risk of losing crucial feature information related to minor defects. As a result, this paper modifies the generator and discriminator in the CycleGAN to construct a new relativistic discriminator and coordinate attention CycleGAN (RCACycleGAN) model, which enables high-precision reconstruction of sparse TFM images. The addition of the coordinate attention module to the CycleGAN enhances the defective feature representation by fully considering the channel and spatial correlation between regions and using the fusion of spatially perceived feature maps in different directions. It solves the problem of easy loss of defective key feature information. The relativistic discriminator replaces the PatchGAN discriminator in the CycleGAN and evaluates the quality of both real and sparse TFM reconstructed images to ensure a relative image quality evaluation. This process solves the problem of unstable image quality of the sparse TFM reconstructed image. Experimental results demonstrate that RCACycleGAN can stably reconstruct sparse TFM images even in small sample dataset scenarios. The proposed network model reconstructs images with better accuracy, including in terms of structural similarity, defect roundness and area, and has a shorter training time than several existing network models.

Automated Wall Thickness Evaluation for Turbine Blades Using Robot-Guided Ultrasonic Array Imaging

Abstract Nondestructive testing has become an essential part of the maintenance of modern gas turbine blades and vanes since it provides an increase in both safety against critical failure and efficiency of operation. Targeted repairs of the blade's airfoil require localized wall thickness information. This information, however, is hard to obtain by nondestructive testing due to the complex shapes of surfaces, cavities, and material characteristics. To address this problem, we introduce an automated nondestructive testing system that scans the part using an immersed ultrasonic array probe guided by a robot arm. For imaging, we adopt a two-step, surface-adaptive Total Focusing Method (TFM) approach. For each test position, the TFM allows us to identify the outer surface, followed by calculating an adaptive image of the interior of the part, where the inner surface's position and shape are obtained. To handle the large volumes of data, the surface features are automatically extracted from the TFM images using specialized image processing algorithms. Subsequently, the collection of 2D extracted surface data is merged and smoothed in 3D space to form the outer and inner surfaces, facilitating wall thickness evaluation. With this approach, representative zones on two gas turbine vanes were tested, and the reconstructed wall thickness values were evaluated via comparison with reference data from an optical scan. For the test zones on two turbine vanes, average errors ranging from 0.05 mm to 0.1 mm were identified, with a standard deviation of 0.06–0.16 mm.

Optimal Design of Sparse Matrix Phased Array Using Simulated Annealing for Volumetric Ultrasonic Imaging with Total Focusing Method.

The total focusing method (TFM) is often considered to be the 'gold standard' for ultrasonic imaging in the field of nondestructive testing. The use of matrix phased arrays as probes allows for high-resolution volumetric TFM imaging. Conventional TFM imaging involves the use of full matrix capture (FMC) for ultrasonic signals acquisition, but in the case of a matrix phased array, this approach is associated with a huge volume of data to be acquired and processed. This severely limits the frame rate of volumetric imaging with 2D probes and necessitates the use of high-end equipment. Thus, the aim of this research was to develop a novel design method for determining the optimal sparse 2D probe configuration for specific conditions of ultrasonic imaging. The developed approach is based on simulated annealing and involves implementing the solution of the sparse matrix phased array layout optimization problem. In order to implement simulated annealing for the aforementioned task, its parameters were set, the acceptance function was introduced, and the approaches were proposed to compute beam directivity diagrams of sparse matrix phased arrays in TFM imaging. Experimental studies have shown that the proposed approach provides high-quality volumetric imaging with a decrease in data volume of up to 84% compared to that obtained using the FMC data acquisition method.

Adaptive ultrasonic full-matrix imaging of internal defects in CFRP laminates with arbitrary stacking sequences

The phased-array-based ultrasonic full-matrix imaging of carbon fibre-reinforced polymer (CFRP) laminates with total focusing method (TFM) is promising yet challenging due to the layered configuration and strong anisotropy. Traditionally, the precise a priori knowledge of ultrasonic ray paths in anisotropic CFRP laminates are required for accurate TFM imaging, making the inspection process complicated and time consuming. In this work, an adaptive ultrasonic-imaging method is proposed for arbitrarily anisotropic CFRP laminates through full-matrix capture (FMC) and TFM. Firstly, the elastic constants are derived from the FMC data, upon which the 3D group-velocity profiles of ultrasonic waves in CFRP laminates with arbitrary stacking sequences can be calculated. The anisotropic effect is then adaptively corrected for TFM. To verify the effectiveness of proposed method, simulations and experiments are conducted on different anisotropic CFRP laminates with mimicked defects, such as delamination, cracks, voids, etc. The simulated and experimental results well agree with each other, indicating that the proposed method can accurately reconstruct the internal defects of different types. This work provides a convenient and comprehensive avenue towards the ultrasonic inspection of arbitrarily anisotropic CFRP laminates, which markedly benefit the quality control of CFRP components in various fields.

