Ultrasonic Nondestructive Testing Research Articles

Numerical Simulation Analysis of Laser Ultrasonic Detection of Defects in Silicon Carbide

Silicon carbide (SiC) is widely used in power electronic devices and other fields, the defects of which can significantly impact its performance in device fabrication. Laser ultrasonic non-destructive testing (NDT) as a novel and effective approach can detect these defects in real time. This study introduces a numerical model for the SiC NDT that elucidates the dynamic interactions between laser-induced ultrasonic waves and surface defects, and internal defects in SiC, respectively. Results show NDT is an effective way to locate the SiC defect and the ultrasonic waves’ vibration amplitude of detection points at defect edges increases by at least 16% compared to adjacent points, with a maximum of 43%. A comparative assessment between surface and internal defect vibration responses for acoustic is also made. For internal defects, the oscillation time of the acoustic wave at the detection point on the surface away from the edge of the defect at the excitation point exceeds that of surface defects by 100 ns, and the amplitude near the excitation point is more pronounced, reaching 1.44 nm, which is 4.2 times that of corresponding surface defects. Additionally, a linear relationship is observed between the arrival time of transmitted Rayleigh Waves (RSR) and internal defect length, with a correlation coefficient of 0.9878. Similarly, a linear relationship is established between the amplitude of reflected Rayleigh Waves (rR) and defect width, with a correlation coefficient of 0.9976, providing an effective way to quantify the inner defect. Furthermore, transient temperature profiles at distinct positions and transient acoustic fields and the relationship of acoustic vibration amplitude increasing with laser spot size under a fixed laser power density are also analyzed. This model provides a theoretical foundation for laser ultrasonic NDT setup and choice of micro-vibration detection device.

Improved Defect Sizing in Adhesive Joints Through Feature-Based Data Fusion

The current study focuses on the examination of adhesive-bonded materials, comprising different type of flaws like brass inclusions and delamination, through the application of ultrasound and X-ray non-destructive testing (NDT) techniques. The findings from both ultrasound and X-ray inspection were used to extract unique features, contributing to a more comprehensive understanding of the distinct characteristics demonstrated by each method. Several distinct features like absolute time of flight difference, peak-to-peak amplitude, variation coefficient in time and frequency domain, mean value of amplitude in frequency domain, and absolute energy were extracted from ultrasound testing results. Similarly, features like maximum amplitude, features from accelerated segment test, dilation, watershed segmentation, wiener deconvolution, and morphological gradient extracted from X-ray data underwent fusion. Different fusion techniques were applied to combine these features into a unified data set. A quantitative evaluation was performed for the individual features and their corresponding fused features from the ultrasound and X-ray results. A systematic analysis was conducted to quantify the improvement in defect sizing within the individual features and fused features from both the X-ray and ultrasonic investigations. The minimum absolute error of 0.02 mm was achieved with average fusion of absolute energy at 2nd interface and X-ray dilate features. This research not only delves into the diverse capabilities of ultrasonic and X-ray NDT methods in identifying flaws but also emphasizes the synergistic advantages arising from the integration of their distinct features. The qualitative study of defect estimation using the proposed fusion methods demonstrate that the distinctive fusion approaches significantly highlight the complimentary benefits of ultrasound and X-ray non-destructive testing methods, resulting in a quantifiable improvement in probability of defect detection.

Novel method to evaluate 3-D printed concrete quality using ultrasonic scatter energy techniques

ABSTRACT This study investigates the feasibility and effectiveness of a non-destructive ultrasonic testing method to evaluate the quality of 3-D printed concrete (3DPC) under varying printing conditions. To simulate different types of layer completions of 3DPC under real-world conditions, three printing schemes with different open times were adopted: no time gap, a 2-minute time gap, and a 5-minute time gap between printing subsequent layers. An air-coupled ultrasonic scanning system was used to measure multiple ultrasonic signals collected from three different 3DPC specimens. The signals were analysed in the frequency-wavenumber (f-k) domain to decompose forward propagating and scattered wave fields. The experimental results demonstrate that ultrasonic scatter energy increases with longer time gaps between subsequent layers in 3DPC. Additionally, the scatter energy was found to correlate well with defect density of 3DPC, assessed by digital image processing using image binarization methods. The findings of this study suggest that the ultrasonic scatter energy approach shows potential more thoroughly assess the quality of 3DPC structures because it interrogates the full volume of the materials and it provides superior damage sensitivity than ultrasonic velocity measurements do.

