Thermographic Signal Reconstruction Research Articles

Impact of the thermal afterglow effect on infrared thermography data evaluation methods

In this work, the influence of the afterglow effects from a source impulse on commonly used data processing methods of infrared thermography (IRT), such as Pulsed Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR) was investigated. To resolve this issue, we propose the adoption of a modified mathematical model or an empirical function to approximate the flash lamp pulse, which we recorded during the test with a photodiode. Two models were evaluated: one consisting of a combination of exponential and power functions describing the entire pulse and a second, focusing only on the decreasing phase. The models were compared with well-established empirical functions, such as those of Degiovanni and Vageswar, using the widely known statistical scores R2adjusted and Akaike information criterion. The quantitative comparison revealed better fitting results for our proposed models. Additionally, we introduce an algorithm based on Hough transform-clustering for automated determination of the exact pulse length. Statistical analyses were performed to evaluate the reproducibility of the measured pulse shape. The results from applying the first model as pulse correction is compared to another widespread IR data treatment methodology, such as Modified Differential Absolute Contrast (MDAC). We also consider the impact of the afterglow effects on the Source Distribution Image (SDI). Two different specimens were used in the experiments: a homogeneous polymer sheet with blind holes as defects and an in-homogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial defects made from Tetrafluorethylen-Hexafluorpropylen-Copolymer (FEP) foil inserts. In both experiments a quantitative comparison was made using the Tanimoto criterion (TC), indicating an improved sensitivity. This work forms the basis for a faster and more reliable quantitative evaluation of experimental data, in particular algorithms for the automated detection and assessment of manufacturing defects or in-service damage will benefit from these results.

A reliability study on automated defect assessment in optical pulsed thermography

Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples.

Improvement of 3D-Active Thermography by using Artificial Intelligence

For the one-dimensional case of heat propagation in active thermography the thickness of the investigated specimen can be directly reconstructed using known evaluation methods such as laser flash analysis or thermographic signal reconstruction and observation of a characteristic time. Concerning the multidimensional case diffusive effects have a strong impact on the heat propagation of the thermal wave, which leads to misinterpretations when evaluating the thickness especially at deep-lying edges and inhomogeneities in a specimen. The deeper a defect is located in the component, the more diffuse it is perceived in a change in surface temperature. Within this paper, heat flux simulations and real measurements of pulse thermography of different defect geometries are used to train a neural network for depth-resolved defect interpretation. The defect geometries are based on geometries of real impact damages in composites and were realistically obtained from a micromechanical fracture simulation. The neural network is based on an encoder-decoder approach where the temperature values of the cooling curve after a pulse-shaped excitation serve as input information. Segmentation is performed as a function of the backwall geometry. By training several thousand defect geometries using an encoder-decoder network, it was possible for the first time to directly infer the backwall geometry of a component without additional information about the component. Finally, it is shown how simulations can support the inversion of thermal waves for 3D-thermography of real measurements by an artificial intelligence system.

Intelligent Detection Algorithm Based on 2D/3D-UNet for Internal Defects of Carbon Fiber Composites

ABSTRACT Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models.

ADAPTION OF INDUSTRIAL NDT PROTOCOLS BASED ON ACTIVE INFRARED THERMOGRAPHY TO THE ART CONSERVATION WORLD: THE CASE OF THE WALL PAINTING AT HERODIUM

Abstract. Salt weathering is one of the most detrimental agents in the deterioration of historic wall paintings. Unfortunately, it is most noticeable once it reaches the surface, and the damage has already been done. Its elusive nature and its integration into the historic structures make it difficult to accurately detect soluble salts at the sub surfaces of wall painting non-destructively. Detailed detection in a delicate artifact such as wall paintings requires custom methodology. The study's case study is the wall painting in Herodium. The wall paintings were severely damaged by salt weathering. Although great efforts were invested in restoring and protecting the paintings their state is still unstable. This study offers a new perspective on the matter utilizing the adaption of industrial protocols based on active Infrared thermography (IRT) with data processing via thermographic signal reconstruction (TSR). The study's primary goal was to optimize the inspection methodology and assist the conservation process with knowledge of soluble salt hazards hidden at the subsurface.

