Non-destructive Testing Techniques Research Articles

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.

Effect of axial stresses on longitudinal guided waves in a fluid-filled pipe.

This paper describes the study of the acoustic field of a fluid-filled pipe subjected to axial stress based on the acoustoelastic theory. The pipe with applied axial stresses can be approximated as a transversely isotropic pipe, and hence, its acoustic fields can be expressed using potential functions. The velocity changes of longitudinal wave modes with applied stresses are analyzed for the pipe filled with oil by an analytical method. It was found that the longitudinal mode velocity changes almost uniformly with the applied stresses. The high speed and low frequency plateaus of longitudinal wave modes are sensitive to stress. The relationship between stress and the velocity change of the guided wave is given. The results indicate that non-destructive testing techniques using longitudinal wave modes have strong potential to identify and monitor the stress levels in pipe structures.

A Novel Dual-Mode Sensor for the Detection of Interface Flaw in “Insulator-Conductor” Composite Structures

“Insulator-conductor” composite structures have been extensively used in a vast number of fields in recent years. However, due to different factors including but not limited to environmental and adverse working conditions, the interface between the insulator and conductor is prone to flaws. Therefore, non-destructive testing (NDT) techniques are needed to inspect the functionality and integrity of such composite structures. A dual-mode sensor that combines principles of eddy current and capacitive sensing is proposed to detect the interfacial flaws in “insulator-conductor” composite structures. The proposed sensor uses phase switching excitation to switch between inductive and capacitive detection modes, and the feasibility of the method was analyzed by equivalent circuit analysis and Finite Element (FE) simulations. The dual-mode sensor was fabricated on a single Printed Circuit Board (PCB) and used for experimental investigation on specimens of “Perspex-aluminum” composite structures. The experimental results verified the results of equivalent circuit analysis and FE simulation, and showed that the proposed sensor is able to detect corrosion under insulation, surface cracking on the conductor and interface debonding. Results also show that the detected information of the two modes complement each other, proving that the detection method has the ability to evaluate flaws in complex situations at the interface.

Experimental Evaluation Method of Carbonation in Concrete Structure Using Nonlinear Resonant Ultrasonic Spectroscopy

Carbonation is a typical type of concrete deterioration that can cause various physicochemical changes and adversely affects the performance of reinforced concrete structures, particularly in offshore structures. Nonlinear resonant ultrasonic spectroscopy (NRUS), which is a non-destructive test technique, is applied in this study to experimentally evaluate the degree of concrete carbonation without damaging the structure. Nonlinearity parameters of test specimens exposed to accelerated carbonation conditions are measured via NRUS. The measured nonlinearity parameters indicate lower values for longer carbonation durations. This is because internal microcracks are reduced by CaCO<sub>3</sub>, which is a carbonation product. The consistent trend of the experimental results shows that NRUS can be effectively applied to evaluate concrete carbonation.

Characterization of Deformation of Bolts and Induced Stress Wave Propagation under Axial Tensile Stress

The nondestructive testing technique used to evaluate the quality of bolt support by detecting the axial force is suitable for the bolt with a short construction time. For the bolt in the support state for a long time period, it may lead to the detection of small axial force only and ignore the fact that the bolt has entered the necking fracture stage, and thus fail to detect safety hazards in time. Three sets of parallel tests were conducted to solve the misjudgment of detection results due to the reduction of axial force when the bolt was in the necking fracture stage. Bolt deformation characteristics and stress wave propagation characteristics in bolts under axial tensile stress were studied by using the bolt tensile and stress wave detection test system which was modified and built independently. The results showed that (1) The diameter of the necking position decreases continuously during the tensioning process of the bolt and the rate of decrease increases abruptly during the necking fracture stage. (2) Due to the bolt stretching, the stress wave propagates in the bolt with different degrees of reflection and transmission, resulting in the attenuation of the stress wave energy; the energy attenuation ratio of the stress wave signal in the necking fracture stage reaches 35%, and the energy attenuation ratio increases exponentially as the necking continues to occur. (3) The frequency distribution of the stress wave signal during the bolt stretching process is from scattered to concentrated, the dominant frequency is gradually prominent and changes from low frequency to high frequency, the high-frequency signal is more sensitive to the cracks and necking of the bolt, and the dominant frequency is between 9500 and 10,000 Hz. (4) The average error of the stress wave method is 1.945% and the maximum and minimum values are 4.12% and 0.51%, respectively. The method is promising and provides a reference for the study of nondestructive testing of bolt stress waves in field support.