Ultrasonic Phased Array Testing and Identification of Multiple-Type Internal Defects in Carbon Fiber Reinforced Plastics Based on Convolutional Neural Network.

Carbon fiber reinforced plastics inevitably develop defects such as delamination, inclusions, and impacts during manufacturing and usage, which can adversely affect their performance. Ultrasonic phased array inspection is the most effective method for conducting nondestructive testing to ensure their quality. However, the diversity of defects within carbon fiber reinforced plastics makes it challenging for the current ultrasonic phased array inspection techniques to accurately identify these defects. Therefore, this paper presents a method for the ultrasonic phased array nondestructive testing and classification of various internal defects in carbon fiber reinforced plastics based on convolutional neural networks. We prepared an ultrasonic C-scan dataset containing multiple types of internal defects, analyzed the defect features in the ultrasonic C-scan images, and established an autoencoded classifier network to recognize manufacturing defects and impact defects of varying sizes. The experiments showed that the proposed method demonstrates superior defect feature extraction capabilities and can more accurately identify both impact and manufacturing defects.

Research on Dry Coupling Technology in the Ultrasonic Non-Destructive Testing of Concrete.

In the health monitoring and safety assessments of concrete structures, ultrasonic non-destructive testing (NDT) technology has become an indispensable tool due to its non-destructive nature, efficiency, and precision. However, when used in inspecting irregular concrete surfaces, traditional planar ultrasonic transducers often encounter energy loss and signal attenuation induced by poor interface coupling, which significantly reduces the accuracy and reliability of the test results. To address this problem, this article proposes a point-contact dry coupling ultrasonic transducer solution, which enables efficient acquisition of ultrasonic signals within concrete without the need for couplants. By combining an array imaging system with a total focusing algorithm, this study not only significantly enhances the convenience and signal-to-noise ratio (SNR) of concrete ultrasonic imaging, but also opens new pathways for ultrasonic NDT technology in concrete.

Characterization of thermal fatigue properties of lead-free Sn–Cu–Al–Mg solder alloy produced by powder metallurgy method

In the present study, lead-free Sn–Cu–Al–Mg-based solder alloys for electronics packaging were produced. Effects of thermal shock and thermal stress on the crack formation in the Sn-based solder alloys were investigated. Lead-free quaternary Sn–Cu–Al–Mg-based solder alloys were produced by using mechanical alloying and powder metallurgy route. Soldering involves using a molten filler metal to wet the surfaces of a joint. Wave soldering is a method for mass assembly of printed circuit boards involving through holes, surface. Lead-free solder alloys must show high wettability, suitable mechanical properties, electrical conductivity, high electrochemical corrosion resistance, and low cost. Mechanical alloying and powder metallurgy method were used in order to prevent formation of intermetallics and formation of voids. Mechanical alloying–powder metallurgy method could reduce the micro/macro-segregation and provide homogeneous microstructure. Initially, elemental metal powders were mechanically alloyed in a ball mill by using zirconia balls in order to produce alloy powders. Then, the alloy powders were compacted at 300 MPa and then the green specimens were sintered at 180 °C for 60 min. Nondestructive ultrasonic tests and eddy current tests were used for characterization of the solder alloy specimens. Elastic modulus of the Sn alloys was determined by ultrasonic measurements. Electrical conductivity of the specimens was determined by using eddy current tests. Microstructure was studied by scanning electron microscope. Effects of thermal shock and thermal stress on the crack formation in the alloys were investigated by heat treatment cycles in a chamber furnace. Heating cycles for thermal stress consist of heating to 150 °C and slow cooling. Heating cycles for the thermal shock consist of heating and quenching. In addition, electrochemical corrosion behaviour of the Sn solder alloys was investigated in NaCl solution.

