Pulsed Phase Thermography Research Articles

One-dimensional N-layer thermal modelling for effective machine learning training data generation for nondestructive testing of composite parts with infrared thermography

The analytical solution of the one-dimensional N-layer thermal model (NLM) was successfully employed to generate training data for a machine learning (ML) based procedure for the nondestructive inspection of carbon fiber reinforced composite parts with infrared thermography (IRT). The main objective was to identify a reliable correlation between the experimental data of a pulsed IRT experiment and the NLM prediction, thereby enabling the use of simulated data for ML training. This paper focuses on the initial stages of this process, in more detail on the analytical modelling and experimental data preprocessing, such as normalization, correction of experimental shortcomings and feature selection for machine learning. For pulse phase thermography (PPT) simulated and experimentally derived phase data was compared directly in the frequency domain. Therefore, the features for training and validation of ML were defined from those phase spectra in frequency domain. The suitability of these features for automated and reliable defect depth and/or defect material detection was investigated in both simulated and measured IRT test data. As a basis for feature selection, we used referenced and normalized phase-frequency curves as a function of defect depth. A correlation was identified between the results of the experimental and the simulated feature sets, both qualitatively and quantitatively. To demonstrate the practical applicability of this method, two different, generic ML techniques, multilayer perceptron and random forest regression, were tested as examples. The investigation was performed on plates made of multidirectional carbon fiber reinforced polymer (CFRP) with artificial defects made from three different materials.

Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model

In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.

Pseudo-noise pulse-compression thermography: A powerful tool for time-domain thermography analysis

Pulse-compression is a correlation-based measurement technique successfully used in many nondestructive evaluation applications to increase the signal-to-noise ratio in the presence of huge noise, strong signal attenuation or when high excitation levels must be avoided. In thermography, the pulse-compression approach was firstly introduced in 2005 by Mulavesaala and co-workers [1], and then further developed by Mandelis and co-authors that applied to thermography the concept of the thermal-wave radar developed for photothermal measurements [2-3]. Since then, many measurement schemes and applications have been reported in the literature by several groups by using various heating sources, coded excitation signals, and processing algorithms. The variety of such techniques is known as pulse-compression thermography or thermal-wave radar imaging.Even despite the continuous improvement of these techniques during these years, the advantages of using a correlation-based approach in thermography are still not fully exploited and recognized by the community. This is because up to now the reconstructed thermograms' time sequences after pulse-compression were affected by the so-called sidelobes, i.e. the temperature time trends of the pixels exhibit oscillations, especially in the cooling stage, so that they do not reproduce the output of a standard thermography measurement. This is a severe drawback since it hampers an easy interpretation of the data and their comparison with other thermography techniques.To overcome this issue and unleash the full potential of the approach, this paper shows how it is possible to implement a pulse-compression thermography procedure capable of suppressing any sidelobe by using a pseudo-noise excitation and a proper processing algorithm.At the end of the procedure, time-sequences of thermograms are reconstructed that correspond to the sample response to a well-defined virtual excitation, namely a rectangular pulse, making the pulse-compression procedure “transparent”. This allows the analysis of pixel time trends by using thermal theory-driven processing such as thermal signal reconstruction, pulsed-phase thermography, etc. Moreover, by tuning the characteristic of the pseudo-noise excitation, it is possible to pass from simulating a very short excitation pulse, retrieving results analogous to pulsed-thermography, to simulating long-pulse excitation to match the sample spectral characteristics maximizing the signal-to-noise ratio. This makes the procedure very flexible and extremely attractive in many applications such as high-attenuating materials, characterization of fast thermal phenomena, and inspection of fragile samples inspection, e.g. paintings or other artworks, etc.

