C-scan Images Research Articles

Focused Electromagnetic Acoustic Transducers for Lamb Wave Inspection

Abstract Ultrasonic inspection of conductive samples can be performed using electromagnetic acoustic transducers (EMATs). These allow generation of guided waves, such as Lamb waves, in thin plates. Lamb waves are used for defect screening as they can travel long distances with relatively little attenuation. However, large wavelengths are used, which results in poor sensitivity to subwavelength sized defects. This work presents methods for using Lamb waves to detect smaller defects. Curved, geometrically focused EMATs were used to generate and detect Lamb waves in a 1 mm thick aluminum plate. The focal spot of these EMATs is investigated and the dependence of the focal spot dimensions and locations on the wavelength of the generation signal are presented. An array of geometrically focused detection EMATs was used to measure a 10×10×0.5mm square machined defect, simulating wall thinning. Perturbations in the pulse transmission in the region of the defect were detected, along with reflections, with each detector in the array sensitive to different defect features. B-scans and C-scans are presented utilizing these perturbations and reflections to locate, size, and image the machined defect. Analysis of data from the B-scan is shown to accurately size the defect width to within ±0.73mm. Very good agreement is shown between the C-scan images and the known location and orientation of the defect. Data fusion techniques are presented to combine data sets from different receiver EMATs and increase the defect detection capability, accuracy, and precision, with the potential for full defect sizing demonstrated.

Ultrasonic Non-Destructive Testing and Evaluation of Stainless-Steel Resistance Spot Welding Based on Spiral C-Scan Technique.

In order to achieve the non-destructive testing and quality evaluation of stainless-steel resistance spot welding (RSW) joints, a portable ultrasonic spiral C-scan testing instrument was developed based on the principle of ultrasonic pulse reflection. A mathematical model for the quality evaluation of RSW joints was established, and the centroid of the ultrasonic C-scan image in the nugget zone of the RSW was determined based on the principle of static moment. The longest and shortest axes passing through the centroid in the image were extracted, and the ratio of the longest axis to the shortest axis (RLS) factor and the average of axis (AOA) factor were calculated, respectively, to evaluate the quality of the joint. To study the effectiveness of the detection results, tensile tests, and stereo analysis were conducted on the solder joints after sampling. The results indicate that this detection method can realize online detection and significantly improve the detection efficiency; the detection value of internal defect size is close to the true value with an error of 0.1 mm; the combination of RLS and AOA factors can be used to evaluate the mechanical properties of RSW joints. This technology can be used to solve the NDT, evaluate problems of RSW joints, and realize engineering applications.

Debonding inspection with an enhanced miniature EMAT based on multiple pulse echoes

The present paper proposes a non-contact ultrasonic testing method for the debonding inspection of metal-based composite structures, utilizing an improved electromagnetic acoustic transducer (EMAT). The enhanced EMAT incorporates a carbonyl iron powder coil backplate and a small butterfly coil to enhance the detecting ability for interfacial bonding. A two-dimensional finite element model is developed to quantify the effectiveness of the carbonyl iron powder coil backplate on increasing the eddy current density and the magnetic flux density in the specimen. An experimental setup was designed using the Ritec SNAP 5000 and the improved EMATs to examine an aluminium-epoxy resin composite structure. Ultrasonic C-scan images of the specimen's bonding interface were obtained from the experiment. Both simulation and experimental results show that carbonyl iron powder coil backplates can increase the efficiency of the EMATs. By investigating the influence of echo number on debonding detection, it was revealed that employing higher-order echoes and utilizing carbonyl iron powder as the coil backplate can enhance the debonding detection capability. The C-scan imaging results indicate that the utilization of high-order echoes enhances imaging quality, and the employment of high-quality miniaturized EMATs enhances the capability for quantitative analysis of debonding defects.

