Frequency Modulated Thermal Wave Imaging Research Articles

Application of Digitised Frequency Modulated Thermal Wave Imaging for Testing and Evaluation of Rebar Corrosion in Concrete Structures: A Numerical Study

Application of Digitised Frequency Modulated Thermal Wave Imaging for Testing and Evaluation of Rebar Corrosion in Concrete Structures: A Numerical Study

Novel Insights on Spatio-Temporal Analysis for Frequency Modulated Thermal Wave Imaging Using Principal Component Analysis on Glass Fibre Reinforced Polymer Material

Novel Insights on Spatio-Temporal Analysis for Frequency Modulated Thermal Wave Imaging Using Principal Component Analysis on Glass Fibre Reinforced Polymer Material

Detecting meso-damage and subsurface cracks in a hard rock using frequency-modulated thermal wave imaging (FMTWI)

Mines, tunnels, and hillside roadways that are subjected to high levels of stress are prone to massive and violent occurrences of rock failures. It results in a multitude of irreversible consequences, including the loss of human lives. Nevertheless, preceding rock failures, the development of micro and macrocracks, which are sometimes not discernible on the surface, takes place. Subsurface cracks indicate the degradation of rock and can be employed as a means to anticipate occurrences of rock failures and bursts. Therefore, the utilization of subsurface imaging techniques for rocks facilitates the estimation of the true strength of the rock mass. Nevertheless, in many instances, rock masses are not easily reachable, posing difficulties for standard techniques such as ground-penetrating radar or computed tomography (CT) scan imaging, to identify the cracks. Hence, this research endeavours to explore the feasibility of employing frequency-modulated thermal wave imaging (FMTWI) for identifying subsurface cracks and their coalescence in hard rocks through the utilization of numerical simulation and experimental methods. A model was constructed using the finite element method wherein artificial cracks were intentionally introduced into a cylindrical granite specimen based on the CT scan data acquired during the meso-damage analysis. The thermograms obtained were subjected to pre-processing and post-processing techniques, and afterwards compared with the CT scan images. The FMTWI tests were conducted in the laboratory to calibrate and validate the simulation results. The findings derived from the analyses of temperature profiles and thermograms indicate that this particular technology is a promising one and offers several advantages in comparison to alternative methods for detecting micro- or macrocracks in deep mines and tunnels.

Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers

Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers

Automatic defect detection in a steel sample using frequency-modulated thermal wave imaging

Non-stationary thermal wave imaging (NSTWI) techniques are primarily used to assess material properties and structural integrity without damaging a structure. Frequency-modulated thermal wave imaging (FMTWI) is a well-known NSTWI approach that uses a low-peak power heat source to examine structures in a reasonable experimentation time. Recently, various methods, such as pulse compression, Fourier transform, principal component analysis (PCA) and independent component analysis (ICA), have been introduced to handle the non-linearity of transient thermal signatures. However, handling non-linearity and developing a fully automatic defect detection system remains very challenging due to certain limitations of the aforementioned methods. To overcome these problems, this paper proposes an artificial neural network (ANN) for the identification of subsurface flaws in a mild steel sample investigated using the FMTWI approach. The accuracy and the performance of the proposed neural network (NN) are evaluated through a confusion matrix and region of convergence (ROC) analysis for the classification of defective and healthy pixels in an infrared image sequence. The developed NN model has achieved 99.7% accuracy in classifying the pixels correctly.

An Analytical Approach for Pulse Compression Favorable Digitized Frequency Modulated Thermal Wave Imaging Technique for the Quantitative Estimation of Breast Cancer

An Analytical Approach for Pulse Compression Favorable Digitized Frequency Modulated Thermal Wave Imaging Technique for the Quantitative Estimation of Breast Cancer

Statistical Analysis of Defect Dection in Glass Fiber Reinforced Polymers Using Frequency Modulated Thermal Wave Imaging

Statistical Analysis of Defect Dection in Glass Fiber Reinforced Polymers Using Frequency Modulated Thermal Wave Imaging

Convolution Neural Networks Based Automatic Subsurface Anomaly Detection and Characterization in Quadratic Frequency Modulated Thermal Wave Imaging

Convolution Neural Networks Based Automatic Subsurface Anomaly Detection and Characterization in Quadratic Frequency Modulated Thermal Wave Imaging

Novel pulse compression favorable excitation schemes for infrared non-destructive testing and evaluation of glass fibre reinforced polymer materials

Infrared thermography (IRT) has been extensively used in the field of non-destructive testing and evaluation (NDT&E) as a condition monitoring tool in commercial as well as industrial applications. It is a fast, non-contact, whole field and quantitative technique for NDT&E applications by mapping the thermal profile over the test sample. This paper presents a novel three-dimensional analytical model for characterization of glass fibre reinforced polymer (GFRP) sample having flat bottom hole defects. The capability of the proposed method is highlighted with recently introduced pulse compression favorable thermal wave imaging modalities. Further, the analytical results have been validated with simulated and experimental studies on the proposed frequency modulated thermal wave imaging (FMTWI), Barker coded thermal wave imaging (BCTWI) and digitized frequency modulated thermal wave imaging (DFMTWI) techniques to analyze the performance of the defect detectability by taking normalized correlation coefficient as a figure of merit. Obtained results clearly shows the capabilities of the DFMTWI over the FMTWI and BCTWI using correlation based matched filter post-processing approach.

