Ultrasonic C-scan Images Research Articles

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.

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.

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.

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.

On the impact damage characteristics of spread-tow woven composites: From high velocity to hyper velocity

Spread-tow woven composites are known to be economically efficient with great potential for use in aerospace applications where components will inevitably suffer various impact threats during their service. In this work, the impact damage behavior of woven composite panels under a wide range of velocities (from about 0.2 km/s to 1.5 km/s) are experimentally and numerically investigated. The damage distribution, ballistic failure mode, and delamination areas are examined in detail. A systematic series of verification studies at a variety of projectile velocity scales—ranging from damage distributions to simulations of the evolution of delamination—were performed to fully evaluate the capabilities of the impact model. The model predictions show good agreement with the experimental results: the delamination zone as determined by ultrasonic C-scan images and the damage behavior of the spread tow woven composite under a wide range of impact velocities were accurately predicted. At high and hyper velocities, the asymmetrical patterns of damage in the panels are similar. The predictions of damage evolution indicate that the larger delamination area results from the large deformation of the rear side of the panel, reducing the bending stiffness of the woven composite and allowing extensive local delamination that is not visible. These results can provide a useful reference for obtaining the dynamic response of a carbon fiber–reinforced polymer woven composite and can aid in the design of composite panels with appropriate impact resistance properties.

On damage behavior and stability of composite T-shaped stiffened panels under compression after impact considering impact locations

Impact locations play a critical role in determining the residual performance of the composite stiffened panel, since the local damage behavior at different locations is not identical. The objective of this paper is to systematically investigate the influence of impact locations on damage behavior, stability and residual compression strength of T-shaped stiffened CFRP panels. Low-velocity impact tests are carried out by impacting at the skin center or the flange tip. The damage patterns and post-buckling behavior are monitored by ultrasonic C-scan and digital image correlation. An effective computational framework with explicit formulations considering complicated damage mechanisms is proposed to investigate the details of damage behavior. The skin center impact causes severe fiber breakage and matrix squeezing, while the flange tip impact induces complex damage patterns including ply splitting, fiber breakage, matrix cracks, as well as extensive skin–rib interfacial delamination. Compression after impact results indicate that impact damage at the skin center has little effect on the residual performance of the structure, and the compression failure load is only reduced by 2.02%, from 276.8 kN to 271.2 kN. However, impact damage at the flange tip weakens the supporting effect of the stiffener, leading to early buckling of the local skin. The propagation of the interfacial delamination at the damaged flange tip directly triggers the premature compression failure, resulting in an 18.79% decrease in the failure load, 224.8 kN. Numerical predictions correlate well with experimental data. The reported results may be useful in structural design and maintenance.

A study on non-destructive testing of geometrical parameters of self-piercing riveting joints using an acoustic microscope with a scanning focusing converter

ABSTRACT A non-destructive technique was proposed for geometrical parameters’ measurement of self-piercing riveting (SPR) joints based on ultrasonic C-scan image. Firstly, in order to enhance the detection precision, the characteristics of the ultrasonic echo signal at the edge of internal structure were analysed, and a theoretical formula on its sound intensity distribution was established. Then, an image segmentation process for the detection of SPR expansion diameter was suggested. Results showed that taking the greyscale value at the ‘inflection point’ from the C-scan image as the threshold of the segmentation, the non-destructive test results can get good correlation with the metallographic cross section, which provides a new method on the quality evaluation of SPR by using non-destructive test.

Ultrasonic C-scan image restoration method using the Richardson-Lucy algorithm and a flaw measurement model

In ultrasonic imaging, blurred edges have a heavily negative impact on the accuracy of the flaw images. To improve ultrasonic C-scan image quality, this paper proposes a deconvolution-based ultrasonic C-scan image restoration method combining the Richardson-Lucy (RL) algorithm and a flaw measurement model. In order to implement an optimal restoration process, an ultrasonic C-scan imaging model is presented and employed to simulate the C-scan images. By restoring the simulated images, a relationship between the optimal iteration number and the ratio of the flaw size to the sound wavelength is established. Furthermore, the equivalent sizes of the flaws are extracted from a flaw measurement model-based non-destructive approach and are used to calculate the optimal iteration number of the RL deconvolution algorithm. Finally, the experimental ultrasonic C-scan images are restored by using the RL iterative algorithm with the optimal iteration numbers, 1, 2, 6, and 14, respectively. The experimental results show that the flaw sizes, obtained by the 6 dB drop method in the original C-scan images, have errors larger than 26 %. After restoration, the maximum error of the flaw size in the C-scan image is only 10 % and the minimum is 2 %, indicating that the proposed restoration method effectively improves the accuracy of the ultrasonic C-scan imaging.