Robot-based dynamic optics scanning infrared thermography for debonding defect detection of big-sized CFRP specimen

ABSTRACT The use of infrared thermography (IRT) for non-destructive testing (NDT) of large aerospace composites, such as carbon fibre reinforced polymer (CFRP), is often limited by the narrow field of view infrared cameras. This paper experimentally investigates the big-sized CFRP specimens with simulated debonding defects using a robot-based dynamic optics scanning infrared thermography (RDOS-IRT) method to meet these issues. Then, static pulse IRT and RDOS-IRT were performed, and the processing performance of different algorithms (pulsed phase thermography, PPT; total harmonic distortion, THD; matched filtering, MF; and supervised dimensionality reduction, SDR) on pulsed IRT was discussed. A combination of pseudo-static matrix reconstruction (FSMR) algorithm and static image sequence processing algorithm is innovatively adopted to improve the defect detection performance of RDOS-IRT results. The experiments show that the RDOS-IRT method with FSMR and static image sequence processing algorithm is effective; moreover, it has almost a similar detection ability compared to the traditional static pulse IRT method in practical applications and saves more time. Results show that the signal-to-noise ratio (SNR) of the repeated defects is not differentiated, and the detection effect is similar. Therefore, the RDOS-IRT method is a reliable tool for NDT tasks on big-sized CFRP specimens and can be practically applied in an industrial setting for high detection efficiency.

Improving defect detection capability of pulse and pulse phase thermography method for CFRP plates by enhancing rear surface heat transfer coefficient

Infrared pulse thermography (PT) is a noncontact and convenient nondestructive inspection method, and its application to various objects has been reported in many papers. However, conventional PT is not suitable for detecting deep- or near-rear-surface defects. This study focuses on PT inspections of CFRP plates and improvements in their defect detection capabilities. To improve the capability of detecting near-rear-surface defects, a method for enhancing the heat transfer coefficient (HTC) of the rear surface during PT inspection was examined in this study; this would increase the temperature difference in the defective areas observed in thermal images. To investigate the effectiveness of the proposed method, experiments were conducted on CFRP specimens with artificial defects. The CFRP specimens were inspected using the PT method, during which water was brought into contact with the rear surfaces of the specimens to enhance the HTC. The experimental results showed that the capability of detecting near-rear-surface defects improved when the rear surfaces were in contact with water with forced convection. In addition, obtaining phase images by applying a Fourier transform to the experimentally obtained temperature data (that is, pulse phase thermography: PPT) was examined. Consequently, the combination of the enhancement of the rear-surface HTC and the use of phase images was significantly effective in improving the defect detection capability.

Alternative data evaluation methodology for infrared thermography analogous to the Shock Response Spectrum analysis method

We propose an alternative method for processing infrared thermography (IRT) data based on a thermal 1-layer model. The method is based on the analogy of the Shock Response Spectrum (SRS) analysis for mechanical systems (ISO 18431). A thermal Q-factor, similar to the resonance sharpness (Q-factor) of a mechanical oscillator, is introduced as a parameter for selecting a specific thermal 1-layer model. Two main aspects of this method and corresponding properties have been considered: the strictly model-based treatment of this methodology and an empirical introduction of additional bandpass filters. The method is validated with two practical examples: for a homogeneous polymer sheet with blind holes and an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial defects, the capability of defect detection is compared with established methods. The comparison of the results with the commonly used data processing methods of IRT as Pulsed Phase Thermography (PPT) and Modified Differential Absolute Contrast (MDAC) shows equivalent or sometimes improved performance.

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.

Combined macro X-ray fluorescence (MA-XRF) and pulse phase thermography (PPT) imaging for the technical study of panel paintings

