Total Focusing Method Imaging Research Articles

Effect of multiple scattering on ultrasonic imaging detection for titanium alloy

Abstract Ultrasonic imaging for the micro-pores can evaluate the performance state of the parts. Wave multiple scattering caused by the micro-pores is significant for ultrasonic imaging. This paper provides an ultrasonic imaging method combined with a total focusing method (TFM) to inspect the micro-pores in additive manufacturing titanium alloys. Initially, a geometric model of the titanium alloy containing micro-pores was created using k-wave software for Phased Array Ultrasonic Testing (PAUT) detection simulations. Subsequently, the time-domain TFM algorithms were developed to generate defect images based on the simulation data. Finally, the influence of wave multiple scattering on TFM imaging has been analyzed.

Ultrasonic characterization of defects in polycrystalline materials based on TFM image reconstruction using denoising diffusion probabilistic models

This paper presents a refined ultrasonic total focusing method (TFM) for accurately characterizing key defect parameters including size, orientation, and location in complex materials. The proposed approach aims to accurately reconstruct the defect shape in images compromised by grain interference. First, a TFM image database was generated with varying defect parameters and grain noise levels by adopting a custom forward modeling process based on superposition of defect and grain scattering data. Second, a new architecture called TFM-DDPM was then proposed which incorporates TFM and the conditional denoising diffusion probabilistic model (DDPM). Third, pixel-level uncertainty can be quantified through repeated sampling of noise which follows a standard normal distribution. An acceptance criterion for the reconstruction was further established by analyzing the quantified uncertainty. In simulation, the median Dice coefficient between the de-noised TFMs of 30° cracks and their corresponding ground truth images increased from 0.5585 to 0.9137 using the proposed approach, and the median IoU metric rose from 0.3875 to 0.8408. By applying the 6-dB drop approach to the reconstructed images, the root mean squared errors (RMSEs) of size, angle and location were decreased by 93.59%, 86.90% and 86.25%, respectively. It is also demonstrated that our method can reject 69.19% of incorrect characterization results caused by high levels of noise and/or images with steeply inclined (e.g., 45°) cracks, while accepting 97.29% accurate results. Experimental results obtained on nickel-based alloy K403 specimens also exhibited significant improvements. For 2–3 mm cracks, the average sizing error reduced from 0.46 mm to 0.13 mm, and the error in crack angle decreased from 28.63° to 1.50°. Comparable performance enhancements were observed for 4–5 mm cracks using the proposed approach.

Multi-mode high resolution TFM imaging of microdefects based on laser ultrasonic full matrix capture

The identification and visualization of submillimeter microdefects have long been a critical focus within the realm of ultrasonic testing. The utilization of high-frequency signals is imperative due to the small acoustic interaction structure. An additional benefit of using lasers is their non-contact nature and ability to utilize multiple imaging modes, effectively reducing false or missed detections and enhancing credibility. This study proposes an advanced optimization algorithm for imaging and quantitative evaluation of submillimeter microdefects using laser ultrasonic full matrix capture (FMC) and total focusing method (TFM). The analysis of acoustic directionality under laser excitation and detection provides a foundation for mode selection. In preprocessing, the multi-scale principal component analysis (MSPCA) method is introduced to suppress coherent noise and blind spots. Moreover, phase coherence methods are implemented to further reduce mode artifacts, background noise, and residual surface acoustic wave (SAW). Additionally, multi-mode imaging and mode fusion are achieved using reflected longitudinal waves, reflected shear waves, converted longitudinal waves, and converted shear waves. Experiments were carried out on two different types of microdefects, namely side drilled holes (SDHs) and blind holes (BHs), resulting in high resolution TFM imaging with a minimum BH size of 0.3 mm. The outcomes demonstrate the efficacy of MSPCA in suppressing coherent noise, with further enhancement in imaging quality after phase coherence weighting. In addition, the −6 dB threshold of multi-mode images also provided the size information. This research is poised to contribute to the resolution of quality assessment challenges in powder metallurgy, welding, and additive manufacturing processes.

