Flat Bottom Holes Research Articles

Applications of Independent Component Thermography for Testing and Evaluation of Glass Fibre Reinforced Polymer Materials

Abstract A novel post-processing technique is proposed for analysing time series thermographic data obtained by imposing a frequency-modulated heat flux on a Glass Fiber Reinforced Polymer (GFRP) material for Non-Destructive Testing and Evaluation (NDT&E). The proposed approach bridges the gap between the statistical and spatiotemporal analysis of the captured thermographic sequence to inspect and identify the flat bottom holes in the sample. It emphasizes the defect detection capabilities of the Principal Component Analysis (PCA) based thermography named Principal Component Thermography (PCT) and Independent Component Analysis (ICA) based thermography named Independent Component Thermography (ICT) and compares them by using two distinct algorithmic implementations for each method. The effectiveness of these four algorithmic implementations is evaluated using the dynamic range of the temporal profiles. This work presents a significant step toward gaining deeper insight into statistical post-processing techniques for defect identification in InfraRed Thermography (IRT).

Experimental analysis of Local Defect Resonance in an aluminum plate

In the context of non-destructive evaluation and testing of structures, the use of Local Defect Resonance is promising for the detection of delamination-type defects in composites. In this context, an experimental analysis Local Defect Resonance is proposed. It is here carried out on a model system: a thin aluminum plate with a Flat Bottom Hole (FBH) defect consisting in a self-adhesive aluminum sheet (a shim) adhesively bonded over a circular through hole. First, the local defect resonance frequencies and mode shapes are analyzed assuming linear vibrations of the structure. Then, a nonlinear approach is used to compare two types of FBH defects, a first flawless one and a second one including a damaged area. For this purpose a high amplitude vibration around the resonance frequencies are used in order to generate high order harmonics. This enhancement is identified as associated with mainly quadratic nonlinearity.

Damage detection by means of broadband local wavenumber estimation in plate-like structures

This short communication evinces the ability of the CFAT methodology [Q. Leclère, JSV, 2015] to detect and characterize thickness-loss defects on homogeneous flat panels. A contactless measurement of the velocity field of an aluminium plate is performed by scanning laser vibrometry, from which the equivalent rigidity (apparent bending stiffness) is locally estimated within a wide frequency band in the ultrasonic domain. The method allows imaging thickness-reduction defects on the hidden face of an aluminium plate up to several hundreds of kilohertz. The first part details the experimental protocol, followed by a second enhancing the identification of the wavenumbers of symmetric S_0 and antisymmetric A_0 modes. The comparison with theoretical Lamb waves dispersion curves reveals the precision of the method. Finally, the methodology validates the possibility to precisely estimate the local material thickness and as a consequence the location, size and depth of a reference defect in the shape of a flat bottom hole (assuming homogeneous material properties of the plate).

Modelling Low-Frequency Vibration and Defect Detection in Homogeneous Plate-Like Solids

The inspection of thick-section sandwich structures with skins around core materials such as honeycomb, balsa, and foam relies on low-frequency vibration techniques to identify defects through changes in amplitude or phase response. However, current industrial methods are often limited to detecting specific types of defects, potentially overlooking others. Moreover, these methods do not gather detailed information about the defect type or depth, as they only analyse a small portion of the available data instead of the full relevant response spectrum. This paper explores the scientific basis of using low-frequency vibration in the pitch-catch variant for defect detection in homogeneous solids, through analysis of the full relevant frequency spectrum (5–50 kHz). Defects in structures lead to reduced local stiffness and mass in the affected area, causing resonance in the layer above, resulting in amplified vibrations known as local defect resonance (LDR). In this work, an aluminium plate with a 40 mm diameter circular flat-bottomed hole (FBH) at a depth of 1 mm (representing a skin defect) is excited with a chirp signal of 5–50 kHz, and the response is monitored 17 mm away from the excitation point. Finite-element analysis (FEA) is used for the numerical model, addressing challenges in creating an accurate model. The process to optimise the numerical model and the reduce model-experiment error is outlined, including challenges such as the lack of knowledge of material damping. The study emphasizes the importance of modelling the probe’s stiffness and damping effects for achieving agreement between the model and experiment. After incorporating these effects, the maximum LDR frequency error decreased from approximately 3 kHz to less than 1 kHz. In addition, this study presents a method with the potential for defect classification through comparison to modelled responses. The minimum difference error was used to quantify the resonance frequencies’ error between the model and the experiment. Since the resonant frequencies are a function of the defect’s shape, size, and depth, a relatively low root mean squared (RMS) error across the resonance frequency error spectrum indicates the defect’s characteristics. Finally, defect detection and sizing using the pitch-catch probe are explored with a wide-band excitation signal and a line scan through the mid-plane of the defect. A method for defect sizing using a pitch-catch probe is presented and experimentally validated. Accurate defect sizing is achieved with the pitch-catch probe when the defect width is at least ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} twice the 17 mm pin-spacing of the probe.

