Microwave Nondestructive Testing Research Articles

Microwave Nondestructive Testing of Nonmetallic Pipes: An Overview of the Major Developments

Microwave Nondestructive Testing of Nonmetallic Pipes: An Overview of the Major Developments

Simulations and experimental study on imaging of thick defect in reusable thermal protective system using microwave NDT

Reusable thermal protective system (TPS) plays a key role for protecting space vehicles against the aerothermal heating. Thus the characterization of TPS performance becomes an important issue in their production, processing and applications. This paper demonstrates the detection and imaging of inner disbond and thick defect, which considerably compromises the performance of TPS materials within a two-layer TPS configuration (a special designed thick heat insulation tile backed by an aluminum alloy plate). The methodology employed for this investigation is a near-field microwave non-destructive testing (NDT), encompassing both simulations and experimental study. A 3D microwave model for the two-layer TPS structure containing the specified defects was established, and the magnitude and phase of the waveguide input impedance (i.e. S11 parameters) for each node within the X-band (8.2 ∼ 12.4 GHz) frequency range were obtained in the work. The simulation results reveal distinct featured amplitude and phase images of defects at various frequencies. Correlation processing algorithms were employed to make the images dimensionally reduction and featured enhancement. Finally, the results were verified by experimental measurements using an open-ended rectangular waveguide. Interestingly, the disbond defect between the two layers could be clearly identified by the phase information after correlation processing algorithms. The proposed method appears to offer a contactless, intuitive, and effective approach for characterizing defects within TPS materials.

Pearson Correlation and Multiple Correlation Analyses of the Animal Fat S-Parameter

In specialised food products, the detection and identification of various sources of oils and fats are essential. Consumers are concerned about the adulteration of vegetable oils to avoid health problems and the risk of developing heart disease. There were a few cases where less expensive animal fat, in particular pork fat, was found in halal meals. The limitations of conventional methods for detecting pork in other meats can be overcome with the use of non-destructive methods. This study reveals the feasibility of using the microwave non-destructive testing method to identify animal fats. In the frequency between 8 GHz and 12 GHz, the microwave non-destructive testing method was used to obtain the S-parameters of the chicken, beef, and pork fats in the form of raw, baked, and oil. Single and multiple correlation analyses were then performed to identify the relationship between the animal fats and to differentiate them. The results of the Pearson correlation analysis show that for all animal fats, both raw and baked fat had a higher correlation value, which indicated they have the same or similar features. The different correlation values obtained from the multiple correlation analysis demonstrated that animal fats have distinct features, regardless of whether they are raw, baked, or liquid forms. The multiple correlation analysis is a more suitable technique to detect animal fats compared to the Pearson correlation analysis.

Fast VGG: An Advanced Pre-Trained Deep Learning Framework for Multi-Layered Composite NDE via Multifrequency Near-Field Microwave Imaging

ABSTRACT Microwave nondestructive testing (NDT) techniques show promise for composite inspection due to microwave signals’ ability to penetrate and interact with internal structures. However, current microwave imaging approaches have poor spatial resolution, struggling to distinguish defects from defect-free regions. This limits reliable subsurface analysis and widespread adoption. This paper introduces a novel multi-frequency microwave imaging fusion method using an open-ended waveguide and deep learning to enhance defect detection accuracy in carbon fiber-reinforced polymer (CFRP) composites. The proposed technique employs an optimized feature extraction strategy to improve differentiation between defective and sound areas for superior subsurface visualization. The method leverages VGG-19 for efficient feature extraction and parallel processing to merge information from multi-frequency images. Our method significantly improved detection accuracy and F1 score, surpassing non-deep learning image fusion techniques by at least 50%. The optimized fusion strategy enables clear visualization of various defect types across multiple subsurface layers. Our approach also reduces computation time versus standard VGG implementation by 3–4×, showing scalability. The results demonstrate the proposed method’s potential to overcome existing constraints and provide rapid, accurate subsurface analysis of complex CFRP structures through an optimized deep learning framework, holding significance for expanding microwave NDE applications.