Investigation of surface roughness induced attenuation of reflected waves using a quasi-Monte Carlo method

Abstract An understanding of the influence of surface roughness on wave scattering and accurate predictions of wave amplitudes are crucial for quantitative ultrasonic nondestructive testing and evaluation. In this work, the effects of surface roughness on the reflection coefficient are investigated using a quasi-Monte Carlo (QMC) method. The wave fields reflected from smooth and rough interfaces with an immersion transducer are modeled using the Rayleigh integral method, and the solutions are efficiently calculated using the QMC method for interfaces constructed using pseudo-random samples. The reflected wave fields are simulated and presented, and the properties of coherent and incoherent waves affected by interface roughness are discussed. The surface roughness induced attenuation of reflected waves is calculated using the ratio of received pressures for waves reflected from rough and smooth interfaces, and the predicted results are compared with those obtained using other recognized methods. It is shown that at low levels of roughness, excellent agreement is obtained between the results from the QMC method and the well-known Kirchhoff approximation, while for high levels of roughness, where the Kirchhoff theory gives pessimistic results, the predicted values agree well with those simulated using a finite element modeling approach, thus verifying the effectiveness of the proposed method.

Estimation of Stresses in a Plate with a Concentrator through Ultrasonic Measurements of Acoustic Anisotropy

Introduction. Acoustic anisotropy is measured during ultrasonic nondestructive testing. It estimates the magnitude of stresses by the acoustoelasticity method. The literature describes in detail the application of this approach in the case of a biaxial strength of extended structures: main pipelines, rail strings, steam generators, and others. They assume the presence of a uniform field with zero or weak gradients of stresses and deformations. However, the problem of timely detection and assessment of critical stresses caused by local concentrators through ultrasonic testing has not been solved. The presented material is intended to fill this gap. The work is aimed at determining the possibilities of the acoustoelasticity method to estimate the difference in the main biaxial stresses around the concentrator — a circular cutout in a rectangular plate.Materials and Methods. A 510×120×15 mm plate with a central hole of 40 mm in diameter was cut from a sheet of commercial-purity aluminum of the AMc brand (AW-3003 according to ISO) across the rolling direction, and subjected to uniaxial step loading in an Instron-8850 testing machine. For ultrasonic measurements, an acoustic sensor with a carrier frequency of 5 MHz was used. The stresses were calculated by solving the problem of stretching an isotropic linear-elastic plate in the ANSYS finite element modeling package and by the relations of the plane Kirsch problem obtained in the polar coordinate system.Results. The research allows us to state that the results of analytical and numerical calculations largely coincide only for points located near the zone of greatest stress concentration. In all other cases, the indicators differ several times in sign and modulus. The difference is explained by the fact that Kirsch's approach assumes the action of compressive stresses in the area of location of some points, but this factor is absent if we are talking about a real plate. It has been established that in the area of material with predominant tensile stresses, the acoustoelasticity method allows for a quantitative estimate of their difference with an error not exceeding the engineering one. Calculations based on the Kirsch relations correlate with the others only at points with the maximum concentration of tensile stresses.Discussion and Conclusion. The results of the study provide applying the acoustoelasticity method to estimate the magnitude of tensile biaxial stresses in the area around the fabrication holes. They are consistent with well-known scientific results and make it possible to rationally select the measurement points of acoustic anisotropy. The results of this scientific work can be applied in ultrasonic non-destructive testing using the acoustoelasticity method.