Experimental analysis of low-velocity impact behaviour on flax-balsa biobased sandwich

This article deals with a detailed experimental study dedicated to the evaluation of the overall mechanical behaviour of a bio-based composite structure used in transportation industries. The sandwich structure is designed to increase the lightening, vibration damping, and composite recyclability. The considered materials consist of a Flax/Elium® laminate composite for skins associated with a balsa core. The sandwich structure was obtained using a one-shot liquid resin infusion process. Low-velocity impact tests were carried out on different sandwich configurations with the aim of characterizing the effects of the stacking sequence and the density and thickness of the core. Furthermore, an experimental comparative analysis was conducted involving two composite laminate types: Glass/Elium and Flax/Elium to enhance the specific behaviour of flax fibre composite to be used as skins in the sandwich structures. The impact tests were carried out at low velocities and at different levels of impact energy using a drop-weight test bench. Notable damage mechanisms have been identified, and a chronological sequence of their development has been suggested. Ultrasonic analyses using C-Scan imaging were applied to the opposite side of the impacted specimen. The research proves the efficient energy-absorbing capability of the biobased sandwich structure during impact. Finally, this study enables a deeper understanding of various parameters that influence the behaviour of sandwiches during low-velocity impacts, thereby facilitating more informed material selection for practical applications.

Laser ultrasonic damage identification of composites based on empirical mode decomposition and neural network

In order to ensure the safety and reliability of equipment during service, the implementation of non-destructive testing (NDT) plays a crucial role. Laser ultrasonic technology (LUT) offers several advantages over traditional ultrasonic technology, including non-contact operation, high frequency, wide frequency band. The utilization of LUT to evaluate the condition of composite materials has emerged as a significant approach to ensure safety. Nevertheless, the unsatisfactory surface optical quality of composite materials often results in indistinct imaging outcomes. Moreover, human factors frequently introduce unavoidable biases in the interpretation of the imaging results. As a solution, a neural network-based method for damage detection using LUT has been proposed. Initially, the empirical mode decomposition (EMD) is employed to decompose the signal and select the relevant intrinsic mode functions (IMFs) for signal reconstruction, which yields time-domain features. Subsequently, these features are inputted into the neural network to recognize the state of the material, and the recognition outcomes are incorporated into the pixel matrix. Lastly, C-scan images are created by stacking the pixel matrix and applying a threshold to reduce noise. The results demonstrate that the accuracy of delamination detection reaches 98.5%. Compared to conventional methods, the C-scan images obtained through neural network are more precise and easier to interpret, thereby effectively minimizing misinterpretations caused by manual intervention.

Nondestructive quantification of internal raster path for additively manufactured components via ultrasonic testing.

This work investigates the viability of discerning the raster pattern of additively manufactured components using high frequency ultrasonic nondestructive testing. Test coupons were fabricated from poly cyclohexylenedimethylene terephthalate glycol using the fused filament fabrication process, in which layers were deposited at various predetermined raster angles. Each printed part was scanned using spherically focused, high-resolution, ultrasonic transducers of various peak frequencies between 7.5 and 15MHz. From the captured waveform data, images are extracted to observe the raster pattern in a layer-by-layer manner, with the results from the 10MHz element yielding the best performance. An in-house MATLAB script was developed to analyze the transducer signal to investigate C-scan images at various depths throughout the component. From the resulting C-scan images, one can consistently identify the proper raster orientation within 2°-4° in each of the first 10 deposited layers, with the accuracy decreasing as a function of depth into the component. Due to signal attenuation, there is insufficient data at depths beyond the 11th and 12th layer, to properly analyze the present data sets accurately. Validation was performed using X-ray computed tomography scans to demonstrate the accuracy of the ultrasonic inspection method.