A skin-core debonding quantitative algorithm based on hexagonal units reconstruction for air-coupled ultrasonic C-scan images of honeycomb sandwich structure

Honeycomb Sandwich Structure (HSS) is widely used in the aerospace industry. The skin-core debonding in HSS can grow to reach a critical size, leading to the sudden failure of structures. Due to the blurry edges on the C-scan image caused by honeycomb units with periodic honeycomb walls, accurate quantification of debonding is still a challenge. In this paper, accurate debonding quantification of HSS based on air-coupled ultrasonic C-scan and hexagonal units reconstruction is proposed. The debonding boundary identification is accurate to the interior of the honeycomb units by partial debonding units division based on calculating the total amplitude of all scanning points within the unit. The detailed process was presented with key steps as ultrasonic air-coupled C-scan for HSS, hexagonal units reconstruction based on peak points on C-scan images, units classification by K-means clustering algorithm, and units division according to the total amplitude. Four types of debonding, including film sticking, adhesive removal, slight subsidence, and severe subsidence, are used to evaluate the effectiveness of the method. The results show that the proposed method can describe and quantify-four kinds of debonding defects reliably and it is ideally suited for air-coupled ultrasonic C-scan image processing of periodic structures.

Experimental investigation of high velocity impact response of CFRP laminates subjected to flyer plate impact

Carbon fiber reinforced plastic (CFRP) composites are widely used in aircraft and automotive industries due to their extremely high specific strength and stiffness. High velocity impact is inevitable throughout the service life of composite structure and it is important to understand its impact response. In this study, high velocity impact response of CFRP laminates subjected to flyer plate impact were investigated in the velocity range of 32∼100 m/s by a gas gun coupled with 3D Digital Image Correlation (DIC) analysis. For comparison purposes, similar tests were carried out on 2A12 aluminum alloy plates of equivalent thickness. The PVDF pressure sensor was used to measure the impact pressure of the target plate, and the influence of impact velocity and target type on the impact pressure was analyzed. The damage and failure mechanism of CFRP laminates during impact were analyzed by digital microscope, ultrasonic C-scan and sectional image. The results show that the variation trend of the theoretical impact pressure is consistent with that of the experimental impact pressure, and the experimental impact pressure is smaller than the theoretical impact pressure. The damage and failure mechanism of two target plates are different under high velocity impact. For 2A12 plate, a truncated circular cone-shape bulge is generated on the 2A12 plate and this damage patterns appears regardless of the impact velocity. For CFRP laminates, impact indentation with elongated splits extending from the indentation edges is observed on the impacted surface of the target plate. The delamination zone of the adjacent layers is distributed in a fan-shaped area and the delamination direction is consistent with the direction of the fibers in the front ply. The data from the experiments can be used to evaluate and refine composite material models and computational methodologies that are used to predict the impact response of composite.

Simulation and experiment of ultrasonic phased array for additive manufacturing titanium alloy with columnar crystal structure

The influence of the grain structure of titanium alloys, used for additive manufacturing (AM), on the propagation of ultrasonic beams is of great significance for the non-destructive testing of components manufactured from this material. In order to explore the issues related to ultrasonic testing, such as low signal-to-noise ratio (SNR) and large imaging error of internal defects, an ultrasonic phased array C-scan image was obtained and the measurements of ultrasonic noise level and defect distortion caused by columnar crystals were analysed. Ultrasonic wave theory was combined with the Voronoi algorithm to establish a finite element (FE) model of the AM titanium alloy with a columnar crystal structure. The focusing and scattering of ultrasonic beams in different grain structures was studied and the defect echo signals from the FE model and the experimental results were extracted and compared. Comparisons of the signal-to-noise ratio and the background noise of three typical sections were in good agreement, which shows that the proposed simulation model can be used to analyse the propagation and scattering of beams for the inspection of AM titanium alloys. Finally, suggestions to improve the ultrasonic detection results of AM titanium alloy components and the applicability of the wave simulation model for other polycrystalline materials are discussed.

Evaluation of fatigue performance of resistance convex welds based on ultrasonic C-scan image analysis

Ultrasonic C-scan detection is adopted in this study to compare the connection status of resistance convex weld (RCW) before, during and after fatigue test. Based on the variation of ultrasonic echoes in different connection zones, the connection state can be observed on ultrasonic C-scan images. With the occurrence of fatigue destructive behavior, ultrasonic C-scan image changes, and so does the pixel distribution. The histogram of pixel grayscale distribution can quantitatively evaluate the fatigue limit of stainless steel RCW joint, which provides an effective method to judge the fatigue life in practical engineering application.

Intelligent damage recognition of composite materials based on deep learning and ultrasonic testing

Ultrasonic non-destructive testing can effectively detect damage in aircraft composite materials, but traditional manual testing is time-consuming and labor-intensive. To realize the intelligent recognition of aircraft composite material damage, this paper proposes a 1D-YOLO network, in which intelligent fusion recognizes both the ultrasonic C-scan image and ultrasonic A-scan signal of composite material damage. Through training and testing the composite material damage data on aircraft skin, the accuracy of the model is 94.5%, the mean average precision is 80.0%, and the kappa value is 97.5%. The use of dilated convolution and a recursive feature pyramid effectively improves the feature extraction ability of the model. The effectively used Cascade R-CNN (Cascade Region-Convolutional Neural Network) improves the recognition effect of the model, and the effectively used one-dimensional convolutional neural network excludes non-damaged objects. Comparing our network with YOLOv3, YOLOv4, cascade R-CNN, and other networks, the results show that our network can identify the damage of composite materials more accurately.