Defect Detection of GFRP Composites through Long Pulse Thermography Using an Uncooled Microbolometer Infrared Camera.

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites.

Calculation of the DAC curve for images reconstructed by digital aperture focusing method

The widespread introduction of antenna arrays into the practice of ultrasonic testing has made it possible to obtain images of reflectors using either phased array technology or digital aperture focusing (DFA) technology. However, many current regulatory documents regulating the rules for conducting ultrasonic non-destructive testing in the nuclear power industry, petrochemistry, gas industry, etc., require determining the equivalent dimensions of reflectors. The article proposes a method for calculating the DAF—DAC array to determine the diameter of an equivalent flat-bottomed hole (FBH) when analyzing an image. It was shown that it is more efficient to work not with the amplitude of the image, but with the integral amplitude. Numerical experiments have shown the accuracy of determining the diameter of the FBH of the order of ± 0.1 mm. In model experiments, the accuracy of determining the diameter of the FBH turned out to be modulo less than 0.2 mm.

Modeling of defects in ultrasonic flaw detection. Status and prospects

In the introduction to the article four factors, which are most important for ensuring the accuracy of defect parameters measurements at ultrasonic inspection, are mentioned: parameters of artificial reflectors in samples, compliance of acoustic properties of material of tuning samples and controlled products, transient characteristics of electroacoustic paths, methodological features of measurements. The present article is devoted to the analysis of the first and partly fourth of listed factors. The review of reflectors, the use of which is regulated in various standards, is carried out. Advantages and disadvantages of flat-bottomed holes, segmental and angular reflectors (“notches”), lateral (BCO) and vertical cylindrical drills, grooves are noted. Taking into account the peculiarities of ultrasonic wave scattering, it is noted that artificial “reflectors” such as “grooves” and BCOs are used to adjust the parameters of modern diffraction control methods. It is recommended to expand the use of grooves, BCO and vertical drilling in the revision of standards governing the use of classical echo-methods. The estimation of accuracy of measurement of defects parameters, first of all — coordinates of crack tip, with application of modern digital methods of information processing at ultrasonic control is given. It is indicated that to increase the accuracy of measurements, to determine the position and orientation of cracks in welds, it is necessary to create a database of digital doubles of samples with artificial reflectors and products with real defects. The general scheme of quality control work execution is given, taking into account the use of standards (measures), digital models of artificial reflectors and digital twins of the control process to ensure the necessary detectability of defects and reliability of manual, automated and, potentially, automatic control.

Depth prediction of GFRP composite using long pulse thermography

Infrared thermography is a useful technique for detecting defects within composites and estimating their sizes and depths. Previous studies have reported various depth prediction methods, primarily focusing on pulse thermography or lock-in thermography. The core innovation of this study is to explore the applicability of four representative methods including peak contrast time (PCT), peak slope time (PST), log second derivative (LSD) and absolute peak slope time (APST) for defect depth prediction using long pulse thermography. The 1D thermal conduction of square-pulsed heating was analyzed using numerical method to establish the linear relationship between the specific characteristic time and defect depth for these methods. The depths of the flat-bottom holes were predicted using experimental cooling data obtained from glass fiber reinforced plastics panels. Based on the experimental results, the PST, LSD and APST methods exhibited promise for use in defect depth prediction with average measurement errors of 5.77%, 8.96% and 6.25%.

Gauging and Imaging of Pipes using Water-Immersible Ultrasonic Instrumentation System

Abstract The purpose of this research work is to establish the functionality of the novel ultrasonic non-destructive inspection system and accurate gauging of pipes and to locate and visualise flaws in the form of B-Scan cross-sectional view (front-view) of the pipe under test. In this paper, presents a custom-made perspex inspection head assembly integrated with a stand-alone, Li-ion battery-powered and IP67-grade water-immersible ultrasonic instrumentation and gauging system, which enables an efficient assessment of the condition and health of pipes in stringent environments. Extensive inspection was carried out on six samples of 12” Inner Diameter (ID) type carbon steel (CS) pipes with length of 500 mm and having machined wall thickness to simulate loss of wall-thicknesses from 10 % over a length 150 mm of pipe, using 5 MHz spherically focused transducers. Further inspection were carried out on a 12” CS pipe with four notches and four flat bottom holes (FBH) machined on the OD side. Identical flaws were also machined onto 12” CS pipe of total length of 700 mm containing water inside the pipe in flowing condition with water flow rate of 100 litres per minute (LPM). The test results demonstrate the effectiveness of the developed IP67 grade water-immersible ultrasonic pipe inspection and gauging instrumentation system for assessing the condition and health of long-length carbon steel pipes operating in harsh environments.