Numerical simulation of the effect of projectile shape and size on the high-velocity impact of carbon fiber reinforced composite laminates

To investigate the impact response and damage mechanism of CFRP composite materials for high-speed trains under high-speed impact from foreign objects of different shapes and sizes, high-speed impact experiments and simulations were conducted on CFRP composite laminate at a velocity of 163 m/s. A finite element simulation model was established in Abaqus/Explicit. This model adopts the 3D-Hashin failure criterion considering strain rate effects to simulate the failure of fibers and matrix. Bonded elements with zero thickness were inserted along the fiber direction in the laminate to replicate the damage phenomena of fiber bundle debonding and pull-out. The effectiveness of the finite element model was validated by high-speed photography of the impact process and ultrasonic C-scan damage images recorded during the experiments. Based on this simulation model, the influence of projectiles of different shapes and sizes on the impact response of CFRP composite materials was studied. Projectiles of different shapes exhibit their own characteristics in terms of the time of peak force occurrence, manifestation, and characteristics of laminar damage. The peak force of the cylindrical projectile is 245.01% greater than that of the spherical one, while the conical projectile is 52.26% smaller than the spherical one. When the projectile size increases from 10 mm to 20 mm, the peak force of the spherical projectile, conical projectile, and cylindrical projectile increases by 79.67%, 120.12%, and 283.13% respectively. The total area of delamination in laminated plates is positively correlated with the size of the impacting body.

Nondestructive detection of laser-cladding coating interface defects: a deep learning-enhanced laser ultrasonics approach

ABSTRACT Laser-cladding technology is now widely used in repairing mechanical parts and creating functional coatings. However, interface defects are commonly found in laser-cladding coatings, significantly impacting the fatigue and mechanical properties of these coatings. Therefore, it is necessary to detect interface defects in laser-cladding coatings. In this study, we utilised laser ultrasonics (LU) method to test interface defects non-destructively. However, the signal-to-noise ratio (SNR) of LU signals was poor. To address this issue, an unsupervised deep learning method was used to enhance the SNR. The results showed that the noises were suppressed by the deep learning method. Based on the deep learning-enhanced LU method, surface acoustic waves were used to present B-scan and C-scan images. The artificial defects with a 0.5 mm diameter were detected. Consequently, the deep learning-enhanced LU method proves to be a viable approach for the contactless, online, and non-destructive testing of interface defects in laser-cladding coatings.

Ultrasonic resonance evaluation method for deep interfacial debonding defects of multilayer adhesive bonded materials

Abstract Multilayer adhesive bonded structures/materials (MABS) are widely used as structural components, especially in the field of aerospace. However, for MABS workpieces, the facts that the weak echo of the deep interfacial debonding defects (DB) caused by the large acoustic attenuation coefficient of each layer and this echo, which generally aliases with the excitation wave and the backwall echo of the surface layer, pose a great challenge for the conventional longitudinal wave ultrasonic nondestructive testing methods. In this work, an ultrasonic resonance evaluation method for deep interfacial DBs of MABS is proposed based on the ultrasonic resonance theory and the aliasing effect of ultrasonic waves in MABS. Theoretical and simulation analysis show that the optimal inspection frequency for II-interfacial DBs is 500 kHz when the shell thickness is 1.5 mm and the ethylene propylene diene monomer (EPDM) thickness is 1.5 mm, and the optimal inspection frequency is 250 kHz when the shell thickness is 1.5 or 2.0 mm and the EPDM thickness is 2.0 mm. Verification experiments show that the presence of a DB in the II-interface causes a resonance effect, and in the same inspection configuration, the larger the defect size, the more pronounced this effect is. This resonance effect manifests itself as an increase in the amplitude and an increase in the vibration time of the A-scan signal as well as a pronounced change in the frequency of the received ultrasonic wave. In addition, the increase in the excitation voltage further highlights the ultrasonic resonance effect. Four imaging methods – the integrations of the signal and the signal envelope curve, the maximum amplitude of the fast Fourier transform (FFT) of the signal, and the signal energy – were used for C-scan imaging of ultrasonic resonance evaluation of MABS’s deep interfacial DBs and all these methods can clearly show the sizes and locations of the artificial defects and internal natural defect. The normalized C-scan imaging method proposed in this study can further highlight the weak changes in the signals in the C-scan image. The research results of this study have laid a solid theoretical and practical foundation for the ultrasonic resonance evaluation of MABS.