Review of Phased Array Ultrasonic Testing of Weld Joints

Welding is a prevalent joining technique used to metals in welding constructions. It is also very important to detect welding defects that may occur without damaging the constructions. The conventional ultrasonic inspection is the most widely used non-destructive test method for the detection of weld defects. Phased Array Ultrasonic Testing (PAUT) is a significant method within the category of ultrasonic non-destructive testing methods. Because ultrasonic phased arrays offer significant technical advantages over conventional ultrasonic such as improved sensitivity, accurate characterization and faster inspection. The aim of this article is to provide a comprehensive review analysing defects in welded joints by utilizing PAUT. Different literature studies related to the non-destructive testing of weld joints using by PAUT have been reviewed. Various examples of how the type, depth, and size of welding defects are detected in a highly effective manner are provided. As a result, it was concluded that phased array ultrasonic is a highly efficient technique compared to conventional ultrasonic methods for detecting welding defects.

Ultrasonic Nondestructive Testing of Li-ion Batteries for Anomaly Detection and Quality Control: Applications and Signal Processing

Use of ultrasonic nondestructive methods for quality control and testing of different types of Lithium-ion battery is progressing in recent years. This study provides a comprehensive review of the current applications and technical challenges of ultrasonic testing in inspection of lithium-ion batteries. First, basic principles and theoretical modeling of such batteries are shown. Secondly, it summarizes the most common defect types in these batteries. Finally, it talks about signal processing methods used for feature extraction and development.

Recent Advancements in Guided Ultrasonic Waves for Structural Health Monitoring of Composite Structures

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of laminated composite structures. Their favorable strength-to-weight ratio renders them ideal for the automotive, marine, and aerospace industries. Among various non-destructive testing (NDT) methods, ultrasonic techniques have emerged as robust tools for detecting and characterizing internal flaws in composites, including delaminations, matrix cracks, and fiber breakages. This review concentrates on recent developments in ultrasonic NDT techniques for the SHM of laminated composite structures, with a special focus on guided wave methods. We delve into the fundamental principles of ultrasonic testing in composites and review cutting-edge techniques such as phased array ultrasonics, laser ultrasonics, and nonlinear ultrasonic methods. The review also discusses emerging trends in data analysis, particularly the integration of machine learning and artificial intelligence for enhanced defect detection and characterization through guided waves. This review outlines the current and anticipated trends in ultrasonic NDT for SHM in composites, aiming to aid researchers and practitioners in developing more effective monitoring strategies for laminated composite structures.

A Composite Pulse Excitation Technique for Air-Coupled Ultrasonic Detection of Defects in Wood.

To overcome the problems of the low signal-to-noise ratio and poor performance of wood ultrasonic images caused by ring-down vibrations during the ultrasonic quality detection of wood, a composite pulse excitation technique using a wood air-coupled ultrasonic detection system is proposed. Through a mathematical analysis of the output of the ultrasonic transducer, the conditions necessary for implementing composite pulse excitation were analyzed and established, and its feasibility was verified through COMSOL simulations. Firstly, wood samples with knot and pit defects were used as experimental samples. We refined the parameters for the composite pulse excitation technique by conducting A-scan measurements on both defective and non-defective areas of the samples. Moreover, two stepper motors were employed to control the path for C-scan imaging to detect wood defects. The experiment results showed that the composite pulse excitation technique significantly enhanced the precision of nondestructive ultrasonic testing for wood defects compared to the traditional single-pulse excitation method. This technique successfully achieved precise detection and location of pit defects, with a detection accuracy rate of 90% for knot defects.

Non‐Destructive and Mechanical Characterization of the Bond Quality of Co‐Extruded Titanium‐Aluminum Profiles

The transportation industry aims to improve energy efficiency and reduce CO2 emissions, with a focus on reducing vehicle mass. A key method involves advanced lightweight construction techniques using materials like aluminum alloys. Research is concentrated on developing processes to combine different materials into reinforced hybrid components, such as aluminum and titanium. This study focuses on the lateral angular co‐extrusion (LACE) process to produce hybrid hollow profiles of EN AW‐6082 and Ti6Al4V, investigating the impact of the thermomechanical processing during extrusion and heat treatment (HT) on the resulting bond quality and material properties. Various HT routes are tested to see their impact on intermetallic phase formation, longitudinal weld seams, and bonding strength. Mechanical testing evaluates the tensile strength of the joining zone, while nondestructive ultrasonic testing (UT) assesses joining zone integrity and poor bonding detection. Results indicate that HT parameters significantly influence the bond quality and mechanical properties of hybrid profiles. UT data shows a strong correlation with tensile strength and intermetallic phase growth, providing a nondestructive way to evaluate bond quality. This study highlights the potential of LACE processes and optimized HT strategies to improve the performance and reliability of aluminum–titanium hybrid components.