Decision fusion based on vibrometric data for increase of reliability of damage detection and indentification for plate-like structures

In the realm of structural health monitoring (SHM) for plate-like structures, the need for accurate and reliable damage detection and identification techniques is paramount. This paper explores the application of decision fusion algorithms to vibrometric data, with a focus on increasing the reliability of damage assessment for such structures. The methodology involves leveraging diverse input data sources, including variations in data processing algorithms, excitation parameters, and multiple measurements. By amalgamating these sources of diversity, we aim to mitigate measurement noise and amplify damage indications, ultimately enhancing the precision of the final diagnostic outcome. The study presents results obtained from laser vibrometry measurements on a 10 mm thick aluminum sample containing flat-bottom holes of varying diameters. A network of 12 PZT transducers was used to excite elastic waves, with impulse and chirp excitation techniques employed. Convolution techniques using OpenCV libraries were applied for data processing and fusion. The outcomes reveal a significant reduction in background noise, demonstrating the effectiveness of the fusion approach. Furthermore, the presentation outlines a roadmap for the future development of this methodology, which includes automating data processing, evaluating the impact of damage indicators, processing parameters and usage of different excitation transducers.

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

Utilizing improved YOLOv8 based on SPD-BRSA-AFPN for ultrasonic phased array non-destructive testing

Non-destructive testing (NDT) is a technique for inspecting materials and their defects without causing damage to the tested components. Phased array ultrasonic testing (PAUT) has emerged as a hot topic in industrial NDT applications. Currently, the collection of ultrasound data is mostly automated, while the analysis of the data is still predominantly carried out manually. Manual analysis of scan image defects is inefficient and prone to instability, prompting the need for computer-based solutions. Deep learning-based object detection methods have shown promise in addressing such challenges recently. This approach typically demands a substantial amount of high-resolution, well-annotated training data, which is challenging to obtain in NDT. Consequently, it becomes difficult to detect low-resolution images and defects with varying positional sizes. This work proposes improvements based on the state-of-the-art YOLOv8 algorithm to enhance the accuracy and efficiency of defect detection in phased-array ultrasonic testing. The space-to-depth convolution (SPD-Conv) is imported to replace strided convolution, mitigating information loss during convolution operations and improving detection performance on low-resolution images. Additionally, this paper constructs and incorporates the bi-level routing and spatial attention module (BRSA) into the backbone, generating multiscale feature maps with richer details. In the neck section, the original structure is replaced by the asymptotic feature pyramid network (AFPN) to reduce model parameters and computational complexity. After testing on public datasets, in comparison to YOLOv8 (the baseline), this algorithm achieves high-quality detection of flat bottom holes (FBH) and aluminium blocks on the simulated dataset. More importantly, for the challenging-to-detect defect side-drilled holes (SDH), it achieves F1 scores (weighted average of precision and recall) of 82.50% and intersection over union (IOU) of 65.96%, representing an improvement of 17.56% and 0.43%. On the experimental dataset, the F1 score and IOU for FBH reach 75.68% (an increase of 9.01%) and 83.79%, respectively. Simultaneously, the proposed algorithm demonstrates robust performance in the presence of external noise, while maintaining exceptionally high computational efficiency and inference speed. These experimental results validate the high detection performance of the proposed intelligent defect detection algorithm for ultrasonic images, which contributes to the advancement of the smart industry.

Phased Array Ultrasonic Testing on Thick Glass Fiber Reinforced Thermoplastic Composite Pipe Implementing the Classical Time-Corrected Gain Method

Thermoplastic composite pipe is gaining popularity in the oil and gas and renewable energy industries as an alternative to traditional metal pipe mainly due to its capability of being spooled onto a reel and exceptional corrosion resistance properties. Despite its corrosion-proof nature, this material remains susceptible to various defects, such as delamination, fiber breakage, matrix degradation and deformation. This study employed the phased array ultrasonic testing technique with the implementation of the classical time-corrected gain method to compensate for the significant spatial signal attenuation beyond the first interface layer in the thick multi-layered thermoplastic composite pipe. Initially, the ultrasonic signals from the interface layers and back wall were detected with good signal-to-noise ratios. Subsequently, flat-bottom holes of varying depths, simulating one-sided delamination, were bored and the proposed method effectively identified ultrasonic signals from these holes, clearly distinguishing them from the background noise and interface layer signals. Finally, a defect deliberately fabricated within the composite laminate layers during the pipe manufacturing process was successfully identified. Subsequently, this fabricated defect was visualized in a three-dimensional representation using the X-ray computed tomography for a qualitative and quantitative comparison with the proposed ultrasonic method, showing a high level of agreement.

