Pulsed Thermography Research Articles

Robust Principal Component Thermography for Defect Detection in Composites.

Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.

Characterizing Depth of Defects with Low Size/Depth Aspect Ratio and Low Thermal Reflection by Using Pulsed IR Thermography.

This study is focused on the quantitative estimation of defect depth by applying pulsed thermal nondestructive testing. The majority of known defect characterization techniques are based on 1D heat conduction solutions, thus being inappropriate for evaluating defects with low aspect ratios. A novel method for estimating defect depth is proposed by taking into account the phenomenon of 3D heat diffusion, finite lateral size of defects and the thermal reflection coefficient at the boundary between a host material and defects. The method is based on the combination of a known analytical model and a non-linear fitting (NLF) procedure. The algorithm was verified both numerically and experimentally on 3D-printed polylactic acid plastic samples. The accuracy of depth prediction using the proposed method was compared with the reference characterization technique based on thermographic signal reconstruction to demonstrate the efficiency of the proposed NLF method.

Thermographic and reflectographic imaging investigations on Baroque paintings preserved at the Chigi Palace in Ariccia

In this work, two different mid-infrared imaging techniques operating in the 3-5 µm spectral range are applied to the study of three paintings on canvas, dating back to the XVII century, preserved at the Chigi Palace in Ariccia (Italy). A combined approach based on the use of pulsed thermography and mid-infrared reflectography is proposed for the analysis of the ‘Primavera’ by Filippo Lauri and Mario Nuzzi, the ‘Ritratto di Mario Nuzzi che dipinge un vaso di fiori’ by Giovanni Maria Morandi and Mario Nuzzi and the ‘Ebbrezza di Noè’ by Andrea Sacchi. The aim is to show how the integrated use of these techniques enables a depth-resolved investigation of the entire layered structure of the paintings, from the surface up to the canvas support. The complementarity of the presented results allows an evaluation of the conservative status of the support and the detection of graphical and pictorial features hidden beneath the surface layer, such as pentimenti, as these features are important for the historical and artistic characterisation of the artefact.

Accurate depth determination of defects in composite materials using pulsed thermography

The determination of defect depth is one of the important advantages of pulsed thermography. The existing methods for depth estimation in pulsed thermography are generally established based on the one-dimension heat conduction along the thickness of the sample. However, most of the methods neglect the effects of defect size and sample thickness, which induces additional systematic errors in depth estimation. In this study, simulations are conducted to demonstrate that the accuracy of depth determination of defects is greatly influenced by the defect size and sample thickness in some simulations. By establishing the relationship between the characteristic time and these factors, an accurate depth determination method based on the peak slope time is proposed. The proposed method has been applied to characterize the defect depths of a composite panel with flat-bottomed holes. The experimental results show that the use of the proposed method is able to identify the depths of defects with high accuracy.

Quantification of pitting corrosion from thermography using deep neural networks.

Pipelines serve as the major infrastructure for transmission and distribution in oil and gas and water industries. The health condition of a metallic pipe is determined mainly by the extent to which it is corroded. Thus, the quantification of pitting corrosion in terms of metal loss is required for the understanding of pipe condition. There are different ways to quantify corrosion pit geometry. Direct methods measure the pit depth of pipe samples, which are sand/grit blasted to remove corrosion products, and are often adopted in the laboratory. Indirect methods employ non-destructive inspection techniques to detect and quantify the corrosion without sandblasting, which is preferred for a field test. In this study, pulsed thermography is considered for the quantification of pitting corrosion in a metallic pipe. Thermography testing can generate a sequence of infrared images, which reflect the diffusion process of heat through the pipe wall. This paper proposes a new deep neural network-based approach to quantify the pitting corrosion damages from the acquired thermography images. Through extensive experimental tests, the quantified results demonstrate a reasonably good linear relationship with metal loss of pipe, as the quantification accuracy was better than 98%.

Detection of internal defects in CFRP strengthened steel structures using eddy current pulsed thermography

This study investigates the effectiveness of eddy current pulsed thermography (ECPT) to detect internal defects in CFRP strengthened steel structures. Various common internal defects of CFRP strengthened steel structures are involved in this study, including delaminations, low-velocity impact damages in CFRP, cracks in steel, and debonding in the CFRP/steel interface. We theoretically analyzed the influence of these defects on the electromagnetic and thermal response of the structures. In order to verify the theoretical results, numerical and experimental investigations are performed on CFRP strengthened steel plates with various kinds of defects. Numerical and experimental results demonstrate that the investigated defects can be inspected by ECPT and indicated as low-temperature regions in the ECPT thermograms. Besides temperature features of thermograms, temperature profile evolutions and temperature evolutions are investigated to further analyze the thermal response on the CFRP surface. Moreover, the efficacy of ECPT in retrieving the shape and size of defects is demonstrated based on the experimental results. In general, theoretical, numerical and experimental results demonstrate that ECPT is effective in inspecting internal defects in CFRP strengthened steel structures, which indicates that ECPT provides a promising way for such a complicated hybrid structure.