Infrared and ultrasonic intelligent damage recognition of composite materials based on deep learning.

With the large-scale application of composite materials in military aircraft, various composite material detection technologies with infrared nondestructive and ultrasonic nondestructive testing as the core have played an important role in detecting composite material component damage in military aircraft. At present, the damage of composite materials is mostly recognized manually, which is time-consuming, laborious, and inefficient. It can effectively improve detection efficiency and accuracy by using intelligent detection methods to detect and recognize damage. Moreover, the effect of infrared detection is significantly reduced with increasing detection depth, while ultrasonic detection has shallow-blind areas. A cascade fusion R-CNN network is proposed in order to comprehensively identify composite material damage. This network realizes the intelligent fusion recognition of infrared and ultrasonic damage images of composite materials. The network is based on a cascade R-CNN network, using fusion modules and BiFPN for improvement. For the infrared image and ultrasonic C-scan image data set established in this paper, the algorithm can identify the type and location of damage detected by infrared and ultrasonic testing. Its recognition accuracy is 99.3% and mean average precision (mAP) is 90.4%. In the detection process, the characteristics of infrared and ultrasonic images are used to realize the recognition of damage depth. Compared to SSD, YOLOv4, faster R-CNN and cascade R-CNN, the network proposed in this paper is better and more effective.

Influence of Incident Energy on Sisal/Epoxy Composite Subjected to Low Velocity Impact and Damage Characterization Using Ultrasonic C-Scan

Impact resistance is an inevitable characteristic of the composites employed in the high performance structural applications. Due to the growing interest in the use of sisal fibre as reinforcement in the polymer composites, it is required to determine the response of sisal/epoxy composites to low velocity impact at high incident energies where perforation can occur and assess the damage characteristics using a non-destructive technique. In this work, sisal/epoxy composites were subjected to drop weight impact in the velocity range of 3 m/s to 5 m/s at different energy levels between 20 J to 50 J according to the ASTM D7136. Based on the results observed, it is concluded that both the peak load and absorbed energy increased with the increasing incident energy level up to 40 J. At 50 J, perforation occurred and the maximum deformation was approximately 22 mm for the sisal/ epoxy composite. Damage characteristics and failure behaviour of the composite at different incident energies was examined from the visual images of the front and back face of the composite. The quantitative assessment of crack propagation in the sisal/epoxy composite and the damage area were determined from the ultrasonic C-scan images of the sample post impact at various energy levels.

Investigating ballistic resistance of CFRP/polyurea composite plates subjected to ballistic impact

As composite structures develop, polyurea coating has been applied to improve the protective performance of multi-layered composite structures subjected to blast or impact loading. In order to investigate the ballistic resistance of polyurea coated Carbon Fiber Reinforced Plastics (CFRP) composites, impact experiments were conducted using a 14.5 mm ballistic gun with cylindrical projectile. The effect of polyurea coating position and coating thickness were analyzed and discussed. The impact velocity of projectile was selected from 600 m/s to 1406 m/s for polyurea coated CFRP composite plates. The energy absorption ratios were calculated to assess the ballistic behavior. Damage zone of CFRP plates were determined by ultrasonic C-scan images. The failure mechanism of CFRP laminates and polyurea were analyzed by an optical microscope and scanning electron microscopy (SEM). The results showed that the deformation of projectiles increased with the increase of initial impact velocity. Energy absorption ratio and damage level of RPU2 configuration was better than pure CFRP plates. The optical microscope and SEM images showed that failure modes were compression-shear failure on the front side of polyurea, tension-shear failure, petaling failure, spallation and punching perforation failure on the rear side of polyurea. Overall, polyurea coating can significantly boost the composite plates’ ballistic performance when spraying polyurea on the CFRP composites’ rear side.

Ultrasonic measurement model-based non-destructive detection method for curved components using an immersion spherically focused transducer

ABSTRACT The ultrasonic measurement model (UMM) has been theoretically studied over the past few decades, but there is little published work on its practical application for guiding defect detection. In this work, we present a model-based ultrasonic method to detect flaws in curved components using immersion of spherically focused transducers. The UMM of focused transducers is further investigated theoretically and verified experimentally, and some key issues in the model with focused transducers are addressed, including the necessity of correctly using effective geometrical parameters and the corresponding transfer function. The UMM is employed to guide the inspection of a magnesium alloy shell component which consists of several curved surfaces. The alignment between the transducer and the shell is conducted by comparing the measured and theoretically predicted interface-echo signals, the water path is selected by simulating the effective detection region, and the system gain is set by quantitatively analysing the amplitudes of flaw signals and noises. When the inspection conditions are determined, a variable threshold based on the predicted flaw signals is introduced to distinguish noise and flaw signals. The ultrasonic C-scan images of the shell are compared with the digital radiography (DR) images, and the comparison shows that the flaws of interest can be detected in the C-scan images, which verifies the effectiveness of the UMM-based detection method.