Induction Thermography Research Articles

Evaluation of Bonding Quality with Advanced Nondestructive Testing (NDT) and Data Fusion.

This work aims to compare quantitatively different nondestructive testing (NDT) techniques and data fusion features for the evaluation of adhesive bonding quality. Adhesively bonded composite-epoxy single-lap joints have been investigated with advanced ultrasonic nondestructive testing and induction thermography. Bonded structures with artificial debonding defects in three different case studies have been investigated: debonding with release film inclusion, debonding with brass film-large, debonding with brass film-small. After completing preprocessing of the data for data fusion, the feature matrices, depending on the interface reflection peak-to-peak amplitude and the principal component analysis, have been extracted from ultrasonic and thermography inspection results, respectively. The obtained feature matrices have been used as the source in basic (average, difference, weighted average, Hadamard product) and statistical (Dempster–Shafer rule of combination) data fusion algorithms. The defect detection performances of advanced nondestructive testing techniques, in addition to data fusion algorithms have been evaluated quantitatively by receiver operating characteristics. In conclusion, it is shown that data fusion can increase the detectability of artificial debonding in single-lap joints.

Characterizing Subsurface Rectangular Tilted Heat Sources Using Inductive Thermography

In this study, we characterize the lateral dimension, depth, and inclination of buried tilted rectangular heat sources from time domain temperature data measured at the surface. The heat sources are representative for planar defects that emit heat in thermographic tests with internal burst excitation. We present a semi-analytical expression for the evolution of the surface temperature distribution. The emitted flux, dimensions and inclination of the heat source are determined by fitting the model to two perpendicular surface temperature profiles and the temperature history at one point of the surface. We show that the sensitivity of the data to the geometrical parameters of the heat source decreases as the angle it makes with the surface increases. The study also shows that the optimum duration of the excitation corresponds to a thermal diffusion length covering the distance from the surface to the deepest end of the heat source. The accuracy and precision of the results for different noise levels and inclinations have been tested by fitting the model to synthetic data with added noise. Fittings of experimental induction thermography data on 3D printed photo-polymeric resin samples containing calibrated Cu slabs confirm that it is possible to characterize tilted rectangular heat sources from surface temperature data.

Detection of dis-bond between honeycomb and composite facesheet of an Inner Fixed Structure bond panel of a jet engine nacelle using infrared thermographic techniques

ABSTRACT The Inner Fixed Structure (IFS) bond panel is a honeycomb sandwich panel with CFRP facesheet and a heat shield on one side, and a perforated CFRP facesheet on the other side, of a jet engine nacelle. It is subjected to extreme temperature on both sides which damages the inner epoxy adhesive bond between the facesheet and the honeycomb core. Accessibility to this layer for non-destructive evaluation is extremely challenging using conventional methods. This work proposes active thermography techniques such as flash thermography and induction thermography for accessing the inner layer. The infrared camera utilises the perforations in the facesheet of the IFS bond panel, which is used for attenuating the engine noise, for imaging the defects. However, flash thermography requires the removal of the thermal insulation layer for the inspection, whereas induction thermography can be performed without any modifications to the structure. The minimum detectable dis-bond size using these techniques is restricted to the spacing between the perforations on the facesheet. A numerical model has developed for induction thermography to optimise the excitation frequency that can produce reasonable thermal contrast at the inner facesheet and minimum temperature rise on the intermediate stainless-steel thin sheet that covers the thermal insulation layer.

Simulation of induction thermography NDT technique using SIBC

PurposeThis paper aims to present a lightened 3D finite element model (FEM) for coupled electromagnetic thermal simulation of the induction thermography non-destructive testing (NDT) technique to reduce the computation time.Design/methodology/approachThe time harmonic electromagnetic problem is expressed in A – ϕ formulation and lightened by using the surface impedance boundary condition (SIBC) applied to both the massive induction coil surface and the surface of conductor workpiece including open cracks. The external circuit is taken into account by using the impressed voltage or the impressed current formulation. The thermal diffusion in the workpiece is solved by using surface electromagnetic power density as thermal source.FindingsThe accuracy and the usefulness of the method for the design of the induction thermography NDT technique have been shown with acceptable deviation compared with a full FEM model. It is also observed that at high frequency, when the ratio between the local radius of the conductor and the skin depth is high, a very good accuracy can be obtained with the SIBC methods. At lower frequency, the effect of the curvature of the surface becomes significant. In this case, the use of the Mitzner’s impedance can help to correct the error.Originality/valueThe SIBC can be used for both massive coil and workpieces with open cracks to alleviate 3D FEMs of the coupled electrothermal model. The implementation in matrix form of the coupled electrothermal formulation is given in details. The comparisons with reference analytical solution and full 3D FEM show the accuracy and performance of the method. In the test case presented, the computation time is 6.6 times lower than the classical model.