Roughness detection method based on image multi-features

Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

Identifying Weak Adhesion in Single-Lap Joints Using Lamb Wave Data and Artificial Intelligence Algorithms

In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive testing techniques to detect weak adhesion. These are based on Lamb wave (LW) data and artificial intelligence algorithms. A dataset consisting of simulated LW time series extracted from single-lap joints (SLJs) subjected to multiple levels of weak adhesion was generated. The raw time series were pre-processed to avoid numerical saturation and to remove outliers. The processed data were then used as the input to different artificial intelligence algorithms, namely feedforward neural networks (FNNs), long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs), for their training and testing. The results showed that all algorithms were capable of detecting up to 20 different levels of weak adhesion in SLJs, with an overall accuracy between 97% and 99%. Regarding the training time, the FNN emerged as the most-appropriate. On the other hand, the GRU showed overall faster learning, being able to converge in less than 50 epochs. Therefore, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them the most-suitable choices. The proposed approach constitutes a new framework allowing the creation of standardized data and optimal algorithm selection for further work on nondestructive damage detection and localization in adhesive joints.

Deep Transfer Learning Models for Industrial Fault Diagnosis Using Vibration and Acoustic Sensors Data: A Review

In order to evaluate final quality, nondestructive testing techniques for finding bearing flaws have grown in favor. The precision of image processing-based vision-based technology has greatly improved for defect identification, inspection, and classification. Deep Transfer Learning (DTL), a kind of machine learning, combines the superiority of Transfer Learning (TL) for knowledge transfer with the benefits of Deep Learning (DL) for feature representation. As a result, the discipline of Intelligent Fault Diagnosis has extensively developed and researched DTL approaches. They can improve the robustness, reliability, and usefulness of DL-based fault diagnosis techniques (IFD). IFD has been the subject of several thorough and excellent studies, although most of them have appraised important research from an algorithmic standpoint, neglecting real-world applications. DTL-based IFD strategies have also not yet undergone a full evaluation. It is necessary and imperative to go through the relevant DTL-based IFD publications in light of this. Readers will be able to grasp the most cutting-edge concepts and develop practical solutions to any IFD challenges they may encounter by doing this. The theory behind DTL is briefly discussed before describing how transfer learning algorithms may be included into deep learning models. This research study looks at a number of vision-based methods for defect detection and identification utilizing vibration acoustic sensor data. The goal of this review is to assess where vision inspection system research is right now. In this respect, image processing as well as deep learning, machine learning, transfer learning, few-shot learning, and light-weight approach and its selection were explored. This review addresses the creation of defect classifiers and vision-based fault detection systems.

Automatic Detection of the Orientation of Strain Gauges Bonded on Composite Materials with Polymer Matrix, in Order to Reduce the Measurement Errors.

Composite materials with a polymer matrix are used on a large scale to make light structures that involve high responsibility. The failure mechanisms of composite materials are very complex and for this reason, advanced techniques for damage detection and the assessment of structural integrity are required. The continuous structural health monitoring (SHM) uses nondestructive testing (NDT) techniques, sensors integrated into the structures, computers and dedicated software. This article presents a new automatic and precise method for detecting the orientation of strain gauges glued onto composite materials with a polymer matrix. The automatic identification of both the directions of the reinforcing fibers and that of the orientation of the strain gauge, respectively, allows for the calculation of the angle between these two directions. By knowing the difference between the nominal value of this angle and the value actually obtained after gluing the strain gauge, corrections obtained by calculation on the experimental values can be applied, using equations found in specialized literature. In this way, a drastic reduction of measurement errors introduced by the misalignment of strain gauges glued on composite materials can be achieved, resulting in a significant increase of measurement accuracy, which contributes to increasing the security of the monitored structures.