Detection and Imaging of Corrosion Defects in Steel Structures Based on Ultrasonic Digital Image Processing

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively.

Corrosion of API 5L X60 Pipeline Steel in Soil and Surface Defects Detection by Ultrasonic Analysis

The corrosion steels phenomenon is one of the main problems in the oil industry, such as in buried transmission pipelines used for high gas pressure for long distances. Steels are protected from the external soil corrosion through a bituminous coating, whose action is coupled with a cathodic protection system, which aims to maintain steel in its protection field and thus to avoid any corrosion risk. However, steels in service may experience external surface defects like corrosion pitting and cracking due to electrochemical or mechanical interactions of bare steel with an aggressive soil solution after steel protection failure. These are concerning phenomena and are the major threats of the pipeline transmission system’s reliability and ecological safety. Corrosion mechanisms are varied and can be evaluated by different methods, such as electrochemical measurements, which are influenced by various factors like temperature, pH, soil characteristics, resistivity, water content, and as well mechanical stresses. Corrosion results from simulated artificial soil solutions showed that steel is sensitive to corrosion by soil. Surface defects detection was carried out using an ultrasonic non-destructive method such as C-Scan Emission testing and the time of flight diffraction technique (TOFD) ultrasonic non-contact testing method. After propagation of the ultrasonic waves, the diffracted ultrasonic reflected wave occurring at the edges of the defects appears due to the presence of a corrosion defect by generating defect echoes. The C-Scan ultrasonic image shows surface reflection, including corrosion defects on interfaces with varying acoustic impedances. The cross-transverse speed ultrasonic propagation through the plate including defect is modified, revealing more surface defects, and cross-transverse speed is shown to increase ultrasonic detection presents some advantages, such as precision and speed of detection without alteration to the structure. This method can be used in the industrial context as an intelligent industrial robotics technique.

Numerical Investigation of the R-Curve Effect in Delamination of Composite Materials Using Cohesive Elements

This paper presents a numerical investigation of the R-curve effect in delamination propagation in composite materials. The R-curve effect refers to the phenomenon whereby resistance to crack propagation increases with the advancement of the delamination, due to toughening mechanisms, such as fiber bridging. Numerical models often neglect this effect assuming a constant value of the fracture toughness. A numerical approach based on cohesive elements and on the superposition of two bilinear traction-separation laws is adopted here to accurately predict the R-curve effect in skin-doubler composite specimens subjected to three-point bending tests. The carbon-epoxy material presents two different sensitivities to the fiber bridging phenomenon resulting in two different R-curves. Comparisons with literature experimental data, in terms of load and delaminated area vs. applied displacement, and ultrasonic C-scan images show the effectiveness of the adopted approach in simulating the R-curve effect. The predicted numerical stiffness aligns with the experimental scatter, although the maximum load is slightly underestimated by approximately 15% compared with the average experimental results. The numerical model accurately predict the R-curve effect observed in the experimental data, demonstrating a 31% increase in the maximum load for the material configuration exhibiting greater sensitivity to fiber bridging.

Nonlinear Ultrasonic C-Scan Imaging for Contact-Type Defects in Diffusion-Bonded Joints-A Case Study.

Diffusion bonding technology is widely used in the connection of precision components, yet accurately and reliably detecting contact-type defects on the bond interface still remains a significant problem. Nonlinear ultrasonic methods have been proven to be sensitive to contact-type defects; however, the use of continuous wave or tone burst wave excitation limits its wider application. In this paper, dual-probe nonlinear ultrasonic testing with pulse wave excitation is proposed to detect contact-type defects in diffusion-bonded joints. A titanium alloy diffusion-bonded specimen with artificial defects was fabricated, and the corresponding detection device was designed based on the existing ultrasonic C-scan testing system. A C-scan imaging program based on nonlinear parameters was developed by extracting the fundamental and second harmonic waves of the reflection echo on the bond interface. The results demonstrated that the proposed detection scheme can obtain the nonlinear parameters of diffusion-bonded interfaces, and the nonlinear ultrasonic C-scan image of the bond interface is also obtained. The nonlinear parameter in the contact-type defects areas calculated from the bond interface echo is about 10 times (20 dB) higher than that in macro defects areas, whose gap is about 10 μm. The results indicate that the nonlinear ultrasonic methods seem to be more sensitive to contact-type defects and have a great potential to complement the insufficient detection capability of linear ultrasound for diffusion-bonded joints.