Detection and characterisation of defects in composite materials using microwave non-destructive testing methods

This paper proposes a technique for detecting impact-induced surface and subsurface fractures in Glass Fibre Reinforced Composites (GFRPs) and hybrid composite materials. Various categories of composite samples, namely Unidirectional GFRP, Bidirectional GFRP, Woven mat GFRP and Carbon-flax hybrid composites are fabricated by the hand-layup process, and cracks are induced in the samples by three-point flexural bending test using a Universal Testing Machine. Near-field microwave Non-Destructive Testing (NDT) is employed in detecting the above cracks by recording the S11 parameter from the microwave transceiver probe throughout the sample. For image rendering the resulting outputs are passed as a 2-dimensional array to the image-rendering algorithms, namely Iterative Curve-Based Interpolation (ICBI), Improved New Edge-Directed Interpolation (INEDI) and the Lanczos algorithm. The most suitable algorithm to identify the clear picture and size of crack is identified, and the effectiveness of Microwave NDT for these composites is analysed.

Inspection of antennas embedded in smart composite structures using microwave NDT methods and X-ray computed tomography

Antennas embedded in composites are key components in communication, radar, and sensing systems aboard aircrafts. The manufacturing complexities and in-service loading could compromise the integrity of the composite structure through defects within the antenna which undermine the structure utility. Therefore, Non-Destructive Testing (NDT) is needed to ensure antenna structure integrity pre- and post- commissioning. While X-Ray computed tomography (CT) is the gold standard method for these inspections as it provides high sensitivity and resolution, it is relatively expensive, slow, and inapplicable for in-situ inspection. This paper devises various Microwave NDT methods towards composite-embedded antenna inspection, unprecedentedly. By direct benchmarking against X-Ray CT, it will be demonstrated that a low-frequency resonant microwave probe provides a viable alternative to X-Ray CT with similar detection sensitivity (85 % defect detection in inspected samples). Moreover, the resonant microwave probe provides multifold enhancement in the sensitivity (>10x) compared to waveguide probes used conventionally for microwave NDT.

Inspection of thick composites: a comparative study between microwaves, X-ray computed tomography and ultrasonic testing

ABSTRACT Inspection of thick and low-density multilayer composite structures is of paramount importance. X-ray computed tomography (CT) and phased array ultrasonic testing (PAUT) are widely employed modalities for non-destructive testing (NDT) of these composites. Owing to low density of constituent materials typically utilised to construct composites and rich wave scattering within their structures, inspecting them using X-ray CT and PAUT does not always yield acceptable flaw detection results. On the other hand, microwave NDT techniques have shown to be particularly suitable for inspecting thick multilayer non-carbon-based composites. However, the relative performance of emerging microwave NDT techniques and the widely accepted X-ray CT and PAUT is yet to be established. This paper provides first-of-its-kind experimental comparison between microwave, X-ray CT, and PAUT on a comprehensive set of thick composite samples with different defect types. It is demonstrated herein that microwave NDT performed on par with X-ray CT in terms of defect detection capability (qualitatively and quantitatively), and in many cases outperformed PAUT. A detailed summary overviewing the performance, advantages and shortcomings of each method for particualr defect types is included which disseminates new knowledge to benefit practitioners and researchers alike.

Detection of surface cracks on slanted surfaces and through thick irregular insulation layers using polarimetric microwave imaging

Reliable detection of cracks on slanted metal surfaces and through irregular insulation/coating is critical in many inspection applications. Microwave non-destructive testing (NDT) techniques have proven to be effective for crack detection and characterization. However, the utility of conventional microwave NDT techniques for challenging inspection scenarios which involve cracks on slanted surfaces, or cracks under relatively thick, and possibly, irregular insulation has been limited. In this paper, a polarimetric microwave imaging approach is employed for the detection of cracks in such inspection cases. The detection concept using interrogation with dual polarized wave to suppress the background is presented. It is established herein that the cross-polarization measurement is independent to irregular surfaces, and therefore, it constitutes an effective mean for reliable crack detection even if the background is not uniform or if the metal surface is slanted. This fact is further corroborated by numerical electromagnetic simulations and measurements. The efficacy of the proposed method is demonstrated using imaging results of various samples spanning the abovementioned scenarios.