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

With the increasing use of induction thermography (IT) for nondestructive testing in the mechanical and rail industry, it becomes necessary for the Manufacturers to rapidly and accurately monitor the health of specimens. The most general problem for IT detection is due to strong noise interference. In order to counter it, general postprocessing is carried out. However, due to the more complex nature of noise and irregular shape specimens, this task becomes difficult and challenging. In this article, a low-rank tensor with a sparse mixture of Gaussian (LRTSMoG) decomposition algorithm for natural crack detection is proposed. The proposed algorithm models jointly the LRST pattern by using a tensor decomposition framework. In particular, the weak natural crack information can be extracted from strong noise. Low-rank tensor based iterative sparse MoG noise modeling is carried out to enhance the weak natural crack information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted for natural crack detection on a variety of specimens. A comparative analysis is presented with general tensor decomposition algorithms. The algorithms are evaluated quantitatively based on signal-to-noise-ratio along with the visual comparative analysis.

Depth quantification of surface-breaking cracks in ferromagnetic materials using DC-biased magnetization based induction thermography

A common challenge in maintenance of critical structures is how to fast localize and quantify geometries of surface-breaking cracks. Induction thermography (IT) is a potential technique to detect this type of defects. However, for testing ferromagnetic structures, if a deep defect exists in the wall structure, only its shallower part within the skin-depth layer affects the early thermal response of IT, whereas its deeper part hardly has any influence on the early response. To extend the detectable depth range of traditional IT for ferromagnetic materials, a DC-biased magnetization based induction thermography (DCMIT) technique is introduced in this paper. Based on the permeability distortion in the skin-depth layer, DCMIT can build a monotonic relation between the thermal feature and the defect depth. Results show that when the coil direction is parallel to the notch’s length direction, DCMIT can quantify the notch with a depth up to 6.0 mm whereas traditional IT can hardly achieve this depth detection.

Scanning induction thermography for subsurface defect orientation detection and depth quantification

Pulsed eddy current thermography (PECT) and eddy current lock-in thermography (ECLIT) are non-destructive testing (NDT) techniques of high promising and interest in subsurface defect detection. In the previous researches, the induction coil was set above the defect region and it always parallel to the defect orientation. However, the location and orientation of subsurface defects cannot be determined before detection. Therefore, the scanning induction thermography (SIT) based on dynamic thermography is proposed by some researchers to localize and distinguish the subsurface defects. Still, the main challenges of SIT are how to detect the subsurface defect orientation and quantify the depth. So that, the quantitative analysis in SIT with the new feature extraction methods was investigated and improved to detect the subsurface defect orientation and quantify the defect depth within 5 mm by using experimental studies.

Defect detection mechanisms with induction and conduction thermography: current flow and defect-specific warming

ABSTRACTActive thermography with electromagnetic excitation, is a reliable non-destructive evaluation method with a wide range of applications. It allows detecting inhomogeneities, like cracks, at or close to the surface of conductive components fast and reliable utilizing infrared imaging. Electric current can be used in two ways for thermography: In induction thermography a current is coupled to the component by passing an AC current through a coil which is close to the inspected component, while in conduction thermography the current is coupled directly into the component. Over the past years the basics of electromagnetic excitation, including the influence of the material of the component to be tested and the necessary post-processing algorithms have been studied.In this paper parameter studies on a comprehensive variety of defect models and their detection mechanisms are presented. Starting with delamination type defects, both within a component and between a coating and the component. Continuing with crack type defects open to the surface, like the well-known slot and notch type cracks, contact-point and ‘area of reduced conductivity’ type cracks. For those types’ parameters like depth, width, length, inductor position, rotation, inclination, and conductivity are discussed. The paper concludes with sub-surface cracks and cracks hidden under non-conductive coatings.