Damage identification of wind turbine blades based on deep learning and ultrasonic testing

ABSTRACT Ultrasonic testing (UT) is an effective method for detecting internal damage in wind turbine blades. However, manual interpretation is time-consuming and subjective. To automate UT in the wind turbine industry, an image detection model based on deep learning called UCD-YOLO (Ultrasonic C-scan image Detection You Only Look Once) is proposed to detect internal damage such as stratified and lack of glue in wind turbine blades. In order to enhance the feature extraction ability under complex damage forms, the use of deformable convolutional networks and context augmentation module are proposed. Designing a lightweight cross stage partial spatial pyramid pool fast (LCSP_SPPF) module, which allows the model to learn more information and improve detection accuracy. The introduction of wise intersection over union (WIoU) improves the convergence speed while reducing the regression error. Using UT devices to collect damage images of wind turbine blades and conducting comprehensive evaluation experiments on the detection performance of the proposed method. Results show that compared to the YOLOv5, UCD-YOLO is not only lighter but also achieves a 7.5% increase in mean average precision (mAP50). And compared with other detection methods, further verifying that the proposed method can quickly and effectively detect wind turbine blade internal damage, improving power generation efficiency.

Residual life prediction of multi-layer composite coatings based on vector quantised and attention-variational AutoEncoder network

ABSTRACT The quality of multi-layer composite coatings (MLCC) directly affects the reliability of equipment. Ultrasonic C-scan has advantages of non-destructive and high-precision. The accurate determination of residual life (RL) from C-scan images of coatings is hindered by the difficulty of feature extraction. To address this issue, a RL prediction method for MLCC based on the Vector Quantised and Attention-Variational AutoEncoder (VQA-VAE) network is proposed. First, by incorporating attention mechanism and residual module, the VQA-VAE network can perform unsupervised feature extraction on C-scan images effectively. Then, the features are fused by two-dimensional entropy weighting method to get the health index (HI), which can be used to establish the RL prediction model. To verify the effectiveness of this method, C-scan is performed on MLCC by ultrasonic microscope to obtain C-scan images, and RL of MLCC is predicted. The experiment results demonstrate that this method achieves higher accuracy in predicting RL of MLCC. In a word, this is a non-destructive, high-efficiency, high-precision, and large-scale measurement technique for MLCC.

Acoustic field simulation-based ultrasonic water immersion non-destructive testing for titanium alloy plate

ABSTRACT Titanium alloy plate is widely used in automotive, aerospace, medical and other fields. Ultrasonic testing is a highly efficient non-destructive testing method for the inspection of titanium alloy components. However, there are strong distortion and attenuation of sound wave propagation in titanium alloys due to the presence of anisotropy and inhomogeneities, making it difficult to detect tiny defects in such components. In addition, some configuration parameters, such as the water path depth also affect the distribution of the acoustic field in the detected components. It is necessary to predict the acoustic field distribution to achieve a better testing accuracy. An acoustic field simulation method based on a multi-Gaussian beam is proposed, which can model the focused acoustic field in a multilayer anisotropy medium. The relationship between the acoustic focused area and the water path depth was explored and optimised comprehensively. C-scan imaging of specimen with flat bottom holes was performed at different water path depths. The results show that the proposed method can optimise the configuration parameters to improve the accuracy of the flaw sizing. This study provides an effective method for testing titanium alloy plate.