A novel microwave-based non-destructive testing system for detection of sub-surface defects in natural flax fiber reinforced composite material

In the work presented here, microwave non-destructive testing using a rectangular waveguide loaded with a novel planar unit cell filter for the inspection of defects in the composite is studied. The proposed planar filter is designed as a unit cell with a copper (annealed) microstrip component on an FR-4 substrate. The waveguide loaded with the planar filter exhibits a reflection coefficient of −35.919 dB at a frequency of 10.548 GHz. This planar filter waveguide is used as the sensor probe for the non-destructive testing method proposed here and is aimed to increase the sensitivity by measuring the differences in permittivity of materials. Computer simulation technology software (CST studio suite) is used for implementing the design, simulation, and material/defect evaluation. The defects studied are voids, cracks, and delamination in an epoxy-based natural flax fiber-reinforced composite material. The performance-improved microwave sensor probe is used to pick up permittivity variations in the presence of defects in lax-based natural fiber-reinforced composite material.

Investigation on Dielectric Properties of Sludge Waste from Water Treatment Using Microwave Non-Destructive Testing (MNDT)

The demand for water cleanup rises in tandem with a country's requirements and development. Recovery of purified water containing nutrients and other beneficial materials is a critical opportunity that must be taken advantage of. A challenge that needs to be tackled is the necessity for large capacity and high-value management of sludge waste following the water treatment process. The pH level and microwave frequencies influence were used as a starting point for assessing the content of the sludge waste. Microwavenon-destructive testing (MNDT) is a microwave measurement that can be used to determine the dielectric characteristics of materials without destroying or modifying the sample's content. The methodology employs a free-space measurement technique with a frequency range of 8 to 12 GHz (X-band). Through S-parameters acquired, a correlation analysis was done to analyze the effect of frequencies with the sludge waste. A comparative investigation with peat soil samples in establishing if the sludge has similar attributes to normal soil is used to ensure the accuracy of the sludge waste data. It can be determined that the sludge waste has a high signal correlation towards the frequency band 8 GHz to 12 GHz, which is compatible with the MNDT approach. All of the sludge samples had a pH range that is appropriate for agricultural use.

A new microwave nondestructive testing method of relative permittivity and conductivity for stratified media based on dyadic Green's function I: Theory

Stratified composite materials are employed in many industrial scenes, including bridge construction, vehicle, ship, aviation, etc. However, minor imperfections in composite materials can have significant effects and even result in disasters when used. In this work, a multi-parameter inversion method that can simultaneously reconstruct the conductivity and permittivity parameters of layered media is devised and presented for microwave nondestructive testing (NDT) of the minor imperfections in stratified composite materials. Firstly, the calculations for the electric field in the layered medium model with the emission source as the point source are provided. Secondly, the spatial electric field variations (the Fréchet derivative) caused by changes in conductivity and permittivity of arbitrary layers are calculated. Then the permittivity and conductivity are reconstructed using the Distorted Born iterative method (DBIM) technique. The numerical examples at microwave frequencies show that the inversion technique has a strong capacity to reconstruct for a single parameter (relative permittivity or conductivity) and multi-parameter (relative permittivity and conductivity). Our suggested multi-parameter inversion technique in this research has excellent potential for use in the identification of multilayer composites and subsurface inclusions.

Quantitative Visualization of Buried Defects in GFRP via Microwave Reflectometry.

Glass fiber-reinforced polymer (GFRP) is widely used in engineering fields involving aerospace, energy, transportation, etc. If internal buried defects occur due to hostile environments during fabrication and practical service, the structural integrity and safety of GFRP structures would be severely undermined. Therefore, it is indispensable to carry out effective quantitative nondestructive testing (NDT) of internal defects buried within GFRP structures. Along with the development of composite materials, microwave NDT is promising in non-intrusive inspection of defects in GFRPs. In this paper, quantitative screening of the subsurface impact damage and air void in a unidirectional GFRP via microwave reflectometry was intensively investigated. The influence of the microwave polarization direction with respect to the GFRP fiber direction on the reflection coefficient was investigated by using the equivalent relative permittivity calculated with theoretical analysis. Following this, a microwave NDT system was built up for further investigation regarding the imaging and quantitative evaluation of buried defects in GFRPs. A direct-wave suppression method based on singular-value decomposition was proposed to obtain high-quality defect images. The defect in-plane area was subsequently assessed via a proposed defect-edge identification method. The simulation and experimental results revealed that (1) the testing sensitivity to buried defects was the highest when the electric-field polarization direction is parallel to the GFRP fiber direction; and (2) the averaged evaluation accuracy regarding the in-plane area of the buried defect reached approximately 90% by applying the microwave reflectometry together with the proposed processing methods.

