Autoencoder with Constraints for Subsurface Defect Detection in Quadratic Frequency Modulated Thermal Wave Imaging

Image processing techniques have become highly essential to improve the data usefulness of the raw images obtained for infrared non-destructive evaluation (NDE). The identification of defective regions from a thermogram has been a common problem in the field of NDE. Image segmentation is one of the promising approaches to identify defective regions from thermogram. In the present work, segmentation approach based on clustering is employed. In this image patterns are organized into clusters or groups considering the relationship among these. The method involves finding the centroids of the regions and formation of clusters around each centroid. Thresholding is applied to improve accuracy in formation of clusters. Double thresholding method is adapted to retrieve the shape of defect and to improve the possible diagnosis capabilities of system for NDE applications. The proposed method is investigated with frequency modulated thermal wave imaging (FMTWI) over glass fiber reinforced polymers (GFRP). The GFRP sample consists of square shaped Teflon inserts of different dimensions placed at various depths. A comparative study of the proposed novel segmentation scheme and existing segmentation methods is conducted.

ABSTRACTThe active thermal non-destructive testing and evaluation techniques have gained wide acceptance in health monitoring of various solids materials such as metals, composites and semiconductors. The most popular active infrared thermographic methods for non-destructive testing and evaluation applications are pulse based thermography and lock-in thermography techniques. But the usage of these techniques is limited due to their inherent limitations. To overcome the limitations of the existing approaches, the present work demonstrates the effectiveness of digitized frequency modulated thermal wave imaging technique for detection of different shaped defects in a glass fibre reinforced polymer specimen. Further, the usage of post-processing scheme facilitates defect detection with high resolution and also preserves the shape of defects.

ABSTRACTIn this article, Frequency Modulated Thermal Wave Imaging (FMTWI) [1–6] is introduced for the first time for determining power distribution of electromagnetic waves on plane surfaces. The advantage with this technique is that we can extract multiple amplitude and phase images from a single run of experiment. The applied excitation signal in this technique is a frequency modulated chirp signal instead of a single frequency signal used in conventional lock-in infrared (IR) thermography [7–11].The thermal images obtained using FMTWI can be used qualitatively, e.g., to detect field leakage near electromagnetic junctions and microstrip feed lines. As a practical demonstration of this technique, an example of 2 × 2 patch antenna array at 8 GHz is considered. First, amplitude images at various modulation frequencies are obtained. Next, signal to noise ratio (SNR) values at each frequency are calculated. It is seen that SNR is lower at higher frequencies. It is observed that at higher modulation frequencies, micro-strip lines feeding the individual patch antennas of the array, are not visible in amplitude images, while at lower frequencies they are clearly visibleMathematical modeling of the microwave absorption screen has also been carried out to show variations of incident, reflected, and transmitted powers as a function of screen surface impedance. It is also observed that the screen minimally perturbs the electromagnetic fields.

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.

Detecting Delaminations in CFRP Using Quadratic Frequency Modulated Thermal Wave Imaging

Frequency modulated infrared imaging for thermal characterization of nanomaterials

Capturing the spatio temporal radiation in the infrared portion of the spectrum from any object renders the temperature evolution of it and culminates in presenting the information about the hidden subsurface anomalies within the object. However deeper depth scanning and higher depth resolution with enhanced subsurface visualization are the significant challenges generally encountered in these studies that demands various stimulation and processing mechanism to explore these details. This manuscript introduces a novel log frequency modulation-based stimulation along with various post-processing approaches that caters to these requirements. This modality facilitates a band of low frequencies with increased energy in the stimulus for deeper depth scanning and spectral processing approaches to provide enhanced depth resolution in a single experimentation cycle. The hypothesis is validated through the experimentation carried out over a carbon fiber-reinforced plastic specimen with embedded flat bottom holes. A qualitative comparison between various signal processing approaches using thermographic metrics like the sizing of the defects and signal-to-noise ratio recommends the superiority of proposed stimulation and processing techniques for enhanced defect detection.