Museum staff usually relies on a proven combination of X-ray radiography (XRR) and infrared reflectography (IRR) to study paintings in a non-destructive manner. In the last decades, however, the research toolbox of heritage scientists has expanded considerably, with a prime example being macro X-ray fluorescence (MA-XRF), producing element-specific images. The goal of this article is to illustrate the added value of augmenting MA-XRF with pulse phase thermography (PPT), a variant of active infrared thermographic imaging (IRT), which is an innovative diagnostic method that is able to reveal variations between or in materials, based on a different response to minor fluctuations in temperature when irradiated with optical radiation. By examining three 16th- and 17th-century panel paintings we assess the extent in which combined MA-XRF and PPT contributes to a better understanding of two commonly encountered interventions to panel paintings: (a) Anstückungen (enlargement of the panel) or (b) substitutions (replacement of part of the panel). Yielding information from different depths of the painting, these two techniques proved highly complementary with IRR and XRR, expanding the understanding of the build-up, genesis, and material history of the paintings. While MA-XRF documented the interventions to the wooden substrate indirectly by revealing variations in painting materials, paint handling and/or layer sequence between the original part and the extended or replaced planks, PPT proved beneficial for the study of the wooden support itself, by providing a clear image of the wood structure quasi-free of distortion by the superimposed paint or cradling. XRR, on the other hand, revealed other features from the wood structure, not visible with PPT, and allowed looking through the wooden panels, revealing e.g. the dowels used for joining the planks. Additionally, IRR visualised dissimilarities in the underdrawings. In this way, the results indicate that PPT has the potential to become an acknowledged add-on to the expanding set of imaging methods for paintings, especially when used in combination with MA-XRF, IRR and XRR.

Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques

ABSTRACT Carbon fibre reinforced polymer (CFRP) composites are widely used in the aerospace industry; they are susceptible to impact damage from low-velocity impacts throughout their service life. Long pulse thermography (LPT) was proposed to detect defect in low-velocity impact test (LVIT). In this framework, the defects of matrix fractures, fibre cracks and interface delamination were produced by LVIT in the CFRP specimens. The halogen lamps LPT test system was applied to detect the low-velocity impact damage, and an A655sc infrared camera was applied to collect the surface temperature. In order to improve the efficiency of the impact damage detection, the infrared image sequences were processed by applying different post-processing techniques such as pulse phase thermography (PPT), thermal signal reconstruction (TSR), principal component analysis (PCA), total harmonic distortion (THD), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). The results show that the signal-to-noise ratio (SNR) was improved by PPT, TSR, PCA, THD, LDA and QDA post-processing techniques. The proposed supervised learning (SL) QDA algorithm achieves better defect identification results than the above post-processing techniques. The detectability of real defects can provide a realistic assessment by the above post-processing techniques.

Scanning pulse phase thermography for surface defect detection in manganese steel turnout frogs

Austenitic manganese steel is a commonly used material for railway turnout frogs due to its beneficial mechanical properties. In service, surface defects caused by rolling contact fatigue (RCF) can occur and need to be detected and assessed during maintenance intervals. In this work inductive scanning pulse phase thermography is used to localize these surface defects. During scanning the surface of the frog is heated (∆T<5K) with an air-cooled inductor. The surface temperature is recorded with an infrared camera. A registration target is also recorded in each frame of the image sequence. This method recognizes the movement in the sequence itself without external sensors. Furthermore, by using a registration target it is also possible to scan manually, as motion speed is calculated frame by frame. For the evaluation, the recorded sequence is transformed such that the turnout frog and the registration target seem to be stationary. In this new sequence the temporal changes in temperature of each pixel of the surface are evaluated by Fourier transform to a phase image. The evaluation via phase image is known to be robust to negative effects such as inhomogeneous heating and emissivity. This work aims at developing a mobile prototype, which allows service personnel to use scanning pulse phase thermography to localize and characterize surface defects on manganese steel turnout frogs during maintenance.

LFM-Chirp-Square pulse-compression thermography for debonding defects detection in honeycomb sandwich composites based on THD-processing technique

ABSTRACT Honeycomb Sandwich Composites (HSCs) have been widely used in aerospace and aircraft industries because of their high temperature and corrosion resistance, etc. As defects and damage to HSCs are unavoidable, non-destructive testing (NDT) is essential to ensure the reliability of HSCs. A series of artificial flat-bottomed holes (FBHs) defects of HSCs were prepared for pulse thermography (PT) and LFM-Chirp-Square pulse-compression thermography (PuCT). LFM-Chirp-Square PuCT has the advantage of frequency modulation over PT to detect different depth ranges of FBHs defects. In order to improve the efficiency of defect detection, pulse phase thermography (PPT) and total harmonic distortion (THD) techniques were applied to process the infrared image sequences. The results show that THD processing technique outperforms the PPT processing technique in improving the signal-to-noise ratio (SNR) from feature images. The combination of LFM-Chirp-Square PuCT and THD techniques can be applied to improve the defect detection identification results in HSCs.