Evaluation of the applicability of microwave nondestructive testing to crack detection on a pipe inner surface

This study explores the applicability of microwave nondestructive testing to detect a metal pipe’s inner crack. Three-dimensional finite element simulations were conducted to study the inspectability of cracks using microwaves in different modes and the dependency of the reflection characteristics of microwaves on crack size. The simulation results showed that microwaves in the TM01 and TE01 modes can detect circumferential and axial cracks, respectively. The positive relationship between crack size and intensity corresponding to the reflected microwaves was obtained in simulations and then verified by experiments. In both simulations and experiments, the axial crack length showed a small influence on the results, especially for shallow crack detection. In the experiments, circumferential and axial cracks with a width of 0.3 mm were detected using microwaves. The experimental results revealed that signal amplitudes decreased when a slit penetrated a pipe wall, probably due to a microwave leakage.

Defects Detection Method Based on Programmable Spoof Surface Plasmon Polaritons in Non-Metallic Composites

Microwave nondestructive testing (NDT) offers promising application prospects due to its advantages of non-contact inspection in detecting defects in non-metallic composites. However, the detection sensitivity of this technology is generally affected by the lift-off effect. To reduce this effect and highly concentrate electromagnetic fields on defects, a defect detection method using scanning instead of moving sensors in the microwave frequency range was proposed. Additionally, a novel sensor based on the programmable spoof surface plasmon polaritons (SSPPs) was designed for non-destructive detection in non-metallic composites. The unit structure of the sensor was made up of a metallic strip and a split ring resonator (SRR). A varactor diode was loaded between the inner and outer rings of the SRR, and by changing the capacitance of this diode using electronic scanning, the field concentration phenomenon of the SSPPs sensor can be moved along a specific direction for defect detection. By using this proposed method and sensor, the location of a defect can be analyzed without moving the sensor. The experimental results demonstrated that the proposed method and designed SSPPs sensor can be effectively applied in detecting defects in non-metallic materials.

Microwave NDT of Smart Composite Structures with Embedded Antennas

The integration of antennas in composite structures is gaining popularity with advances in wireless communications and the ever-increasing demands for efficient smart structures. Efforts are ongoing to ensure that antenna-embedded composite structures are robust and resilient to inevitable impacts, loading and other external factors that threaten the structural integrity of these structures. Undoubtedly, the in situ inspection of such structures to identify anomalies and predict failures is required. In this paper, the microwave non-destructive testing (NDT) of antenna-embedded composite structures is introduced for the first time. The objective is accomplished using a planar resonator probe operating in the UHF frequency range (~525 MHz). High-resolution images of a C-band patch antenna fabricated on an aramid paper-based honeycomb substrate and covered with a glass fiber reinforced polymer (GFRP) sheet are presented. The imaging prowess of microwave NDT and its distinct advantages in inspecting such structures are highlighted. The qualitative as well as the quantitative evaluation of the images produced by the planar resonator probe and a conventional K-band rectangular aperture probe are included. Overall, the potential utility of microwave NDT for the inspection of smart structures is demonstrated.

Investigation of Animal Fats Using Microwave Non-Destructive Testing at X-Band

Investigation of Animal Fats Using Microwave Non-Destructive Testing at X-Band

A hemispherical near‐field focusing antenna for non‐metallic delamination detection

This paper proposes a novel hemispherical near-field focusing radial line slot array (RLSA) antenna to detect delamination within non-metallic composites. By adjusting the positions and dimensions of the antenna slots, the near-field focusing antenna produced a radiation pattern with a narrow beamwidth and a low side lobe level of the normal component of the electric field. Since the electromagnetic (EM) field of the near-field focusing antenna can be focused on the material under test (MUT), delamination within it will significantly affect the reflected information of the antenna. Simulation and measurement have shown the good focusing characteristics of the novel RLSA antenna, a low side lobe level (−19 dB) and a narrow beamwidth (6 mm) were attained. Taking glass fiber reinforced polymer (GFRP) sheets as MUTs, delamination with different thicknesses are detected using a set of mechanical scanning experimental devices. The results show that delamination as thin as 30 μm can be detected. The proposed near-field focusing antenna can detect thinner delamination inside non-metallic composites at a lower cost and in a simpler way than other microwave non-destructive testing (NDT) methods.