InfraRed image correlation for non-destructive testing and evaluation of delaminations in glass fibre reinforced polymer materials

InfraRed Thermography (IRT) is one of the widely used Non-destructive Testing and Evaluation (NDT&E) method for characterization of fiber reinforced polymers. Among verity of testing methodologies and associated post processing schemes recently proposed pulse compression favorable thermal wave imaging methodologies gain importance due to their merits. This present paper highlights a highly depth resolved Frequency Modulated Thermal Wave Imaging (FMTWI) and its digitized version named as Digitized Frequency Modulated Thermal Wave Imaging (DFMTWI) for identification of subsurface Teflon patches introduced as defects to simulate the delaminations at various layers of GFRP test specimen of various sizes and depths.

Pulse compression favorable frequency modulated thermal wave imaging for non-destructive testing and evaluation: an analytical study

InfraRed Thermography (IRT) is one of the non-destructive testing and evaluation (NDT&E) approach widely used for testing and evaluation of wide verity of materials such as metals, semiconductors and composites. Among the widely used Thermal NDT&E (TNDT&E) approaches for better depth resolution and sensitivity for detection of defects located at different depths inside the test specimen recently proposed correlation based approach gained importance due to its enhanced defect detection capabilities. The present paper introduces a novel one-dimensional analytical solution for the frequency modulated excitation scheme under adiabatic boundary conditions for detection of flat bottom holes as defects in a mild steel sample. The performance of the Pulse Thermography (PT), Lock-in Thermography (LT) and Frequency Modulated Thermal Wave Imaging (FMTWI) methods are highlighted their defect detection capabilities have been compared by adopting the recently introduced correlation based post-processing approach. Finally, the proposed analytical method has been validated with the results obtained from the commercially available finite element based software.

Pulse Compression Favorable Thermal Wave Imaging Techniques for Non-Destructive Testing and Evaluation of Materials

This article highlights the application of pulse compression favorable Frequency Modulated Thermal Wave Imaging (FMTWI) technique for testing and evaluation of Carbon Fibre Reinforced Plastics (CFRP). The concept of InfraRed Image Correlation (IRIC) has been introduced to reconstruct the correlation fringes for the pulse compressed data obtained from the matched filter approach to detect the flat bottom hole defects in a CFRP specimen. Further, comparison has been made with the acquired phasegrams using frequency and time domain based data processing approaches. Obtained results clearly demonstrate the strengths of the proposed IRIC based post-processing scheme in providing the spatio-temporal correlation fringes for thermal Non-Destructive Testing & Evaluation (NDT&E) of CFRP materials.

Deep and Handcrafted Feature Fusion for Automatic Defect Detection in Quadratic Frequency Modulated Thermal Wave Imaging

Deep and Handcrafted Feature Fusion for Automatic Defect Detection in Quadratic Frequency Modulated Thermal Wave Imaging

Probability of Detecting the Deep Defects in Steel Sample Using Frequency Modulated Independent Component Thermography

Active thermography is a widely used non-destructive testing and evaluation technique (NDT&E) for evaluating the properties of materials without impairing its future usefulness. In this work, a mild steel sample made of artificial flat-bottom holes at varied depths, was examined with the emerging non-stationary thermal wave imaging (NSTWI) technique, i.e. frequency modulated thermal wave imaging (FMTWI). The pulse compression favorable of NSTWI technique is eminent for compressing the applied thermal energy into a narrow-compressed pulseto enhance the depth resolution and sensitivity. In this work, pulse compressed thermographic data generated from FMTWI experimentation is analyzed with the unsupervised learning approach independent component analysis (ICA) to test their mutual return in the detection of the deep defects in a mild steel sample and this proposed technique was referred to as frequency modulated independent component thermography (FMICT). In comparison, the effect of FMICT was contrasted with othermethodsi.e. pulse compression of time domain and ICA of feature space by considering the signal-to-noise ratio (SNR) as a figure of merit. Furthermore, a probability of detection (POD) analysis framework based on the minimum threshold SNR criteria for apparent visibility of the defects has been presented to assess the probability of identifying defects at various depths using such approaches. The influence of the SNR threshold value for the above strategies on the POD curves has also been presented.