Inspection of defects in composite structures using long pulse thermography and shearography

Long pulse thermography (LPT) and shearography have been developed as primary methods for detecting debonding or delamination defects in composites due to their full-field imaging, non-contact operation, and high detection efficiency. Both methods utilize halogen lamps as the excitation source for thermal loading. However, the defects detected by the two techniques differ due to their distinct inspection mechanisms. In this study, LPT and shearography are employed to evaluate internal damage in various composite structures. The experimental results demonstrate that LPT, when combined with thermal signal processing algorithms, can clearly detect debonding defects in rubber-to-metal bonded plates, whereas excessive adhesive defects can only be identified by shearography. Flat-bottom holes in the CFRP panel can only be detected by LPT, and shearography is particularly effective for detecting composite materials with a metal skin. For the quantitative measurement of defect sizes, the average errors of the rubber-to-metal bonded plate and CFRP panel using LPT are 4.9 % and 2.2 %, respectively, whereas the average errors of the rubber-to-metal bonded plate and aluminum honeycomb panel using shearography are 15.12 % and 95.4 %, respectively. This indicates that LPT is superior to shearography in quantitatively measuring defect sizes. These two nondestructive testing methods, based on different principles, each have their own advantages and disadvantages. Employing a multi-modal inspection method can leverage their complementary advantages, preventing misdetection and leakage of internal defects in composites.

Quantitative Measurement and Evaluation of High-Resolution Ultrasonic Sound Fields using a Novel Automated Ultrasonic Immersion Scanner

Ultrasonic probes are an integral part of the automated ultrasonic non-destructive testing and evaluation (NDT&E) systems to detect and size the defects in a wide range of structural materials in production lines. A complete quantitative assessment of the performance characteristics of an application-specific UT probe is needed to be evaluated on the basis of the international standard DIN EN ISO 22232-2. This requirement of full assessment not only improves the quality assurance of the manufactured probes by evaluating the acceptance criteria but also provides the useful technical information to the end-user to optimize the automated ultrasonic testing on-site. In addition, the evaluation of probe characteristics should be carried periodically throughout their service life. In this work, we developed a novel high-precision ultrasonic immersion scanner to evaluate the full ultrasonic probe characteristics which include the squint angle measurement in three different reference planes (XY, XZ, and YZ), RF-signal and its frequency spectrum at water-steel interface, focal distance, focal range, focal width, focal length and also the angle of beam divergence in different planes of the sound field distribution. In the newly developed UT immersion scanner, for the first time, we successfully integrated the high-precision Hexapod with six axes to achieve a few micrometer (~15 µm) spatial-resolution in the measured sound field patterns and a good angular resolution (~0.05°) in the squint angle measurement. In addition, we used a verified UT instrument in accordance with the standard DIN EN ISO 22232-1 to obtain more accurate amplitude values (±0.5 dB) while scanning a test block. The automated scanning, data acquisition, evaluation, visualization and the automated test report generation are carried based on the standard DIN EN ISO 22232-2. The measured sound beam characteristics of both focused and non-focused application-specific ultrasonic probes with center frequencies ranging from 0.2 - 15 MHz using the pulse-echo technique on a 3 mm half-ball steel reflector are presented. In addition, the measured sound beams of an application-specific 5 MHz multi-element transmit-receive (TR) probe using the 3 mm flat-bottomed-hole (FBH) reflector in a steel reference plate are discussed. Finally, the quantitative analysis of the measured sound field results and the acceptance criteria in accordance with DIN EN ISO 22232-2 are discussed.