Fusion model of inductive thermography and ultrasound for nondestructive testing

Aluminum alloy is widely used in industry. Generally, the ultrasound testing as one of the non-destructive testing technologies has been applied to the metal sheet inspection for guarantying the quality of the production. However there exist challenges if only single ultrasound testing is applied. The near-field area of ultrasonic method remains limitation for detection. This paper investigates a robust data fusion model for comprehensive defects detection by hybriding inductive thermography and ultrasound testing in dynamic scanning. The proposed data fusion architecture is based on feature layer construction and it is able to cover the entire inspection from the surface, near surface and inside of the specimen. The experiment result demonstrates that the measurement of defects position and contour is accurate and reliable. It is easier to achieve automatic inspection through visualization and feature extraction. This method is effective and reliable for comprehensive detect and measurement metal plate.

A Model-Assisted Probability of Detection Study on Induction Thermography Technique

In this paper, one proposes a model-assisted probability of detection approach to study the reliability of detecting special defects. The approach is based on a 3-D finite-element T– $\Omega $ formulation with circuit coupling. This model is then applied to calculate performances of induction thermography method to detect thin open cracks in vessel reactor shell in nuclear installation.

The effect of paint coatings on detection of vertical surface cracks in metals by induction thermography

The effect of polymer coatings on the detection of surface cracks by induction thermography was investigated. Paint applied in defined thickness, layers of black adhesive tape and of sellotape and thicknesses of 0.05–0.5 mm were studied. Artificial vertical cracks in ferritic steel and natural cracks in a forged ferritic steel part were investigated. An analytical thermal wave model was set up. Experiments performed at 25 kHz induction frequency with pulses of 0.2s duration showed decrease of absolute and relative contrasts of the crack signals with increasing coating thickness. The effect of infrared transparency of coatings was visible in the experiments and treated in an analytical model of the radiation from the sample.

Lock-in inductive thermography for surface crack detection in different metals

ABSTRACTInductive thermography is a non-destructive technique for detecting surface cracks in electrically conductive materials. The induced eddy currents and the heat diffusion are disturbed by surface cracks, which makes the defects visible in infrared images. In non-magnetic materials, with high electrical and thermal conductivity, a short heating pulse is necessary, otherwise the thermal signal diminishes too quickly. However, with a short heating pulse only a little amount of heat is induced into the material, so the noise may be too high compared to the signal, causing a too low signal-to-noise (SNR) ratio for reliable defect detection. Applying a sequence of short pulses offers a solution. The evaluation of this multi-pulse excitation is in principle identical to the well-known lock-in technique, which is usually applied for sinusoid modulated heating. The main goal of the paper is to investigate how the temperature and phase noise can be reduced by several factors. The influence of the number of pulses, the heating power, the pulse duration, and the crack depth on the SNR is investigated. Theoretical considerations and experimental results are presented and compared to the derived equations. Aluminum and non-magnetic steel samples with artificial cracks were inspected and compared to the theoretical results.

Periodic Pulsed Thermography for Inner Defects Detection of Lead-Steel Bonded Structure

Multilayer materials with metal-metal bonded structure have been widely applied in aviation, aerospace, and nuclear industry. Disbond is prone to exist in lead-steel bonded structure, which degrades the load capacity and mechanical behaviors. Thermography nondestructive testing is a potential candidate for sub-layer defect detection. However, lead-steel bonded structure is unbearable when undertaken with over-heating of instantaneous temperature, which will lead to subsequent damage or generation of more unpredictable disbond. In addition, detection sensitivity of the deeper defects requires to be enhanced. In this paper, the mathematical derivation and the implementation of the periodic pulsed thermography have been established for detecting inner defects of lead-steel structure. This has been especially conducted for detecting small and deep defects that require high energy to increase detectability. Validation of the proposed method has been undertaken on both inductive thermography and optical thermography. The obtained results have demonstrated that periodic pulsed thermography is highly efficient for deep inner defect inspection of metal-metal bonded structure.