Ultrasonic TFM imaging inspection of large complex rings using defect quantitative evaluation mappings

ABSTRACT Large complex rings are widely used in wind power energy, aerospace, large construction machinery. There are different scales of defects in the manufacturing process for large complex rings. Because of the complex cross-section and large thickness, it is difficult to quantitative evaluation of defects by conventional ultrasonic technology. Therefore, the ultrasonic total focusing method (TFM) defect quantitative inspection for large complex rings is proposed in this paper. First, the TFM inspection technology of large complex rings for simultaneous scanning of the end-face and side-face is designed. Then, the maximum amplitudes of defects are collected at each grid point of the imaging area by the TFM inspection of calibration specimens. The Φ1.5mm defect quantitative evaluation mappings are obtained by the interpolation fitting algorithm. Finally, the ultrasonic TFM and B-scan inspection experiments of the Φ3440mm large ring are carried out. The results show that the TFM quantitative evaluation mapping has higher accuracy compared to the conventional B-scan method. Especially for the Φ1.5mm defects, the average error of TFM quantitative evaluation is 2.92%. The proposed method can effectively identify defects of other sizes that exceed the calibration mapping. This work has industrial application value for developing ultrasonic TFM inspection of large complex components.

Ultrasonic guided wave phased array based on reconstructed pulse-echo signals from phase information

Abstract Aiming at the inherent dispersion characteristics of ultrasonic guided waves and the problems of gratings and side lobes in classical total focusing method (TFM) imaging algorithms, this paper proposes a full focus imaging algorithm based on reconstruct signals from phase information. Utilizing the numerical solution of the dispersion curve in the wavenumber domain, the time domain signal of Lamb wave is propagated backward to revise the changes in waveform shape caused by dispersion, and the flight time of the pulse-echo is eliminated simultaneously. The phase of pulse-echo after dispersion compensation should be consistent with the excitation signal. And the pulse-echo should have a large waveform correlation coefficient with the excitation signal. For each imaging point, dispersion compensation for all collected signals is respectively implemented so that the sign coherence factor (SCF) and the waveform correlation factor (WCF) between pulse-echo and excitation waveform can be extracted to reconstruct scattering signals. The reconstructed signals only contain information about the pulse-echo from defect. TFM imaging of the reconstructed signal can effectively suppress the grating side lobes of classical TFM imaging and improve the signal-to-noise ratio (SNR).

An approximation method to determine the travel time in total focusing imaging of carbon fiber-reinforced laminates

Abstract It is critical to accurately determine the propagation time in testing objects for ultrasonic array imaging using the Total Focusing Method (TFM). However, it poses challenges for the calculation of wave propagation time in carbon fiber reinforced polymer (CFRP) laminates because of the material anisotropy and inhomogeneity. This paper introduces a novel approach for computing ultrasonic wave travel time to enhance the TFM image of CFRP laminates. This method, termed the layer-by-layer acoustic travel time approximation (L-L TravelTimeAppro), considers the anisotropy within each monolayer and the heterogeneity of CFRP. It approximates the propagation path as straight throughout the material and computes the wave propagation duration utilizing the group velocity in each ply. The application of this method to TFM imaging is referred to as TravelTimeAppro-TFM. Experiments were conducted in a specially designed CFRP laminate. Subsequently, TFM imaging was processed by using propagation time calculated by isotropic, Dijkstra, and L-L TravelTimeAppro methods, respectively. The results indicate that TravelTimeAppro-TFM outperforms isotropic-TFM in terms of imaging amplitude gain with a maximum gain of 4.67 dB. Furthermore, it offers superior computational efficiency compared to Dijkstra-TFM.

Defect recognition and classification from ultrasonic phased array total focusing method imaging based on domain adaption