Towards development of an intelligent failure analysis system based on infrared thermography

Electronic components of which reliability cannot be quantified are unacceptable and potentially hazardous, especially in safety-relevant areas such as driver assistance systems and medical technology where the zero-error principle applies. Reliability as a quality criterion has its origin in production, i.e. process variations have a negative influence on the structural integrity of the contact elements on the packaging and interconnect technology and, thus on the device performance in the field under thermo-mechanical load (temperature changes, vibration, humidity). At present, to ensure reproducibility of the reliability of each component, regular quality tests are often carried out in practice. However, a better and reliable approach will carry out 100 % inline checks for traceability and immediate readjustment. This work is the first step towards developing an intelligent non-destructive inline-capable failure analysis technique using infrared thermography. Good data forms the base on which robust and accurate AI algorithms can be trained and developed. However, the obtained thermographic images need to be processed so that subsurface defects can be detected. In this work, prominent algorithms, namely Pulse Phase Thermography (PPT), Thermographic Signal Reconstruction (TSR), Principal Component Thermography (PCT), Slope and Correlation Coefficient, have been thoroughly discussed and examined on the thermographic sequence from a plexiglass sample. A hybrid algorithm of TSR and PCT has also been suggested with promising results. In the end, potential post-processing algorithms from which the obtained results can be used for training an ML/AI model have been discussed.The major problem associated with the deep learning approach is the lack of data in the training set. The model's performance is dependent on the quality and quantity of the training data. The training data should be a good representation of the real-world scenario, i.e. it should be accurate enough and contain enough images, including exceptions for learning reliable features. However, getting sufficient data is a challenge in the manufacturing industry. However, using the Finite Element Model (FEM) approach for data generation can help overcome the data hurdle. In this paper, we also evaluate the potential application of FEM and the problems one faces when trying to generate a large amount of data for training using FEM.

IRT-GAN: A generative adversarial network with a multi-headed fusion strategy for automated defect detection in composites using infrared thermography

InfraRed Thermography (IRT) is a valuable diagnostic tool to non-destructively detect defects in fiber reinforced polymers. Often, a range of processing techniques are applied, e.g. principal component analysis, Fourier transformation, and thermographic signal reconstruction, in an attempt to enhance the defect detectability. Still, for the actual defect detection and evaluation, the interpretation by an expert operator is required which thus limits the (industrial) application potential of infrared thermography.This study proposes a Generative Adversarial Network (GAN) framework, termed IRT-GAN, to create a single unique thermal-image-to-segmentation translation of defects in composite materials. A large augmented numerical dataset has been simulated for a range of composite materials with different defects in order to train the IRT-GAN model. Integrated with the Spatial Group-wise Enhance layer, the IRT-GAN takes six pre-processed thermal images, thermographic signal reconstruction images in our case, as input and progressively fuses them via a multi-headed fusion strategy in the Generator. As such, this proposed IRT-GAN framework leads to the automated generation of a unique defect segmentation image.The high performance of the IRT-GAN, trained on the virtual dataset, is demonstrated on experimental data of both glass and carbon fiber reinforced polymers with various defect types, sizes, and depths. In addition, it is investigated how early, middle, and late-stage feature fusion in the GAN influences the segmentation performance.

Characterization of Wall-Loss Defects in Curved GFRP Composites Using Pulsed Thermography

Curved glass fiber–reinforced polymer (GFRP) composites are superior to alloy-steel pipes due to their excellent corrosive resistance properties, finding wide applications in the transportation of petrochemicals, chemical storage tanks, and power and water-treatment plants. Among the defects found in GFRP pipes, internal pitting or wall loss is one of the most severe, caused by material deterioration and the friction of small particles in the transfer fluid. This study investigates these in-service discontinuities using a pulsed thermal nondestructive evaluation technique. The paper focuses on the quantification of defect depth using the temperature peak contrast derivative and defect sizing using the full width at half maximum method. Further, the paper investigates the ability of pulsed thermography to estimate pitting or wall-loss defects at various depths and sizes through simulation and experimentation. Thermographic signal reconstruction images are used for quantification of defects at a deeper depth. The results of the present study are then compared with well-established ultrasonic C-scan results.

Three-Dimensional Printed Subsurface Defect Detection by Active Thermography Data-Processing Algorithm.

This article evaluates an active thermography algorithm to detect subsurface defects in materials made by additive manufacturing (AM). It is based on the techniques of thermographic signal reconstruction (TSR), thermal contrast, and the physical principles of heat transfer. The subsurface defects have different infill, depth, and size. The results obtained from this algorithm are compared with state-of-the-art TSR technique and show the high performance of the proposed algorithm even for subsurface defects done by 3D AM. The resulting images are better shown using the absolute difference in the place of variance. The proposed algorithm has higher contrast, better sensitivity to the defect depths, and lower noise than the TSR. The resultant image is quite clean and gives no doubt where the subsurface defects are.