Fastener hole inspection using 2D phased array

Fastener hole cracks, resulting from stress concentration around the hole, are potential sources of failure in mechanical structures. Traditionally, inspection involves manually moving a single ultrasonic probe around the hole or using an eddy current probe to move through the hole. However, single ultrasonic probe inspection is time-consuming and needs skilful operators. Eddy current inspection requires the removal of the fastener in advance and does not allow easy crack sizing and visualization. This paper proposes a novel approach that combines a 2D phased array with a coupling block to enable complete fastener hole inspection from a single array position. A single full matrix capture (FMC) data set is collected, from which 3D volumetric images are formed. To obtain clear images of the geometry and potential defects, half-skip and multi-skip total focusing method imaging algorithms are investigated together with transmitter–receiver pairs selection methods to detect different target features. Experimental results demonstrate that the proposed inspection approach can eliminate dead zone problems and capture all target information from a single inspection position. These methods show great potential in enhancing the efficiency and reliability of fastener hole inspections.

Highly constrained low level fluid sensing in process pipes utilizing non-invasive ultrasonic methods

Determination of the presence of hazardous waste in sealed pipes is a major issue of concern in many industries. Typically, when hazardous process piping needs to be opened, it is very difficult to truly know whether the pipe is empty, and thus free of hazardous material. Therefore, standard procedure is to assume worst case scenario and dedicate significant resources to Hazmat teams and containment when opening the piping or preparing it for decommissioning. In this work, we explore three separate ultrasonic-based techniques for very low-level fluid sensing in process piping. It was found that ultrasonic techniques suffer from several issues, including guided wave contamination, local resonance, overlap from other echoes, and more. To overcome these issues, three separate techniques were explored, including pulse-echo with subtraction, total focusing method (TFM) imaging, and another technique that exploits the local resonance of the pipe when it is fluid-loaded. It was found that, with proper calibration and signal processing, pulse-echo is an easy-to-use technique down to relatively low fluid levels. However, the best performance came from modified total focusing method imaging, which could detect extremely low fluid levels while being robust from false negatives (detecting no waste when there was waste).

Layer-by-layer acoustic travel time approximation using ray theory for total focusing method imaging in carbon fiber reinforced polymer

This paper proposes a layer-by-layer acoustic travel time approximation method based on ray theory for total focusing method (TFM) imaging in carbon fiber reinforced polymer (CFRP) laminates. The method considers the anisotropy in every monolayer and heterogeneity of CFRP, which approximates the path of propagation as straight in the whole material. The application of this method to TFM imaging is called TravelTimeAppro-TFM. In comparison to isotropic-TFM and Dijkstra-TFM, the experimental results indicated that TravelTimeAppro-TFM outperforms isotropic-TFM in terms of imaging amplitude gain with a maximum gain of 4.67 dB. On the other hand, this approach reduces the computational work compared to Dijkstra-TFM. The proposed method demonstrates significant improvements in both focusing performance and the speed of calculation. This paper also investigates the effective angular range of the layer-by-layer acoustic travel time approximation method through experimental and finite element simulation analysis.

Towards a multi-fidelity deep learning framework for a fast and realistic generation of ultrasonic multi-modal Total Focusing Method images in complex geometries

This paper presents a deep-learning surrogate model tailored for a fast generation of realistic ultrasonic images in the Multi-modal Total Focusing Method (M-TFM) framework. The method employs both physics- and data- driven data-sets. To this end, we propose a Conditional U-Net (cU-Net) to perform a controlled generative process of high-resolution M-TFM images by spanning the set of inspection parameters, employing both the experimental data (high-fidelity acquisitions) and the simulated ones (a low-fidelity counterpart). Once trained on experimental and simulated images, the cU-Net embodies an enhanced realism, learnt from the experimental data, coupled with a quasi-real-time prediction that prevents the need for extra simulations. Moreover, our surrogate model provides a controlled M-TFM generation conditioned by the steering parameters of the simulation as well as by the physics underlying the ultrasonic testing schema. The performances of our approach are demonstrated in a case study of M-TFM images of a component with planar defects in a complex weld-like profile. Furthermore, we consider uncertainties in M-TFM image parameters reconstruction in both numerical and experimental data to reproduce the on-site inspection. Additionally, we show how the trained neural network can learn its inner layers (i.e., the cU-Net layers) according to the physical parameters at stake so that it can be considered an white-box model enabling a qualitative interpretation of the generative process.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