Ultrasonic and Impact-Echo Testing for the Detection of Scaling in Geothermal Pipelines

The efficiency and longevity of components in geothermal plants are significantly reduced by mineral deposition in pipes, also known as scaling [1]. A number of methods are used to monitor fouling (a broader term for all types of organic and inorganic deposition) in other industries, such as the oil, gas and food industries. These include direct and indirect, offline, in-situ and online, as well as inline methods, each of which have pros and cons [2, 3]. An effective monitoring solution specific to the high temperature conditions in geothermal plants, which optimises maintenance and cleaning measures in dependency of scaling, has yet to be developed. In this paper, two non-destructive testing techniques: contact ultrasonic testing (UT) and impact-echo (IE) testing are investigated in their viability as methods to detect scaling growth in geothermal plants. A descaling measurement was conducted on a heavily scaled segment of an obsolete pipeline from the production well of the geothermal power plant in Sauerlach, Germany. The pipeline segment was inserted in a test rig and the scaling was gradually etched away using an acidic solution. At regular time intervals, the scaling thickness was measured mechanically and contact UT as well as IE measurements were carried out. The testing apparatus was designed to withstand high temperatures ({140},^{circ }{text {C}} at the inlet pipe surface) and to be easy to install whilst being cost-efficient. Both techniques yielded usable results with submillimeter resolution. The advantages and limitations of the two methods are discussed. Impact-echo testing, in particular, can be automated and presents a simple and cost-efficient scaling monitoring option.

Fibre volume fraction screening of pultruded carbon fibre reinforced polymer panels based on analysis of anisotropic ultrasonic sound velocity

Composites have become the material of choice in a wide range of manufacturing applications. Whilst ultrasound inspection is a well-established non-destructive testing (NDT) technique, the application to composite imaging presents significant challenges stemming from the inherent anisotropy of the material. The fibre-volume fraction (FVF) of a composite plays a key role in determining the final strength and stiffness of a part as well as influencing the ultrasonic bulk velocity.In this work, a novel FVF determination technique, based on the angular dependence of the sound velocity with respect to the composite fibre direction, is presented. This method is introduced and validated by inspection of pultruded carbon fibre reinforced polymer (CFRP) panels commonly used in the manufacture of high-power wind turbine blades. Full matrix capture (FMC) data acquired from a phased array (PA) ultrasonic probe is used to generate calibration data for samples ranging in FVF from 60.5% to 69.9%. Sample velocity, as a function of propagation angle, is used to estimate the FVF of samples and ensure they fall within the desired range. Experimental results show values of 61.1, 66.1 and 68.3%, comparing favourably to the known values of 60.5, 66.3 and 69.9% respectively.The work offers significant potential in terms of factory implementation of NDT procedures to ensure final parts satisfy standards and certification by ensuring any FVF inconsistencies are identified as early in the manufacturing process as possible.

Application of Non-destructive Testing (NDT) Techniques on Reinforced Concrete Structure: A Review

Abstract: To properly maintain our public infrastructure, engineers and designers must learn different methods of inspection. An exhaustive review has been carried out for different aspects of non- destructive testing (NDT) adopted for RCC structures. NDT evaluates the remaining operation life of different components of structure. It provides an accurate diagnosis which allows prediction of extended life operation beyond the designed life. Different aspects are considered which includes condition assessment, durability, corrosion, condition ranking and service life of structures. In this review, several non-destructive inspection methods are evaluated, with the aim of identifying those, which are practical for detecting defects at early in the production sequence as possible. The methods used for carrying out non destructive analysis used by different investigators are also discussed. Merits and demerits of each method are also stated. RCC structures considered are reinforced buildings, bridges, ESRs, recently developed NDT techniques which are useful for prediction of performance of structure are also included.