Non-destructive evaluation of resistance projection welded joints (RPW) by flash thermography

Pulsed thermography is a well-known thermographic method used as nondestructive control for detecting defects and evaluating the quality of components. One of the most challenging application is the development of thermographic non-destructive methods on thin welds for quality control, in order to overcome the limits of commons non-destructive methods such as ultrasound tests (UT). In the present work, pulsed thermography in reflection mode with flash lamps excitation, post-processed by pulsed phase thermography (PPT), has been used on resistance projection welded joints (RPW). A set of RPW specimens has been produced according to a factorial plan and a procedure to identify welding parameters by analysing the phase profile has been developed. The results have been analysed by using the ANOVA and the features extracted have been verified.

Capability of active thermography to detect and localize pores in Metal Additive Manufacturing materials

Active thermography is a fast, contactless and non-destructive technique that can be used to detect internal defects in different types of material. Volumetric irregularities such as the presence of pores in materials produced by the Additive Manufacturing processes can strongly affect the thermophysical and the mechanical properties of the final component. In this work, an experimental investigation aimed at detecting different pores in a sample made of stainless AISI 316L produced by Laser Powder Bed Fusion (L-PBF) was carried out using pulsed thermography in reflection mode. The capability of the technique and the adopted setups in terms of geometrical and thermal resolution, acquisition frequency and energy density of the heating source were assessed to discern two contiguous pores as well as to detect a single pore. Moreover, a quantitative indication about the minimum resolvable pore size among the available and analysed defects was provided. A powerful tool to assess the limits and the opportunities of the pulsed technique in terms of detectability and localizability was provided by comparing active thermography results to Computed Tomography as well as a related Finite Element Analysis (FEA) to simulate the pulsed heating transfer with Comsol.

Design and investigation of a two-coil magnetic core inductor used for Eddy current pulsed thermography

Eddy current pulse thermography (ECPT) is an active thermography technology in the nondestructive testing field. The inductor of ECPT is an important component that influences detection quality and efficiency. In this paper, a two-coil magnetic core inductor is proposed for ECPT, which characteristics are investigated by numerical simulation methods. This new structure enlarges the detectable area for ECPT and increases the view field for infrared cameras. Research results show that the uniformity of temperature distribution is improved significantly. At the same time, the geometric parameters and working parameters of the proposed inductor, including the coil turns, the coil height, the leg length of the magnetic core, the lift-off distance, the working frequency, are researched.

Application of the Pulse Infrared Thermography Method for Nondestructive Evaluation of Composite Aircraft Adhesive Joints.

In this article, we examine the possibility of using active infrared thermography as a nontraditional, nondestructive evaluation method (NDE) for the testing of adhesive joints. Attention was focused on the load-bearing wing structure and related structural joints, specifically the adhesive joints of the wing spar caps and the skins on the wing demonstrator of a small sport aircraft made mainly of a carbon composite. The Pulse Thermography (PT) method, using flash lamps for optical excitation, was tested. The Modified Differential Absolute Contrast (MDAC) method was used to process the measured data to reduce the effect of the heat source’s inhomogeneity and surface emissivity. This method demonstrated a very high ability to detect defects in the adhesive joints. The achieved results are easy to interpret and use for both qualitative and quantitative evaluation of the adhesive joints of thin composite parts.

Blind image separation for the debonding defects recognition of the solid propellant rocket motor cladding layer using pulse thermography

Blind image separation based on wavelet transform (WT) enhancement algorithm for the pulse thermography was introduced as a novel modality to recognize the debonding defects of the solid propellant rocket motor cladding layer. Experimental research on the pulse thermography detection of the cladding layer debonding defects was carried out. The main noise sources and the characteristics associated with pulse thermography were analyzed. Subsequently, the traditional image enhancement algorithm and WT enhancement algorithm were compared and investigated. The influence of wavelet parameters on the enhancement of noise reduction ability of WT blind separation is studied. The half-width method is used to detect the defect size, and the results showed that the debonding defects of the solid propellant rocket motor cladding layer can be recognized with high precision by blind image separation based on WT enhancement algorithm.

A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography.

Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP) laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weldability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.

Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks

Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.

Detection of Fatigue Microcrack Using Eddy Current Pulsed Thermography

Microcracks are a common metallic defect, resulting in degradation of material properties. In this paper, specimens with different fatigue microcracks were detected by eddy current pulsed thermography (ECPT). Signal processing algorithms were investigated to improve the detectability and sensitivity; principal component analysis (PCA) and Tucker decomposition were used to compare the performance of microcrack detection. It was found that both algorithms were highly adaptable. A thermal quotient was used to assess the temperature variation trend. Furthermore, the potential correspondence between crack closure and temperature change was investigated.