Optimized ultrasonic total focusing imaging of diverse and multiple defects in crossply CFRP: Floquet wave theory, numerical simulation, and experimental validation

Carbon fiber reinforced polymers (CFRPs) with large thickness (≥20 mm) are being extensively used for aerospace structures, and are prone to the manufacturing and in-service defects due to the complexity and difficulty of manufacturing. This study addresses an intensive interrogation of multi-parameter optimization towards a high-quality ultrasonic total focusing method (TFM) imaging for diverse and multiple defects in crossply CFRP. Firstly, material parameters of unidirectional laminate are inversely constructed based on Christoffel equation by inputting the experimentally measured wave speed, based on which the wave speed and homogenization region for the crossply CFRP are obtained with the Floquet wave theory. Then, the wave propagation at crossply CFRP with preset side drilled holes (SDHs) at depths of 4 mm and 12 mm is modelled, from which the full matrix data are extracted for TFM imaging using multiple parameter optimization, including wave velocity correction, propagation restriction angle, and wave central frequency. With the proposed signal-to-noise ratio as the criterion of TFM imaging quality, the combined parameters for optimized imaging are sorted out, which are strongly related to the feature of propagating and non-propagating waves calculated with the Floquet wave homogenization theory. A further experimental validation is performed to image multiple SDHs and delamination with size over 2 mm and depths covering 2.5 mm to 17.5 mm. This systematic analysis of parameter optimization for TFM imaging in thick CFRP sets up the foundation for the TFM towards a real engineering application of CFRP detection.

Compressive Sensing Strategy on Sparse Array to Accelerate Ultrasonic TFM Imaging

Phased array ultrasonic testing (PAUT) based on full matrix capture (FMC) has recently been gaining popularity in the scientific and nondestructive testing communities. FMC is a versatile acquisition method that collects all the transmitter-receiver combinations from a given array. Furthermore, when postprocessing FMC data using the total focusing method (TFM), high-resolution images are achieved for defect characterization. Today, the combination of FMC and TFM is becoming more widely available in commercial ultrasonic phased array controllers. However, executing the FMC-TFM method is data-intensive, as the amount of data collected and processed is proportional to the square of the number of elements of the probe. This shortcoming may be overcome using a sparsely populated array in transmission followed by an efficient compression using compressive sensing (CS) approaches. The method can therefore lead to a massive reduction of data and hardware requirements and ultimately accelerate TFM imaging. In the present work, a CS methodology was applied to experimental data measured from samples containing artificial flaws. The results demonstrated that the proposed CS method allowed a reduction of up to 80% in the volume of data while achieving adequate FMC data recovery. Such results indicate the possibility of recovering experimental FMC signals using sampling rates under the Nyquist theorem limit. The TFM images obtained from the FMC, CS-FMC, and sparse CS approaches were compared in terms of contrast-to-noise ratio (CNR). It was seen that the CS-FMC combination produced images comparable to those acquitted using the FMC. Implementation of sparse arrays improved CS reconstruction times by up to 11 folds and reduced the firing events by approximately 90%. Moreover, image formation was accelerated by 6.6 times at the cost of only minor image quality degradation relative to the FMC.

Accelerating Algorithm for Total Focusing Method Imaging Based on Optimization of Full Matrix Data

Accelerating Algorithm for Total Focusing Method Imaging Based on Optimization of Full Matrix Data

Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer

For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is required to be inspected from the buttering layer side. The buttering layer exhibits very high elastic anisotropy due to elongated columnar grains with preferred orientations. In this paper, the effect of elastic anisotropy on the phased array ultrasonic imaging of defects in the buttering layer is demonstrated for data acquired in full matrix capture (FMC) mode and reconstructed with the total focusing method (TFM). The anisotropy in the buttering layer leads to distorted flaw images, which limits the lateral resolution and defect detection sensitivity. Angle-dependent ultrasonic velocity measured in through-transmission FMC mode has been used for processing the FMC data to obtain high-resolution TFM images with improved sensitivity. The velocity values used are in line with the grain orientations observed by electron-backscatter diffraction (EBSD) studies. Further, an alternate approach is also proposed to obtain a TFM image with improved resolution using a suitable isotropic velocity. The approach can be implemented in any commercial phased array ultrasonic system having the FMC-TFM feature.