Frequency Modulated Thermal Wave Imaging for Infrared Non-destructive Testing of Mild Steel

Frequency Modulated Thermal Wave Imaging for Infrared Non-destructive Testing of Mild Steel

An analytical approach for frequency modulated thermal wave imaging for testing and evaluation of glass fiber reinforced polymers

Frequency modulated thermal wave imaging (FMTWI) has been considered as one of the promising non-destructive testing and evaluation approach due to its merits such as economical, safe, fast, sensitive and high depth resolvability. The present work provides a novel analytical solution for FMTWI using one-dimensional heat conduction equation with adiabatic (Neumann) boundary conditions. The temperature gradient over the glass fiber reinforced polymer specimen has been analyzed and validated with a commercially available three dimensional mathematical finite element model to retrieve the quantitative information regarding the subsurface defects. The efficiency of the proposed method is highlighted using matched filter based approach for a frequency modulated imposed heat flux. The depth resolvability of the proposed method has been studied from the obtained correlation lag and the time domain phase obtained for FMTWI technique.

Defect Detection Based on Segmentation of Thermographic Images of Frequency Modulated Thermal Wave Imaging

To improve the usefulness of the data, the raw images acquired during non-destructive testing should be processed by image processing techniques. In this paper, by Frequency Modulated Thermal Wave Imaging, we use the image fusion technique to boost the detection capability of defects in a GFRP sample with 25 squared Teflon inserts of different sizes positioned at various depths. In applications such as detection, image segmentation is useful where it is difficult to process the entire image at a time. In this paper, Adaptive Thresholding Image segmentation is used to classify the delamination in Thermographic Images of Infrared Non-Destructive Research on images captured at two different times. Image fusion is later applied to segmented images. Image fusion is used to merge two or more pictures of a different focus and to provide the best picture quality. Fusion is carried out using the Basic Averaging Method here. Using Relative Foreground Area error, the performance of the proposed method is quantitatively assessed. The region and shape of an object are important parameters in the case of Non-Destructive Evaluation. Such parameters are contrasted with current methods of segmentation.

Automatic Defect Detection and Depth Visualization in Mild Steel Sample Using Quadratic Frequency Modulated Thermal Wave Imaging

Deeper defect detection and depth resolution capabilities of quadratic frequency-modulated optical stimulus became a viable approach for material inspection in active infrared non-destructive testing modality. But the limitations of complex and non-linear analytical models associated with processing techniques propel towards automated defect assessment techniques in infrared thermography. This paper introduces a deep neural network-based automatic defect detection and depth visualization technique in quadratic frequency modulated thermal wave imaging. The neural network classifier uses the modified loss function of a one-class support vector machine to classify defects. The regression network estimates the depth of classified defects. A mild steel specimen with artificial delaminations is numerically modeled and excited by a quadratic frequency-modulated heat flux. The proposed network classification and regression performances are qualitatively assessed using testing time, accuracy, and mean squared error as a figure of merits.

Detection of slags in structural steel sample using InfraRed thermal wave imaging

The estimation of defects in any material is a critical problem in the domain of InfraRed thermography (IRT). The paper presents simulation aspects of three-dimensional heat transfer model for a finite thickness structural steel sample having six different slag inclusions using a low peak power frequency modulated thermal wave imaging (FMTWI) and its digital counterpart digitized frequency modulated thermal wave imaging (DFMTWI) technique. The sample surface is subjected to proposed thermal heat flux and defect analysis was carried out by studying the thermal gradient over the test sample surface. Further, time domain pulse compression approach allowing better defect depth analysis has been discussed to evaluate the correlation coefficients and time delays for the proposed approaches. Results show better performance of the DFMTWI approach with high correlation coefficient values for all the defects with low time delays than the FMTWI approach.

Probability of defect detection in glass fibre reinforced polymers using pulse compression favourable frequency modulated thermal wave imaging

Infrared Non-Destructive Evaluation (IRNDE) is an emerging approach among the Non-Destructive Testing (NDT) techniques to evaluate sub-surface defects, due to its non-contact, whole field, fast and quantitative defect detection abilities. Among various recently proposed aperiodic Thermal Non-destructive Testing and Evaluation (TNDT&E) methods, pulse compression favourable thermal wave imaging approaches gained significant importance due to their improved defect detection capability in terms of sensitivity and resolution. The present work attempts to explore the applicability of pulse-compression favourable Frequency Modulated Thermal Wave Imaging (FMTWI) approach for testing and evaluation of Glass Fibre Reinforced Polymer (GFRP) sample by considering the Peak to Side Lobe Ratio (PSLR) as a figure of merit. Results clearly depict that in pulse compression favourable FMTWI exhibits higher probability for detection of sub-surface defects of higher aspect ratio, with the subtraction of background temporal temperature distribution of the test sample in comparison with the presence of background.