Detection and Characterization of Artificial Porosity and Impact Damage in Aerospace Carbon Fiber Composites by Pulsed and Line Scan Thermography

Nondestructive testing (NDT) of composite materials is of paramount importance to the aerospace industry. Several NDT methods have been adopted for the inspection of components during production and all through the aircraft service life, with infrared thermography (IRT) techniques, such as line scan thermography (LST) and pulsed thermography (PT), gaining popularity thanks to their rapidity and versatility. On one hand, LST is an attractive solution for the fast inspection of large and complex geometry composite parts during production. On the other hand, PT can be employed for the characterization of composite materials, e.g., the determination of thermal diffusivity and defect depth estimation. In this study, the use of LST with an uncooled microbolometer camera is explored for the identification of artificially produced porosity and barely visible impact damage (BVID) on academic samples. The performance of LST is quantitatively assessed with respect to PT (considered the gold standard in this case) using a high-definition cooled camera through the contrast-to-noise ratio (CNR) criterium. It is concluded that, although in most cases the measured CNR values were higher for PT than for LST (as expected since a high-definition camera and longer acquisition times were used), the majority of the defects were clearly detected (CNR ≥ 2.5) by LST without the need of advanced signal processing, proving the suitability of LST for the inspection of aerospace composite components. Furthermore, the deepest defect investigated herein (z ≈ 3 mm) was detected solely by LST combined with signal processing and spatial filtering (CNR = 3.6) and not by PT (since pulse heating was not long enough for this depth). In addition, PT was used for the determination of the thermal diffusivity of all samples and the subsequent depth estimation of porosity and damaged areas by pulsed phase thermography (PPT).

Qualitative Comparison of Lock-in Thermography (LIT) and Pulse Phase Thermography (PPT) in Mid-Wave and Long-Wave Infrared for the Inspection of Paintings

When studying paintings with active infrared thermography (IRT), minimizing the temperature fluctuations and thermal shock during a measurement becomes important. Under these conditions, it might be beneficial to use lock-in thermography instead of the conventionally used pulse thermography (PT). This study compared the observations made with lock-in thermography (LIT) and pulse phase thermography (PPT) with halogen light excitation. Three distinctly different paintings were examined. The LIT measurements caused smaller temperature fluctuations and, overall, the phase images appeared to have a higher contrast and less noise. However, in the PPT phase images, the upper paint layer was less visible, an aspect which is of particular interest when trying to observe subsurface defects or the structure of the support. The influence of the spectral range of the cameras on the results was also investigated. All measurements were taken with a mid-wave infrared (MWIR) and long wave infrared (LWIR) camera. The results show that there is a significant number of direct reflection artifacts, caused by the use of the halogen light sources when using the MWIR camera. Adding a long-pass filter to the MWIR camera eliminated most of these artifacts. All results are presented in a side-by-side comparison.

Scanning inductive pulse phase thermography with changing scanning speed for non-destructive testing

ABSTRACT This work aims at non-destructive detection of surface and subsurface defects in metallic specimens (e.g. railway track components) via pulsed inductive thermography in a moving setup. A new approach for scanning with changing speed has been developed to enable manual scanning of components in track. A steel specimen is heated with an inductor, and the surface temperature, including its variation over time, is recorded with an infrared camera. The infrared sequence is evaluated by Fourier transform to phase image, in order to increase the image quality. The proposed method uses a referencing object with AprilTags visible in each frame, allowing an adjustment by image registration. Changes in velocity during the scanning process are possible, as demonstrated by a manually performed scan of the specimen and the corresponding evaluation. Results obtained with different infrared cameras and scanning speeds were condensed into an estimation of the maximum scanning speed for a given camera specification.