Model-Based Parametric Study of Surface-Breaking Defect Characterization Using Half-Skip Total Focusing Method

As the demand of structural integrity in manufacturing industries is increasing, the ultrasonic array technique has drawn more attention thanks to its inspection flexibility and versatility. By taking advantage of the possibility of individual triggering of each array element, full matrix capture (FMC) data acquisition strategy has been developed that contains the entire information of an inspection scenario. Total focusing method (TFM) as one of the ultrasonic imaging algorithms, is preferably applied to FMC dataset since it uses all information in FMC to synthetically focus the sound energy at every image pixel in the region of interest. Half-skip TFM (HSTFM) is proposed in multi-mode TFM imaging that involves a backwall reflection wave path, so that the defect profile could be reconstructed for accurate defect characterization. In this paper, a method involving Snell’s law-based wave mode conversion is proposed to account for more reasonable wave propagation time when wave mode conversion happens at backwall reflection in HSTFM. A series of model based simulations (in software simSUNDT) are performed for parametric studies, with the intention of investigating the capability of defect characterization using HSTFM with varying tilt angle and relative position of surface-breaking notch to array probe. The results show that certain TFM modes could help with defect characterization, but the effectiveness is limited with varying defect features. It is inappropriate to address a certain mode for all characterization perspectives but rather a combination, i.e., multi-mode TFM, should be adopted for possible interpretation and characterization of defect features.

On the uncertainty in the segmentation of ultrasound images reconstructed with the total focusing method

This study presents an investigation into the uncertainty of images reconstructed by the total focusing method (TFM) using non-destructive evaluation (NDE) and phased array probes. Four neural network architectures based on the U-Net model are used to probabilistically segment TFM images and evaluate the uncertainty of the segmentation results. The models are trained on three simulated phased-array datasets, which contain various sources of uncertainty from the simulated defects or surrounding material. Physical limitations, such as the defect’s shadow zone, led to high uncertainty. Results demonstrate that probabilistic segmentation can be helpful in determining the source of uncertainty within segmented TFM images. The model performance is investigated based on several metrics, and the influence of defect size on model performance is shown. The probabilistic U-Net shows the highest F1-score overall test datasets. This study contributes to the advancement of NDE using TFM by providing insights into the uncertainty of the reconstructed images and proposing a solution for addressing it.

Assessment of Laser-Generated Ultrasonic Total Focusing Method for Battery Cell Foil Weld Inspection

ABSTRACT The feasibility of using laser-generated ultrasonic Total Focusing Method (TFM) was assessed for guided ultrasonic waves in finite plates. The application under consideration is for inspection of ultrasonically welded battery tab-to-electrode foil stack joints. The testing constraints for this weld necessitate couplant-free, remote, guided-wave conditions making laser ultrasonic TFM an ideal inspection technique. It was determined that laser-generated guided wave TFM can be used to remotely assess defects in a finite plate when the defects are strong reflectors in the plane of wave propagation. The finite dimensions of the tab require a strong understanding of the edge reflection effects on the TFM image. The guided wave modes used in this study were strongly affected by scattering due to the complex weld geometry, which most resembles that of a periodic triangular grated wave guide. Future work will investigate methods to compensate for the strong scattering/guided wave effects, the use of other guided wave geometries, out of plane TFM reconstruction for other weld defect types, as well as apodization effects.

Simulation of ultrasonic TFM/FMC imaging for porosity clusters using multiple scattering modelling: Quantitative analyses and experimental comparisons

Ultrasonic imaging using the Total Focusing Method (TFM) is very useful for locating and sizing defects accurately. However, when imaging clusters of pores, the results may be distorted by multiple scattering phenomena. Several simulations are performed in a 2D context considering different scattering model approaches: single scattering, single scattering including defect shadowing, and multiple scattering. In order to reproduce clusters of pores, we use sets of side-drilled holes to impose well-controlled properties (position and size) for comparison with experimental tests. Comparisons of the simulated TFM images with the experimental ones show qualitative and quantitative improvements when using the multiple scattering model, thanks in particular to a better consideration of shadowing and other interaction effects between defects.