Assessment of Defects under Insulation Using K-Medoids Clustering Algorithm-Based Microwave Nondestructive Testing

Composite insulations, such as ceramics, are commonly utilized in the turbine system as a thermal coating barrier to protect the metal substrate against high temperatures and pressure. The presence of delamination in the composite insulations may cause turbine failure, leading to a catastrophic accident. Thus, regular non-destructive testing is required to detect and evaluate insulation defects. Among the non-destructive testing techniques, the microwave technique has emerged as a promising method for assessing defects in ceramic coatings. Although the method is promising, microwave non-destructive testing suffers from poor spatial imaging, making the defect assessment challenging. In this paper, a novel technique based on microwave non-destructive testing with a k-medoids clustering algorithm for delamination detection is proposed. The representative ceramic coating sample is scanned using a Q-band open-ended rectangular waveguide with 101 frequency points that operated between 33 to 50 GHz. The measured data is transformed from the frequency domain to the time domain using an inverse fast Fourier transform. The principal component analysis is then used to reduce the dimensionality of 101 time steps into only 3 dominant attributes. The attributes of each inspected location are classified as defect or defect-free using the k-medoids clustering algorithm for accurately detecting and sizing the defects in the ceramic insulation. The results reported in this paper highlight the superiority of the k-medoids clustering algorithm in delamination detection, with an accuracy rate of 95.4%. This is a significant step forward compared to earlier approaches for identifying ceramic defects.

Quantitative evaluation of pipe wall thinning defect sizes using microwave NDT

ABSTRACT This study investigated the applicability of microwave non-destructive testing, which has been proved effective in quickly detecting the defect location in a long pipe, to the size evaluation of wall thinning defects. Artificial wall thinning defects with different sizes (depths and lengths) and edge profiles were introduced to a flanged brass pipe with a total length of 15 m, and reflected microwave signals were measured in experiments. A signal processing method combining windowing and dispersion compensation was proposed to extract the defect-related reflection signals in the frequency domain. Resonant frequencies, at which the amplitude of extracted signals dropped significantly, decreased with the increase of either wall thinning depth or length. In addition, the results demonstrate that wall thinning location and pipe end conditions have little influence on resonant frequencies after signal processing. A back propagation neural network was trained by simulation data, using resonant frequencies as the input, to simultaneously evaluate defect depth and length, and the performance was validated by experiments. Maximum prediction errors of depth and length of wall thinning were 0.06 mm and 0.57 mm, respectively, which indicated the feasibility of the proposed method to evaluate the wall thinning defect sizes.

A Microwave Time Domain Reflectometry Technique Combining the Wavelet Decomposition Analysis and Artificial Neural Network for Detection of Defects in Dielectric Structures

The microwave nondestructive testing (MNDT) technique is an attractive candidate for defect detection in the dielectric structures as microwave signals can penetrate inside and interact with the insides of the dielectric material. In this research, a novel MNDT technique is presented for defect detection combining the time domain reflectometry (TDR) technique, the wavelet decomposition analysis (WDA) technique, and an artificial neural network (ANN), which has good generality and can be applied to various dielectric materials with different thicknesses. This method is based on scanning the dielectric material with an open-ended rectangular waveguide (OERW) operating from 18 to 26.5 GHz with 201 frequency points and analyzing its reflections using the proposed algorithm. The method was validated by simulation and experiments successfully. In the simulations, two typical kinds of dielectric materials were used to verify the generality and accuracy of this proposed method. The accuracies of these two dielectric materials were 96% and 90%, respectively. A 2-D image of detection can be constructed by performing repetition of the process with a certain step on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xy</i> plane of a material, which is illustrated by simulations. Meanwhile, the sensitivity analysis was performed by studying the similarity quantified using the Pearson’s coefficient correlation. Eventually, some FR4 samples were machined to demonstrate the feasibility of this method. The acceptable accuracy is 86% of the experimental result.