Evaluation of the Influence of Surface Roughness Parameters on Ultrasonic Rayleigh Waveforms.

Ultrasonic nondestructive testing is widely used not only in the laboratory, but also in industry. The tests use various types of ultrasonic waves, diverse measurement techniques and different apparatus. One of the problems encountered is the high susceptibility of the surface wave to interference. Some of the interference is random in nature and can be minimized (e.g., contamination of the surface or resting a finger on the surface under study). Some of the interference is permanent in nature, such as variable surface roughness. The aim of the conducted research was to evaluate the influence of roughness on ultrasonic wave propagation. The study used samples with surface roughness Sa from 0.28 to 219 µm, and ultrasonic surface wave probes with frequencies from 1.41 to 8.02 MHz. It was observed that roughness significantly affects the attenuation of the ultrasonic wave, and the differences in signal amplification reached more than 15 dB. Similarly, the effect of the ultrasonic wave's transit time through surfaces of different roughness was noted. It was found that the difference in the ultrasonic wave transition time was more than 50 µs. The results of the study can be helpful for the ultrasonic testing of materials with different surface conditions.

Modeling of reflected ultrasonic fields in composed samples

Ultrasonic nondestructive testing involves the study of propagation, reflection and refraction patterns of elastic waves excited by contact or non-contact piezoelectric transducers in the inspected object. The finite element modeling usually requires high computational costs and additional postprocessing to select individual waves from the total solution. When probing joints of homogeneous materials, such as turbine blades made of heat-resistant monocrystalline alloys, the joint boundary is low-contrast, and the reflected signals are relatively weak. This causes additional difficulties for their separation from the total wave field and correct interpretation of the information they bring. To solve this problem, explicit asymptotic representations for reflected and transmitted waves in a two-layer elastic half-space with a surface source are proposed in the present work, which allow fast parametric analysis. They can be used to analyze ultrasonic probing data, for example, to estimate the state of the junction zone or to determine the mutual orientation of the crystals’ principal axes.

Defect classification of composite materials using transfer learning methods

ABSTRACT Nowadays, composite materials have become prevalent across various sectors, particularly finding usage in large-scale applications such as spaceships, automobiles, and aircrafts. The accurate detection of the defects in these materials is crucial, yet traditional methods often rely on human inspection, which is susceptible to errors. Recent advancements in machine learning have enabled defect detection using ultrasonic non-destructive testing methods. This paper introduces a new dataset named UNDT, which is obtained from the scans of 60 different composite materials, generating a total of 1150 images depicting both defective and non-defective areas. Several transfer learning methods are applied on the newly introduced UNDT dataset as well as the publicly available USimgAIST ultrasonic dataset. Comparative performance assessments illustrate the significance of utilising the transfer learning approach for defect classification on ultrasonic inspection images. Furthermore, the research emphasises the substantial benefits of employing these transfer learning methods. Notably, the DenseNet121 and VGG19 models achieve the highest accuracy rates, with 98.8% and 98.6% on the UNDT and USimgAIST datasets, respectively.