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Under the same detection frequency and depth, when the center spacing of multiple defects is less than the resolution threshold determined by the Rayleigh criterion, it is challenging to achieve super-resolution imaging of multiple defects using the ultrasonic total focusing method (TFM). A multilevel deep learning network is proposed as a super-resolution reconstruction method for ultrasonic Lamb wave TFM images. The first-level network is a detection network that uses a Resnet50 with more convolutional layers to improve the linear expression capabilities of the neural network. It introduces residual structure to solve low accuracy issues of multi-convolutional layers so that the Resnet50 can accurately detect defects from TFM images. The second-level network is a super-resolution reconstruction network that uses a Deeplab v3+ with a dilated convolutional layer. It controls the receptive field without changing the image feature size of the TFM image. With this model, a super-resolution reconstruction of multiple defects with a center spacing less than the resolution threshold is realized by extracting the detailed features of defects in the TFM image. Experimental results show that when the defect center spacing is greater than the resolution threshold determined by the Rayleigh criterion, the super-resolution reconstruction method improves the calculation accuracy of the defect center spacing by 4.7% and the calculation accuracy of the defect area by 93.7% compared with TFM. When the defect spacing is less than the resolution threshold, the method can still identify and accurately calculate the center spacing of multiple defects.

Acoustic Path Filtering for Improved Multimode Total Focusing Method Inspection

Total focusing method (TFM) is an ultrasonic testing (UT) technique that provides nondestructive testing (NDT) inspectors with new imaging modes, enabling more accurate detection, sizing, and representation of challenging defects. While TFM may offer convenient, nearly true-to-geometry imagery as the inspection result, it is often detrimentally affected by mode conversion artifacts. Current standards, such as ASME Section V, place the burden on the inspector to explain the origins of those artifacts, impacting the productivity and reliability of the inspection. A method enabling direct control of the ultrasonic wave propagation modes—that is, transverse wave (T) or longitudinal wave (L)—through each interface of the acoustic path is proposed and evaluated in this paper. This control is achieved after the full matrix capture acquisition by modulating, according to the desired propagation mode, the gain applied on the individual paths within the summation process. This leads to the formation of a Path-Filtered Total Focusing Method image. Empirical results on various use cases show considerable improvement of the signal-to-noise ratio through the almost complete elimination of signals originating from undesired paths.

Ultrasonic multi-view data merging using the vector coherence factor

Over the years, ultrasonic imaging, both in the context of medical and industrial applications, has ushered in the development of many advanced techniques, some based on the use of the phase of the measured signal instead of its amplitude. These techniques use what is called coherence factors to enhance image quality. Leveraging these factors when calculating the image provides an effective solution to the low restitution of small scatterers, where specular reflectors in conventional ultrasonic imaging are strongly restituted, by weighting an amplitude-based beamformer output. In the field of nondestructive testing, this solution generally boils down to multiplying the well-known Total Focusing Method (TFM) image by a coherence factor map which is also obtained through delay-and-sum (DAS). It has also been demonstrated that coherence factor maps, especially in the case of the vector coherence factor (VCF), can be used as interpretable images themselves, which then results in an amplitude-free imaging process with many potential advantages. Additionally, the fusion of multi-view (or multi-mode) TFM images is still of particular interest since the burden of selecting a single or several views for an inspection scenario highly depends on the geometry and position of the defect that needs to be identified. Although sensitivity maps have been introduced to help in the selection of relevant views, no similar tool currently exists for views generated using directly coherence factor maps. In this study, two different merging approaches for multi-view VCF images are explored and evaluated: pixel-by-pixel summation and maximum pixel value selection. A probabilistic threshold, derived from the VCF statistical background, is also defined to enhance the resulting merged view contrast. The images obtained through the proposed methods were evaluated both qualitatively and quantitatively using the Contrast-to-Noise Ratio (CNR). Experiments were conducted on two sets of samples. The first one was a 20 mm thick steel plate with Flat Bottom Holes (FBH) machined on its edge mimicking reflectors used for calibration of a circumferential weld inspection apparatus. The second one was composed of two 19.05 mm (34′′) thick steel plates with EDM notches at several angles centered at the mid-wall or surface-breaking on the backwall. Since the experimental setup, consisting of a 60-element probe, with a center frequency of 7.5 MHz, standing on a 36°Rexolite wedge, involved exciting mostly shear waves, only views composed of the transversal wave paths were considered for merging. Results show that by applying either one of the proposed merging methods, all artificial defects are reconstructed with a satisfactory CNR (above 30 dB), irrespective of the shape or location of the defects. Overall, the pixel-by-pixel summation and the maximum pixel value methods gave similar results. Applying the proposed threshold on the merged view improved the CNR by up to 15.8 dB. Finally, these simple but generic and robust multi-view merging methods and their associated thresholds could be employed while using a simplified hardware setup, as demonstrated previously in the literature.