Radial Field Detector to Improve Sensitivity of Localized Defects in Remote Field Eddy Current Technique

ABSTRACT This paper presents the development of a cone-shaped radial magnetic-field-detector–based remote field eddy current (RFEC) probe for enhanced detection of localized defects in ferritic steel tubes. The performance of the radial coil detector has been evaluated for the detection of different types of defects such as through holes, flat bottom holes and notches of different sizes and orientations. The RFEC signals are observed to be corrupted by heavy noise due to spatial variations in material permeability. To remove the random noise from the nonstationary time series RFEC signals, empirical mode decomposition (EMD) processing has been adopted and has been found to significantly improve the signal-to-noise (SNR) ratio. Furthermore, the effect of the circumferential extent of defects on the signal amplitude is analyzed through experimental and finite-element-model–based studies. The linear resolution of the designed radial coil detector and its repeatability behavior are also scrutinized. Studies demonstrate that the developed radial field detector is a promising configuration in remote field eddy current technique for enhanced defect detection and also in designing an array probe.

Robotization of the IR-Thermographic technique – impact on the visualisation quality and considerations on the data workflow

Quality control automation is becoming increasingly popular in industrial production lines. Active thermography techniques are well-regarded for their adaptability, providing fast, non-contact, and full-field non-destructive evaluation. Automating thermographic evaluation can increase assessment speed and repeatability without sacrificing inspection accuracy. By using a robot arm to manipulate the thermographic setup, it becomes possible to inspect large components and refine scans on suspicious zones, even in parts with complex geometries. In this study, the performance of a new thermographic inspection platform is compared with a conventional setup to showcase the potential improvements. A plastic curved-shaped sample with artificial flat bottom hole defects is used as a benchmark for the comparison. The advantages and disadvantages of robotizing the infrared non-destructive setup are analyzed, and the impact of the data workflow and future research activities are also discussed.

ON THE EFFECT OF THE DURATION OF THE EMITTED PULSE ON THE DGS-DIAGRAM OF THE NORMAL PROBE

The influence of the pulsed nature of the signal of various durations emitted during ultrasonic monitoring on the DGS-diagram of a normal probe with a round piezoplate using an approximate far field formula for the distance to the probe over three near fields has been studied by computer modeling. For a distance of up to one and a half near fields, a scalar approximation of the integral representation of the pressure on the probe was used when receiving a longitudinal wave reflected from a flat bottom hole for the case of an axis symmetric arrangement of the hole and the probe. In the far field, the influence of the pulsed nature of the signal emitted during ultrasonic monitoring on the DGS-diagram has not been established, and for the distance to the probe of no more than 1,5 near fields, the results of the calculations carried out generally qualitatively confirm the effect of the change in the duration of the emitted pulse on the DGS-diagram, shown in the previously published works of prof. I. N. Ermolov.

High fidelity ultrasonic measurements for defect detection and characterisation

Repeatable measurements with detachable transducers such as phased arrays have much potential for enhancing the sensitivity of nondestructive evaluation (NDE), as baseline subtraction can be employed. This paper aims to develop post-processing methods to overcome the variation between inspections which leads to poor subtraction performance. In this paper, ultrasonic immersion inspections are performed on a brass sample and a steel sample with a corroded backwall. Reference measurements are taken, and then small defects are machined to simulate defect growth in industrial applications. Defect types include two 1 mm diameter flat bottom holes (FBH) extending from 15 mm to 17 mm in the brass sample and two 2 mm diameter holes drilled to 1 mm depth from the corroded surface of the steel sample to represent the growth of localised pits. Although FMC/TFM is used in this paper, the defects have similar response amplitudes to the structural noise, and they cannot be detected or characterised directly from the images by the conventional 6 dB drop method. This paper demonstrates the utilisation of ultrasonic measurements, together with cross-correlation, to obtain inspection variables which include array position and couplant wave velocity. By knowing the variations between inspection conditions, compensation is made in post-processing to improve image repeatability, and hence the performance of baseline subtraction. The SNR is improved by approximately 10–25 dB depending on how great the actual variations are. It is shown that these previously undetectable defects can be seen and accurately sized after conducting baseline subtraction with compensation.

Non-linear frequency modulated thermal wave imaging for subsurface analysis

Thermographic analysis for subsurface non-destructive evaluation often encounters challenges in achieving better depth scanning and enhanced depth resolution. Usually, deeper depth analysis will be facilitated by the strength of the low-frequency components and the depth resolution on the frequency sweep of the stimulation, along with material properties in this active thermographic modality. Non-linear frequency modulation became prominent for subsurface analysis due to its ability to provide a band of frequencies with increased energy at low frequencies. This manuscript introduced novel Logarithmic frequency modulated stimulation for subsurface analysis and compared it with its well-established linear frequency counterpart. A quantitative analysis was performed using detectability and sizing of the defects. Experimental results demonstrate that the proposed modality offered an edge over the state of the art, as shown by the experimentation carried out over a carbon fibre-reinforced plastic specimen with embedded flat bottom holes.