Detection and quantification of artificial delaminations in CFRP composites using ultrasonic thermography

Delamination is inevitable during either fabrication or service of a carbon fiber reinforced polymer (CFRP) component. These flaws are major degradation phenomena that are difficult to be detected by nondestructive testing techniques, especially for closed delamination. This study performed an experimental investigation on delamination detection in CFRP plates using ultrasonic thermography with an ultrasonic transducer. In this work, a CFRP composite plate with delaminations was manufactured by a compression molding process. The ultrasonic was excited in the specimen and the heat was activated at the delaminations. Temperature distribution on the specimen surface was recorded by an infrared camera. The effects of wave propagation distance, delamination size, and depth were analyzed. Furthermore, the three most established signal processing techniques of thermography, namely thermal signal reconstruction (TSR), principal component analysis (PCA), and fast Fourier transform (FFT) were employed to deal with the subtracted-background thermal data. The performance of each algorithm was evaluated by temperature contrast, and global signal-to-noise ratio (SNR). Finally, the delamination area was quantitatively measured by Otsu’s thresholding method. The results indicate that the first and second principal components (PC1 and PC2) of PCA provide the highest SNR and the largest number of delaminations detected, respectively. The delamination of ϕ5 and a depth of 1.5 mm can be detected with excellent image quality. Hence, ultrasonic thermography can provide an efficient and cost-effective thermographic method for delamination detection in CFRP structures.

Machine learning based models for defect detection in composites inspected by Barker coded thermography: A qualitative analysis

Machine learning and artificial intelligence have evolved as enablers for automation in various industrial applications. Barker-coded thermography is an active thermal non-destructive testing technique for examining subsurface features in industrial components. This article introduces supervised and unsupervised machine learning models for automatic defect detection in composite specimens inspected by the barker-coded stimulus. This work provides supervised and unsupervised machine learning methods to detect defects in composite specimens examined with a barker automatically coded stimulus. The suggested technology is tested using a carbon fiber-reinforced polymer sample with synthetically reproduced flat bottom hole flaws. The one-class Support vector machine is chosen for the unsupervised class of operation, whereas the supervised technique modifies the traditional Support Vector Machine (SVM). The qualitative comparison suggests that the unsupervised approach presents a less than 1% marginal difference in defect detection from its supervised counterpart.

Effect of thermal fatigue on mechanical properties and microstructure of concrete in constant ambient humidity

The daily and annual temperature differences in mid and high latitudes areas vary greatly, which leads to thermal fatigue deterioration of concrete. The deterioration of concrete properties can also be generated by the cyclic change of humidity in concrete. In the experiment, the thermal fatigue tests of concrete with two strength grades were carried out at the temperature difference of 20 °C, 30 °C, and 40 °C (the initial temperature is 20 °C) under the condition of constant ambient humidity. The macroscopic properties change of concrete such as compressive strength and splitting tensile strength were measured. The microstructure changes of concrete were determined by ultrasonic nondestructive testing technique and mercury intrusion test. The results indicated that thermal fatigue has obvious deterioration effect on concrete. With the increase of temperature difference and cycle number, the strength of concrete decreased dramatically and the decrease of C40 concrete was greater than that of C25 concrete. The splitting tensile strength was more sensitive to thermal fatigue than compressive strength. The ultrasonic wave velocity gradually decreased and the damage factor increased, indicating the internal crack defects of concrete increased. At the same temperature difference, the porosity, total pore volume, average pore diameter, medium pore diameter, and the most probable pore diameter of concrete increased gradually with the increase of the cycle number, while the total pore surface area decreased. The pore structure showed the characteristics of coarsening and the trend of deterioration. The porosity of C40 concrete was less than that of C25 concrete, but the relative change value of porosity was larger, which reveals the internal reason for the strength damage of concrete under thermal fatigue at the microscopic level.