Problems of Active Dynamic Thermography Measurement Standardization in Medicine

Reliability of thermographic diagnostics in medicine is an important practical problem. In the field of static thermography, a great deal of effort has been made to define the conditions for thermographic measurements, which is now the golden standard for such research. In recent years, there are more and more reports on dynamic tests with external stimulation, such as Active Dynamic Thermography, Thermographic Signal Reconstruction or Thermal Tomography. The subject of this report is a discussion of the problems of standardization of dynamic tests, the choice of the method of thermal stimulation and the conditions determining the credibility of such tests in medical diagnostics. Typical methods of thermal stimulation are discussed, problems concerning accuracy and control of resulting distributions of temperature are commented. The best practices to get reliable conditions of measurements are summarized.

A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure

Electronic supply chain vulnerability has been threatened by counterfeits for decades and as a result, the authentication and quality of Electronic Components (ECs) are highly valued by stakeholders for safety-critical industrial systems. Thermography Nondestructive Testing (NDT), offering a rapid inspection while covering a large area within a short time frame, is a promising technique to inspect the integrity of ECs. However, recognising the internal structural layout directly through a packaged EC from thermography images is still a challenge due to its sealed and cramped structure. In this research, we propose a Pulsed Thermography (PT) based data analysis method for enhancement and identification of the layout of die and lead frame in ECs. The proposed solution combines the Sliding Window (SW) concept, Thermographic Signal Reconstruction (TSR) and Principal Component Thermographic (PCT) technique, named SWPCT, to enhance the contrast of die, die substrate and lead frame. Compared to the conventional full-scale PT image analysis, the proposed method presents its superior sensitivity in small thermal features by reducing the spatial thermal information redundancy benefiting from the sliding window PCT analysis. It overcomes the structural thermograms obstruction from the thick packaging, reveals the hidden die and lead frame layout and suppresses the noise simultaneously. By selecting a proper window size, this paper reveals that SWPCT can reconstruct the position and dimension of die and die substrate in ECs, and spatially distinguish the lead frame layout. Two groups of typical OpAmps chip samples with internal structure diversity (X-ray observed) were tested using PT to demonstrate the effectiveness of the proposed method. Simulation data were also used to verify this approach. This research could unlock the challenge of thermography-based inspection for key internal structure in the encapsulated integrated components.

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.

Shape reconstruction of delamination defects using thermographic infrared signals based on an enhanced Canny approach

he Canny shape-reconstruction algorithm is combined with the thermographic signal reconstruction (TSR) technique for the characterisation of delamination defects in planar multilayer structures. The adopted approach consists in fitting the raw thermographic images to a low-degree polynomial in the log–log representation of the time axis, according to TSR technique, and then apply the Canny algorithm to the reconstruction of the original signals. The proposed juxtaposition of the two treatments benefits from the inherent signal de-noising and compression of the TSR algorithm, thus enhancing the performance of the overall reconstruction process. The proposed approach has been applied using synthetic signals obtained via a 3D semi-analytical simulation code and the obtained reconstructed images have been compared with the (known) delamination geometry demonstrating satisfactory results.

Extension of the Thermographic Signal Reconstruction Technique for an Automated Segmentation and Depth Estimation of Subsurface Defects.

With increased use of light-weight materials with low factors of safety, non-destructive testing becomes increasingly important. Thanks to the advancement of infrared camera technology, pulse thermography is a cost efficient way to detect subsurface defects non-destructively. However, currently available evaluation algorithms have either a high computational cost or show poor performance if any geometry other than the most simple kind is surveyed. We present an extension of the thermographic signal reconstruction technique which can automatically segment and image defects from sound areas, while also estimating the defect depth, all with low computational cost. We verified our algorithm using real world measurements and compare our results to standard active thermography algorithms with similar computational complexity. We found that our algorithm can detect defects more accurately, especially when more complex geometries are examined.

Detecting Delaminations in Semitransparent Glass Fiber Composite by Using Pulsed Infrared Thermography

Thanks to its good strength/mass ratio, a glass fibre reinforced plastic (GFRP) composite is a common material widely used in aviation, power production, automotive and other industries. In its turn, active infrared (IR) nondestructive testing (NDT) is a common inspection technique for detecting and characterizing structural defects in GFRP. Materials to be tested are typically subjected to optical heating which is supposed to occur on the material surface. However, GFRP composite is semi-transparent for optical radiation of both visual and IR spectral bands. Correspondingly, the inspection process represents a certain combination of both optical and thermal phenomena. Therefore, the known characterization algorithms based on pure heat diffusion cannot be applied to semi-transparent materials. In this study, the phenomenon of GFRP semi-transparency has been investigated numerically and experimentally in application to thermal NDT. Both Xenon flash tubes and a laser have been used for thermal stimulation of opaque and semi-transparent test objects. It has been shown that the penetration of optical heating radiation into composite reduces detectability of shallower defects, and the signal-to-noise ratio can be enhanced by applying the technique of thermographic signal reconstruction (TSR). In the inspection of the semi-transparent GFRP composite, the most efficient has been the laser heating followed by the TSR data processing. The perspectives of defect characterization of semi-transparent materials by using laser heating are discussed. A neural network has been used as a candidate tool for evaluating defect depth in composite materials, but its training should be performed in identical with testing conditions.