Abstract With the rapid development of China’s economy, the demand for oil and gas resources is continuously increasing, leading to the expansion of the scale of oil and gas long-distance pipelines. Consequently, the risk of safety incidents in oil and gas pipelines is on the rise. Welding defects are identified as one of the significant factors contributing to safety incidents in long-distance oil and gas pipelines. In comparison to traditional conventional ultrasonic detection techniques, which exhibit low efficiency and accuracy, ultrasonic phased array detection technology has the capability to accurately detect welding defects in oil and gas long-distance pipelines. This is particularly true with the development of total focusing method imaging technology, enabling quantitative defect analysis and localization in oil and gas long-distance pipelines.However, in the current scenario, analyzing the types of defects in massive data from long-distance pipelines requires human intervention, leading to low detection efficiency and subjective influence on result analysis. Addressing challenges such as low efficiency in human evaluation of defects in ultrasonic phased array detection, low accuracy in traditional image recognition methods, strong subjectivity in manually extracting features, and the absence of a standardized industrial dataset for ultrasonic phased array detection defects, especially in the case of limited data on ultrasonic phased array detection of welding defects, this study focuses on small sample defects in phased array detection images. The study proposes an improved ResNet50 algorithm through domain adaptation in a deep neural network, trained on a large-scale model data, and then applied to ultrasonic phased array detection data. This approach achieves accurate identification and classification of typical volumetric defects (such as pores) and typical area defects (such as cracks) in the phased array detection of welding defects, laying the foundation for intelligent recognition of defects in long-distance oil and gas pipelines.

Adaptive total focusing method for ultrasonic full-matrix imaging of anisotropic CFRP laminates

Abstract The application of total focusing method (TFM) for imaging of Carbon fiber reinforced plastics (CFRP) with ultrasonic phased array is particularly challenging due to the layered structure and anisotropy of CFRP. To improve the TFM imaging performance, the precise knowledge of complex ultrasonic paths in anisotropic CFRP laminates should be obtained for accurate calculation of time of flights (TOFs), which makes the inspection process complicated and time-consuming. In this work, an adaptive TFM is developed for full-matrix imaging of CFRP laminates using ultrasonic phased array. Based on the acquired full matrix data, the elastic constants, including C 11, C 22, C 55, and C 13, can be directly derived using TOFs of A-scan signals. Based on the measured elastic constants, group velocity profiles of quasi-pressure (QP) wave were then calculated for different stacking sequences, from which the TOFs were corrected. To verify the effectiveness of proposed method, simulations and experiments are demonstrated on the CFRP laminates with different stacking sequences. As a representative example, the delamination defects are showcased. Compared with isotropic TFM, the SNR of proposed method is increased by 10.2-18.0 dB. The simulated and experimental results agreed well with each other, indicating that the feasibility of proposed method in full matrix imaging of anisotropic CFRPs.

Research on Phased Array Ultrasonic Imaging Method Based on Time Reversal Theory

Abstract With the wide application of additive manufacturing components and composite materials, it is of great significance to detect defects with non-destructive testing methods in the material manufacturing process and in service. Due to the strong attenuation and inhomogeneity characteristics, it is inefficient and poor imaging results to detect its internal defects by conventional ultrasonic testing methods. To improve the detection accuracy, this paper proposes an improved imaging algorithm with time reversal-total focusing method (TR-TFM) imaging technology to improve the defects detection in strongly attenuating materials. The experimental results show that the amplitude of the defect signal is increased by 8.30 dB, and compared with total focusing method (TFM), the diameter of the detected defect with TR-TFM imaging is increased from 1.20 mm to 1.60 mm, which is more adjacent to the accurate size. Meanwhile, with the sparse matrix based on FMC data, TR-TFM could obtain the same image quality with the approximate time of TFM imaging.

Sparse TFM imaging of different-scale phased array based on the DWSO algorithm

ABSTRACT Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection.

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Abstract Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

Phased Array Ultrasonic Imaging for Characterizing Steel with an Elevated Temperature Gradient.

In this research work a phased array ultrasonic Testing (PAUT) is used for inspecting steel at elevated temperature. Velocity of ultrasound is affected by temperature of medium, and the study of ultrasound is done when it is propagating through High temperature media of steel. For this purpose, a steel sample is heated from one surface and temperature gradient is generated in it. The methods like Full Matrix Capture (FMC) and Total Focusing Method (TFM) are used to generate real time ultrasonic image at particular instant of time. It is a method to generate the real time two-dimensional ultrasonic image. The FEA model of steel with temperature gradient is created and the ultrasonic inspection methods, FMC and TFM are applied in order to generate the image by taking elementary A-scan data from simulation and plotting the same to generate the image. The simulation results can be validated using high temperature experiments with steel samples and the experimental data can be taken to use TFM algorithm for ultrasonic evaluation. These TFM images provide a visual representation of the steel cross section at elevated temperature at specific instances, facilitating early-stage in-situ monitoring and optimization of the steel solidification process.

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.