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography.

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented.

Field Inspection of High-Density Polyethylene (HDPE) Storage Tanks Using Infrared Thermography and Ultrasonic Methods

High-density polyethylene (HDPE) is widely used for above-ground storage tanks (ASTs). However, there are currently no guidelines for the non-destructive testing (NDT) and evaluation (NDE) of HDPE ASTs. Moreover, the feasibility, limitations, and challenges of using NDT techniques for the field inspection of HDPE ASTs have not been well established. This study used both infrared thermography (IRT) and ultrasonic testing (UT) for the field inspection of HDPE ASTs. Highlighting the implementation challenges in the field, this study determined that: (1) ambient environmental parameters can affect IRT accuracy; (2) there is an ideal time during the day to perform IRT; (3) the heating source and infrared camera orientation can affect IRT accuracy; and (4) with proper measures taken, IRT is a promising method for flaw detection in HDPE ASTs. Additionally, UT can be used following IRT for detailed investigation to quantify the size and depth of defects. The manuscript concludes with a discussion of the limitations and best practices for the implementing of IRT and UT for HDPE AST inspections in the field.

Nondestructive Determination of Young's Modulus of Interconnecting Thin Films by a Broadband Surface Acoustic Wave‐Interdigital Transducer

A facile and rapid method for nondestructive testing (NDT) of the Young's modulus of interconnecting thin films using a broadband surface acoustic wave‐interdigital transducer (SAW‐IDT) is presented herein. A linear periodic SAW‐IDT with 20–125 MHz is designed and fabricated to excite surface acoustic wave (SAW) signals on the samples. By applying burst excitations under different frequencies, the SAW waveforms are picked up by a miniaturized piezoelectric transducer and processed by slant stack transformation. Consequently, the SAW dispersion curves over a wide frequency band are established to match the film parameters. Young's modulus of the 100 and 300 nm thickness of SiO2 films and the 800 and 1500 nm low‐k polyimide (PI) films is characterized. The nanoindentation method, as a control experiment, is also carried out to verify the accuracy of the SAW method, and the test results show that the SAW method can eliminate the effect of the substrate with higher precision. This work shows the advantage of the NDT technique by SAW‐IDT in measuring the Young's modulus of ultrathin films with low cost, controllable frequency range, and high accuracy.

Simulation of Layer Thickness Measurement in Thin Multi-Layered Material by Variable-Focus Laser Ultrasonic Testing

Thin multi-layered materials are widely used in key structures of many high technology industries. To ensure the quality and safety of structures, layer thickness measurement by non-destructive testing (NDT) techniques is essential. In this paper, a novel approach for the measurement of each layer's thickness in thin multi-layered material is proposed by using ring-shaped laser generated focused ultrasonic bulk waves. The proposed method uses a ring-shaped laser with a variable radius to generate shear waves with variable focus inside the structure. By analyzing the signal characteristics at the ring center when the laser radius varies from zero to maximum, the direct measurement of layer thickness can be realized, considering that only when the focal depth and the layer thickness satisfy the specific relationship, the reflected shear waves converge and form a peak at the ring center. This straightforward approach can increase the pulse-echo SNR and prevent the processing of aliasing signals, and therefore provides higher efficiency and accuracy for the layer thickness measurement. In order to investigate the feasibility of this method, finite element simulations were conducted to simulate the ring-shaped laser generated ultrasonic waves in multi-layered structure in detail. Following the principle of the proposed method, the layer thickness of a bi-layer and 3-layer structure were respectively measured using simulation data. The results confirm that the proposed method can accurately and efficiently measure the layer thickness of thin multi-layered material.

Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging

Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.