Synthesizing Data of Active Infrared Thermography under Optical and Ultrasonic Stimulation of Products Made of Complex-Shaped CFRP

We propose a combined method for thermal nondestructive testing using optical and ultrasonic stimulation by means of combining individual thermograms obtained at the corresponding time points. The resulting sequences of infrared thermograms provide more efficient identification of defects of various types and can also be processed using well-known algorithms, for example, thermographic signal reconstruction, principal component analysis, etc. We obtained experimental results on a composite CFRP sample imitating aircraft ribs using a robotic manipulator.

Comparing the efficiency of defect depth characterization algorithms in the inspection of CFRP by using one-sided pulsed thermal NDT

The efficiency of eight algorithms of defect depth characterization (pulse phase thermography – PPT, thermographic signal reconstruction by analyzing the first and second derivatives– TSR, early observation – EO, apparent thermal inertia – ATI, thermal quadrupoles - TQ, non-linear fitting - NLF and neural networks – NN) has been comparatively analyzed on both theoretical and experimental IR image sequences obtained in the inspection of CFRP composite. Synthetic noise-free image sequences have been calculated by means of the ThermoCalc-3D software, while experimental results have been obtained by applying a one-sided procedure of pulsed thermal NDT to the inspection of artificial defects in CFRP. A relative error in the evaluation of defect depth has been chosen as a figure of merit. It has been demonstrated that a simple and robust processing technique is the use of the Fourier transform resulting in phase-domain data (PPT). The technique of TSR ensures maximal values of signal-to-noise ratio and is less susceptible to uneven heating and lateral heat diffusion. The calculation of ATI has allowed the characterization of defects at depths up to 1.5 mm, but it is sensitive to uneven heating thus requiring to carefully choose a non-defect area. The EO method, as well as the technique of TQ, have revealed inferior results in defect depth identification because of a noisy character of raw signals. Non-linear fitting is a convenient processing technique allowing simultaneous characterization of some test parameters, such as material thermal properties, defect depth and thickness, etc., but this technique is time-consuming and can hardly be applied to full-format images. In the whole defect depth range, minimal characterization errors have been ensured by the use of the NN that is a promising tool for automated identification of hidden defects.

Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the physical principles of heat transfer. The results obtained from this algorithm are compared to the TSR technique and the raw thermogram obtained by stepped thermography inspection. Experimentally, a short thermal pulse is used and the surface temperature of the sample is monitored over time with an infrared camera. Due to the volume of data, the first step is data compression. Newton’s law of cooling was used to store the normalized temperature data pixel-by-pixel over time and a compression ratio of 99% was obtained. The main contributions of the developed algorithm are: only four parameters for data compression and the concept of change in the direction of the heat flow to delimit the edges of the defects, where the borders are identified with a remarkable accuracy. Some well known image processing technique are also integrated to improve the thermal analysis: edge detection/interface between the sample and the image background; consolidation in a single image by aggregating the indicators referring to the concept of cooling/heating time constant, maximum thermal amplitude and contrast.

Automated non-destructive inspection of Fused Filament Fabrication components using Thermographic Signal Reconstruction

Manufacturers struggle to produce low-cost, robust and intricate components in small batches. Additive processes like Fused Filament Fabrication (FFF) inexpensively generate such complex geometries, but potential defects may limit these components’ viability in critical applications. We present a high-accuracy, high-throughput and low-cost approach to automated non-destructive testing (NDT) for FFF interlayer delamination. This Artificially Intelligent (AI) approach utilizes Flash Thermography (FT) data processed with Thermographic Signal Reconstruction (TSR). A Deep Neural Network (DNN) attains 95.4% per-pixel accuracy when differentiating four delamination severities 5 mm below the surface in PolyLactic Acid (PLA) widgets, and 98.6% accuracy in differentiating acceptable from unacceptable states for the same components. Automation supports time- and cost-efficient inspection for delamination defects in 100% of widgets, supporting FFF's use in critical and lot-size one applications.

Automated defect classification in infrared thermography based on a neural network

This paper reports on the use of a neural network in infrared thermography to classify defects, such as air, oil, and water, which can degrade material performance. A finite element method and experiment were adopted to simulate air, water, and oil ingress. Raw data, and thermographic signal reconstruction coefficients were used to train, and test the two multilayer, feed-forward NN models. Quantitative comparisons showed that the model using coefficients as features performed better than the one using raw data. It was more precise and had better test repeatability. This indicates the model is more generalizable.