Enhancement of Defect Detectability in Pneumatic Pressure Equipment Using an Automatic Detection Technique in ECPT

Abstract Pneumatic pressure equipment is an important component of wind tunnel test systems, which have been extensively applied in most modern industries. To guarantee the reliability and safety of pneumatic pressure equipment, a regular inspection is needed. To improve the inspections process, automating the process of identifying defects continues to receive attention in the research community. In this paper, in order to achieve automatic defect identification, an improved feature extraction algorithm in eddy current pulsed thermography is presented. The presented feature extraction algorithm contains four elements: data block selection, variable step search, relation value classification, and between-class distance decision function. The data block selection and variable step search are integrated to decrease the redundant computations in the automatic defect identification. The goal of the classification and between-class distance calculation is to select the typical features of a thermographic sequence. The main image information can be extracted by the method precisely and efficiently. Experimental results are provided to demonstrate the capabilities and benefits (i.e., increased precision, reduced processing time) of the proposed algorithm in automatic defect identification.

Moran’s Index-Based Tensor Decomposition for Eddy Current Pulsed Thermography Sequence Processing

Eddy current pulsed thermography (ECPT) is widely used in the nondestructive testing (NDT) of metal surface defects. Because defect information is sometimes affected by the edge effect, it is necessary to segment the ECPT image sequence, that is, separate the defected part from the background, to improve the detection effect. Tensor robust principal component analysis has been widely used in many image segmentation fields, such as medical imaging, face recognition, and so on, which also includes NDT, but it faces the problems of high time cost, overfitting, and insufficient physical interpretability in industrial applications. In this article, a new variable, global Moran’s index, is introduced to tensor decomposition for ECPT image sequences processing since the defect signal always gathers around the defects and shows spatial cohesion. The proposed method, named Moran’s index-based tensor decomposition (MITD), can significantly reduce the iterations, by up to 97%, and remove the influence of background noise. To demonstrate the performance of MITD, several experiments are carried out on three artificial samples and three natural samples from a nuclear power plant. Furthermore, a detailed comparison is drawn between MITD and other existing methods. The experimental results show that MITD not only reduces the time cost but also improves the image contrast.

Bending residual strength of damaged Al foam sandwiches through UT and IRT analyses

In the present work, the effect of low-speed impact on the static residual strength of aluminum foam sandwiches having GFRP skins has been evaluated. Two different configurations have been considered, which differs both for thickness and layup used for the manufacturing of GFRP skins. The detection and the quantification of the damage induced by impact have exploited the potentiality of pulsed thermography and phased array ultrasonic techniques, which gives the possibility to provide synergistic and complementary information. Mechanical strength has been evaluated through three-point bending tests carried out on undamaged and damaged specimens. Static tests allowed also evaluating the failure mode of panels and the changes induced by the presence of a low-speed impact zone.

Carbon Nanotubes Dispersion Assessment in Nanocomposites by Means of a Pulsed Thermographic Approach.

The extensive production of polymer composites reinforced by carbon nanotube is limited by the absence of non-destructive evaluation (NDE) methods capable of assessing product quality to guarantee compliance with specifications. It is well known that the level of dispersion of carbon nanotubes (CNTs) in the polymer matrix is the parameter that, much more than others, can influence their enhancement capabilities. Here an active Infrared Thermography Non Destructive Testing(IR-NDT) inspection, joined with pulsed phase thermography (PPT), were applied for the first time to epoxy-CNT composites to evaluate the level of dispersion of the nanoparticles. The PPT approach was tested on three groups of epoxy nanocomposite samples with different levels of dispersion of the nanoparticles. The phasegrams obtained with the presented technique clearly show clusters, or bundles, of CNTs when present, so a comparison with the reference sample is not necessary to evaluate the quality of the dispersion. Therefore, the new NDE approach can be applied to verify that the expected dispersion levels are met in products made from epoxy and Multi-Walled Carbon Nanotubes (MWCNTs). The mechanisms underlying the effects of the dispersion of carbon nanotube on the thermal response of polymer composites have been identified.

Automatic Extraction of Material Defect Size by Infrared Image Sequence

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

A differential temperature contour map to characterize the detection limit of pulsed thermography of materials

Pulsed thermography has been widely used in the inspection of composite materials. The detection limit of pulsed thermography of the materials is often a question being concerned by end users. However, the detection limit cannot be described only by a simple parameter, and is affected by many factors associated with the defect, material, excitation, infrared camera and data processing algorithm. The current concept of detection limit defined with the minimum detectable size, the maximum detectable depth or the minimum aspect ratio depends on the specific experimental conditions, and it lacks universality when the extrinsic experimental conditions change. The aim of this paper is to establish a unified standard model of the detection limit which can be applied to various experimental conditions. The solution was to decouple the material’s intrinsic thermal property and extrinsic experimental condition dependencies and then allow them to be described independently. A new expression of the detection limit of the PT was firstly proposed by creating a differential temperature contour map. Taking delaminations in a C/SiC composite as detecting defects, an experiment was conducted to assess the feasibility and the detection limit, and to evaluate the noise level in practice. The influences of defect size and defect depth on the defect informative parameters were analyzed by using the finite element method based on 2D modeling. Eventually, the differential temperature contour map was calculated for the pulsed thermography of delaminations of C/SiC composites. The results show that the differential temperature contour map proposed can strictly describe the detection limit due to its differential temperature invariance property, and it provides a comprehensive tool to consider many experimental parameters including thermal excitation energy intensity, noise level, and SNR threshold by using a criterion equation in practice.