Study of multi-long-pulse thermography using high-power fiber lasers

Active infrared thermography tests are performed using a high-power continuous fiber laser as the excitation source. Multi-long-pulses (MLPs) are used to detect deep defects in samples with reduced heating. Simulation results are compared to theoretical predictions based on 1D heat conduction equations. Different excitation modes are compared. Thermal images are processed using pulsed phase thermography and principal component thermography (PCT), showing that the combination of MLP thermal excitation and PCT data analysis provides better defect detection capability and reduces the specimen’s surface temperature in comparison to conventional pulsed thermography.

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.

Toward an approach for moisture estimation during hot air drying of neem leaves (Azadirachta indica) using pulsed phase thermography

Drying agricultural products allows farmers to extend the life of their products for a longer time. In recent years, nondestructive testing provided an alternative to moisture estimation in the dehydrated products industry. This work improves a methodology to estimate moisture based on pulsed thermography. Furthermore, to demonstrate the principle of operation, an infrared camera monitored evolution across time from dry leaves stimulated with a heat pulse; a heat-response curve was generated from the images sequence, and several regressions (linear, polynomial, exponential, among others) related the moisture content to the reaction to the heat stimulation. Cross-validated best results, 0.91, 0.085164, 0.0072529 and 0.068838 of R-squared, RMSE, MSE, and MAE, respectively, indicated that pulsed thermography is an efficient method to estimate the moisture content in neem (Azadirachta indica) leaves.

Super-Resolution Thermal Imaging Using Uncooled Infrared Sensors for Non-Destructive Testing of Adhesively Bonded Joints

To compensate for the inability of inexpensive uncooled infrared (IR) sensors to perform high-speed thermography detection with low noise and high sensitivity in the field of non-destructive testing (NDT), a post-processing method for uncooled IR sensors based on compressive sensing is proposed in this paper to perform super-resolution NDT thermography. Super-resolution IR phase images can be generated by first using a sparse representation of the low-resolution (LR) IR images and a sparse dictionary generated from randomly sampled high-resolution (HR) raw training image patches to generate an HR IR image sequence, and, subsequently, implementing IR thermography NDT. To verify the reliability of the proposed method, super-resolution IR images were combined with pulsed phase thermography (PPT) to verify bonded joint structures in carbon fibre-reinforced polymer (CFRP). Compared to other methods, the proposed method reduces the mean square error (MSE) by 11.13% and yields a better visual effect on the PPT phase image reconstructed using three-fold super-resolution. The proposed method not only produces sharp defect edges, but also retains the original texture, which is expected to enable wider application of uncooled IR sensors in NDT applications in the future.

Defect detectability based on square wave lock-in thermography.

A lock-in thermography technique based on a periodical square wave is used to detect stainless steel plates with defects. Combining a neural network with lock-in thermography, an image processing technique is proposed, and the results are compared with traditional image processing methods. A full-field defect reconstruction technology is proposed that combines pulsed phase thermography, threshold segmentation technology, and lock-in thermography technology to reconstruct the full-field depth image. This method has fast processing speed and high detection accuracy. Finally, the effects of excitation frequency and duty cycle on thermal image quality, defect detection range, and defect detection accuracy are investigated through extensive experiments to arrive at the optimal excitation frequency and duty cycle.

The Study of Inspection on Thin Film Resistance Strain Gauge Contact Failure by Electrical Excitation Thermal-Wave Imaging

Contact failure as one of primary failures, which occurs between thin film resistance strain gauge (FRSG) and metal measured objective substrate, seriously influences the measurement validity and precision of the strain gauge. In this article, an electrical excitation thermal-wave imaging (EE-TWI) method is proposed for the inspection of contact failure. Equivalent circuits of the contact resistance under an external electrical source are used to identify the location of the contact failure. Long pulse thermography and lock-in thermography (LIT) are experimentally studied on the detection of contact failure. Pulsed phase thermography and principal component thermography in long pulse thermography as well as the amplitude, quadrature component in LIT are desirable to form the EE-TWI feature images, respectively. A graph-based segmentation method is employed to distinguish and localize the contact failure from EE-TWI images. EE-TWI provides a powerful tool for the contact failure evaluation of integrated thin film resistance strain gauge (FRSG) and has a significant potential to ensure the thin FRSG sticking performance and quality with application of mechanical properties measurements.