Ultrasonic Total Focusing Method Technology in Carbon Steel Butt Weld Application

Carbon steel butt welds are widely used in aircraft carrier, shipbuilding, railway, aerospace and other industries. As a new technology of ultrasonic non-destructive testing, Total Focusing Method (TFM) technology is a method of image reconstruction in which the value of each constituent datum of the image results from focused ultrasound. TFM may also be understood as a broad term encompassing a family of processing techniques for image reconstruction from full matrix capture (FMC). This paper compares resolution and focus between TFM and conventional phased array technology, by using phased array test block type B. The paper also introduces the testing system, including equipment, probe, wedge, calibration block as per ASME, PC analysis software, for detection of 21mm thickness carbon steel welds. In order to improve the defect detection ability, a customized software on board was designed based on TFM technology, and an appropriate propagation mode was adopted for different types of defects, such as longitudinal cracks on surface, lack of fusion, porosities, slag inclusions. The test results showing that compared with conventional phased array technology, the defect information such as length and depth detected by TFM are nearer to the actual situation, and the shape is closer to the physical one. TFM technology has more advantages in quantitative and qualitative capabilities in testing carbon steel butt welds, and can meet the requirements of on-site testing.

Machine learning for enhancing manufacturing quality control in ultrasonic nondestructive testing: A Wavelet Neural Network and Genetic Algorithm approach

With the rapid development of the global manufacturing industry, an efficient and accurate quality control system has become key to enhancing competitiveness. Ultrasonic Nondestructive Testing (NDT), as an efficient means of quality inspection, plays a crucial role in improving manufacturing quality through the precision of its data analysis. This study aims to explore the application of ultrasonic NDT data in manufacturing quality control by integrating machine learning technologies, with a specific focus on the Wavelet Neural Network optimized by Genetic Algorithms (GA-WNN). This study achieved significant prediction and evaluation results by applying a GA-WNN to quality control in manufacturing. Compared to traditional Wavelet Neural Network (WNN) models, the GA-WNN more effectively identifies and predicts potential quality issues, especially in noisy data and complex production environments, demonstrating higher accuracy and stability. When predicting possible defect types in the manufacturing process, the GA-WNN showed a notable improvement in accuracy over other models. Additionally, in quality stability evaluation, GA-WNN was able to capture production fluctuations more accurately, providing more valuable results for decision-making. The methodologies and discoveries of this study offer new perspectives and tools for quality control in manufacturing and the analysis of ultrasonic NDT data, presenting broad application prospects.

Analysis of the Remaining Life Assessment of Fb-8701c Spherical Tank based on Thickness Measurement Data with Ultrasonic Testing Method

This remaining life assessment assessed the thickness and corrosion rate characteristics of A516 Grade 70 carbon steel, a material widely used in the fabrication of pressure vessels and boilers, including spherical tanks. The material was evaluated through ultrasonic non-destructive testing, with the aim of determining the minimum thickness and corrosion rate, based on field data collected by the inspector. The data was then analyzed to determine the minimum allowable thickness and estimate the remaining life of the pressure vessel based on API 510 standard. The study results showed that all components examined were within safe limits for use, above the required thickness (actual thickness > required thickness). The calculation of the highest corrosion rate is on the Top Head Plate 7 component, which is 0.067 mm/year, so this component has the shortest remaining life, which is 54.06 years. This study also calculates the maximum allowable working pressure (MAWP) value which is a reference to ensure the safety of pressure vessels when operated. The working pressure when operated must not exceed the calculated MAWP value. The calculated MAWP value is in the range of 6.99 to 8.12 psi.

In-situ detection of electrolyte defects in lithium-ion batteries using ultrasonic imaging and analysis of formation patterns

The urgent necessity for enhanced cycling performance in lithium batteries is paramount for electric vehicles to attain broader economic advantages. However, prolonged usage and cycling often lead to electrolyte de-wetting within the battery, posing a significant risk to battery safety and lifespan reduction. A thorough understanding of the mechanisms underlying electrolyte de-wetting is pivotal for the development of long-lasting batteries. This study employs an ultrasonic non-destructive testing method to investigate changes in the internal electrolyte during battery operation. Analysis of the results found that during to the relatively higher current density and temperature near the tabs, electrolyte de-wetting is more prone to occur in these areas, with defects being more severe at the positive electrode compared to the negative electrode. Finally, these issues are addressed to provide reasonable recommendations for future battery design and optimization.