Eddy current testing of CFRP/Aluminium honeycomb sandwich structure

ABSTRACT Aluminium honeycomb sandwich structure with CFRP panels has broad applications in aerospace and other industries. This paper presents an experimental study on the eddy current (EC) testing of such structure. An EC probe consisting of two pancake coils, one up and one down, is used for the testing and the amplitude of the differential voltage of the coils at each position of probe scanning is measured. The result of linear scan shows that the local minimal EC responses correspond to the edges of the core walls. In the C-scan results, different types of defects in the honeycomb cores including wall fracture, node disconnection, and core wrinkle can be identified. Threshold processing is performed to improve the indication of the defects. Topological graph of honeycomb core is obtained by connecting the pixels of C-scan image at which the EC responses are the local minima. Detection of impact damages is carried out and the effects of impact energy on the C-scan image are analysed. When the impact energy is increased, the part of the image in the area of damage becomes more blurred, and the minimal signal decreases monotonically. The area of damage seen in C-scan image is not necessarily increased with the increase of impact energy.

Modelling of Fatigue Delamination Growth and Prediction of Residual Tensile Strength of Thermoplastic Coupons.

Thermoplastic composites are continuously replacing thermosetting composites in lightweight structures. However, the accomplished work on the fatigue behavior of thermoplastics is quite limited. In the present work, we propose a numerical modeling approach for simulating fatigue delamination growth and predicting the residual tensile strength of quasi-isotropic TC 1225 LM PAEK thermoplastic coupons. The approach was supported and validated by tension and fatigue (non-interrupted and interrupted) tests. Fatigue delamination growth was simulated using a mixed-mode fatigue crack growth model, which was based on the cohesive zone modeling method. Quasi-static tension analyses on pristine and fatigued coupons were performed using a progressive damage model. These analyses were implemented using a set of Hashin-type strain-based failure criteria and a damage mechanics-based material property degradation module. Utilizing the fatigue model, we accurately foretold the expansion of delamination concerning the cycle count across all interfaces. The results agree well with C-scan images taken on fatigued coupons during interruptions of fatigue tests. An unequal and unsymmetric delamination growth was predicted due to the quasi-isotropic layup. Moreover, the combined models capture the decrease in the residual tensile strength of the coupons. During the quasi-static tension analysis of the fatigued coupons, we observed that the primary driving failure mechanisms were the rapid spread of existing delamination and the consequential severe matrix cracking.

Comparative Study on Ultrasonic C-Scan Imaging of Composite Lap Joints Using Piezoelectric Transducer: Pulse-Echo and Pitch-Catch Configurations

Comparative Study on Ultrasonic C-Scan Imaging of Composite Lap Joints Using Piezoelectric Transducer: Pulse-Echo and Pitch-Catch Configurations

3D Reconstruction of embedded object using ground penetrating radar

Ground Penetrating Radar (GPR) is a non-destructive device widely used to locate and map underground utilities such as pipes, cables, etc. Its principle is based on the reflection signal of a transmitter-receiver antenna that strikes underground objects by means of the propagation of a short pulse of electromagnetic waves into the ground. The GPR will produce a hyperbolic curve as a result of the object’s presence. Accurate interpretation of hyperbola curves is greatly important and highly depends on user expertise; thus, it is considered a challenge. To address this issue, this study aims to develop 3D reconstructions of embedded objects. In this study, C-scan images were acquired, and 3D interpolation and the Synthetic Aperture Focusing Technique (SAFT) were introduced. In this framework, the acquired data is subjected to pre-processing techniques via time-zero correction, background removal using average background subtraction, and Kirchoff’s migration method. The software Reflex 3D Scan has been used to analyse and preprocess the 3D reconstruction of embedded objects. The obtained results show that 3D interpolation and SAFT methods are not only able to reconstruct 3D models but are also able to reveal information on the dimension and location of the buried object represented by voxel points in the 3D space cube.