This chapter highlights the compressive Sensing in Quadrature Frequency Modulated Thermal Wave Imaging. Active infrared thermography is a widely used technique in the field of non-destructive. testing. The subsurface defects produce non-uniform heat dissipation and this phenomenon is captured by an infrared camera. At a frequency much higher than the necessary nyquist rate, an IR camera records the thermal response from test material. This is a waste of resources like power, memory, and sensors. In this study, the thermal response from test samples of aluminium and bone is captured using compressed sensing (CS) at a lower frame rate, and the full response is then recovered using the CS recovery algorithms Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP). The capturing rate is much larger than the nyquist rate as suggested by the thermal stimulus. This leads to wastage of resources and produces a sparse data. Compressive sensing (CS) is a new signal acquisition procedure in which a sparse signal is acquired with far fewer measurements than the dimension of the signal. Utilising a correlation-based pulse compression approach, the recovered thermal response is processed to carry out subsurface analysis and find any defects. To verify the effectiveness of the recovery algorithms comparison is done between original and recovered data and the results are promising. It can be concluded that by applying CS in thermography there is huge scope for conserving sensors, power and memory.

An objective comparison of three different thermal non-destructive evaluation (NDE) techniques - pulsed thermography (PT), lock-in (LI) thermography, and frequency modulated thermal wave imaging (FMTWI), has been carried out on a CFRP sample. The matched energy comparison shows that on the basis of computed SNR, the shallow defects are better detected by PT, while deeper defects are detected equally by all techniques.

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

Thermal diffusivity of anodic alumina (AAO) templated bismuth telluride nanowires has been measured using recently proposed frequency modulated thermal wave imaging (FMTWI). The technique provides a fast and efficient non-contact approach for in-plane thermal characterization of nanomaterials. An intensity modulated up-chirp signal is applied as photothermal excitation and the thermal response is monitored using an infrared (IR) thermography based temperature sensing system. Thermal diffusivity of the sample is experimentally assessed using the multiple phase information extracted from a single run of the experiment. This feature considerably reduces the operational time of the experiment as compared to similar lock-in thermography based approaches. This unique approach of solely using the phase information for thermal diffusivity measurements, allows the experiment to be more immune to the local variations in surface temperature and emissivity of the radiating surface. The experimental details of the technique are discussed, with practical measurement of thermal diffusivity of Bi₂Te₃/AAO nanocomposite in direction perpendicular to the nanochannel axis.

In the field of thermal non-destructive testing and evaluation (TNDT&E), active thermography gained popularity due to its fast wide-area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression-favorable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, an experimental investigation has been carried out on a hardened steel sample used in the ship building industry with a flat-bottom-hole-simulated defect using the frequency-modulated thermal wave imaging (FMTWI) technique. The defect detection capabilities of FMTWI have been investigated from various statistical post-processing approaches and compared by taking the signal-to-noise ratio (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, pulse compression has been carried out using different methods, namely the offset removal with polynomial curve fitting and principal component analysis (PCA), which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimens used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.

Research on thermal wave processing of lock-in thermography based on analyzing image sequences for NDT

Carbon fiber reinforced polymer (CFRP) has become an essential structural material in aerospace, new energy, rail transportation, and other fields due to its lightweight and high-strength properties. However, its complex manufacturing processes and demanding service environments can lead to various types of damage on both the surface and inside the material, significantly compromising safety. Existing detection techniques often adopt a step-by-step inspection mode, which results in complex processes and makes it difficult to achieve cross-dimensional correlation analysis of defects, leading to blind spots in structural health assessment. To address these issues, this paper proposes a method for simultaneous detection of surface and subsurface defects in CFRP based on infrared polarization imaging. First, we developed an infrared polarization imaging-based defect detection system and collected a CFRP defect dataset of samples containing various surface and subsurface defects. Then, we proposed a surface and subsurface defect detection network, which integrates extracted thermal diffusion features and polarization features to decouple and enhance surface and subsurface features. Through the constraints of multi-task defect detection, simultaneous detection of surface and subsurface defects is achieved. The experiments on the collected CFRP defect dataset demonstrate the effectiveness of the proposed method.

ABSTRACTStudies on design optimisation of eddy current probe have been carried out for detection of deep sub-surface defects in thick stainless steel plates. The performance of send–receive type air core, ferrite core, ferrite core with outer shielding, and cup-core probes has been analysed and compared using finite element modelling. The model predicted results have indicated that the cup-core probe ensures better sensitivity for detection of deep sub-surface defects. For experimental studies, a high sensitive eddy current instrument with digital lock-in amplifier functionality implemented in LabVIEW software has been developed. Experimental results have confirmed that cup-core probe is capable of detecting defects located at 8.0 mm below the surface in 12.0 mm thick stainless steel plates as well as simulated sub-surface localised corrosions. A linear relationship between phase angle and defect depth below the surface has been observed, apart from discrimination of sub-surface and surface defects.