Induction and conduction thermography: From the basics to automated testing taking into account low and high residual stresses

Abstract Active thermography via electromagnetic excitation is a non-destructive evaluation method with a wide range of applications. It is a quick and reliable means for detecting inhomogeneities like cracks at or close to the surface of conductive components utilizing infrared imaging. Electric current can be used in two ways for thermography: In induction thermography, a current is coupled to the component without contact by passing an alternating current through a coil which is in close proximity to the component inspected; in conduction thermography, the current is coupled directly with the component. In this paper, we present a review of the basics of this non-destructive evaluation method along with several component examples and examples of inspection systems. In particular, automated testing is regarded. Additionally, sufficient detectability of cracks under low and high residual stress is discussed and compared with other surface testing methods.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Improving visibility of rear surface cracks during inductive thermography of metal plates using Autoencoder

Inductive thermography is one kind of infrared thermography (IRT) technique, which is effective in detection of front surface cracks in metal plates. However, rear surface cracks are usually missed due to their weak indications during inductive thermography. Here we propose a novel approach (AET: AE Thermography) to improve the visibility of rear surface cracks during inductive thermography by employing the Autoencoder (AE) algorithm, which is an important block to construct deep learning architectures. We construct an integrated framework for processing the raw inspection data of inductive thermography using the AE algorithm. Through this framework, underlying features of rear surface cracks are efficiently extracted and new clearer images are constructed. Experiments of inductive thermography were conducted on steel specimens to verify the efficacy of the proposed approach. We visually compare the raw thermograms, the empirical orthogonal functions (EOFs) of the prominent component thermography (PCT) technique and the results of AET. We further quantitatively evaluated AET by calculating crack contrast and signal-to-noise ratio (SNR). The results demonstrate that the proposed AET approach can remarkably improve the visibility of rear surface cracks and then improve the capability of inductive thermography in detecting rear surface cracks in metal plates.

Induction thermography of steel coupons with cracks.

The induction thermography technique was assessed experimentally and numerically on notched steel coupons using two coil configurations: straight line and loop conditions. The coupons had different lengths of fatigue crack varying from zero to 3mm. The numerical predictions showed that the peak temperature was always located at the crack tip or the notch tip position. The loop coil resulted in higher temperatures than those of the straight line coil. Results showed that the numerical methods effectively supported the application assessment of this nondestructive evaluation (NDE) technique for the steel material, but crack geometry remains a challenge as the exact shape of a natural crack is typically unknown. It is shown that induction thermography can detect a crack as small as 1mm in the notched steel coupons. Commentary on the application of the NDE technique with the numerical modeling support was provided.

Characterization of Electrical Conductivity of Anisotropic CFRP Materials by Means of Induction Thermography Technique

Numerical simulations by means of finite elements are used to investigate the capacities of the induction thermography technique for the characterization of electrical conductivities of unidirectional carbon fiber reinforced polymer (CFRP) materials. A coupled electromagnetic-thermal model is presented using hexahedral elements to deal with the thin region of fully anisotropic physical properties. Eddy-current problem is solved using the $ {{T}}$ - $\Omega $ formulation to deduce heat source density power. Transient thermal problem is solved with a fixed time step. An error minimization-based method is presented to identify the electrical resistivity of unidirectional CFRP from the induction thermography measurement.

Induction Thermography for Surface Crack Detection and Depth Determination

In the last few years, induction thermography has been established as a non-destructive testing method for localizing surface cracks in metals. The sample to be inspected is heated with a short induced electrical current pulse, and the infrared camera records—during and after the heating pulse—the temperature distribution at the surface. Transforming the temporal temperature development for each pixel to phase information makes not only highly reliable detection of the cracks possible but also allows an estimation of its depth. Finite element simulations were carried out to investigate how the phase contrast depends on parameters such as excitation frequency, pulse duration, material parameters, crack depth, and inclination angle of the crack. From these results, generalized functions for the dependency of the phase difference on all these parameters were derived. These functions can be used as an excellent guideline as to how measurement parameters should be optimized for a given material to be able to detect cracks and estimate their depth. Several experiments on different samples were also carried out, and the results compared with the simulations showed very good agreement.

Through coating imaging and nondestructive visualization evaluation of early marine corrosion using electromagnetic induction thermography

Early marine corrosion especially under coating is difficult to be detected, sized and predicted. This work proposed through coating imaging (TCI) based on high frequency electromagnetic induction, thermal wave propagation and infrared temperature field measurement. The proposed method is called electromagnetic induction pulsed phase thermography (EMIPPT), whose advantages include being nondestructive, non-contact, fast speed, large-area, high resolution and visualization. By phase analysis, the complex influence of parameter variation on temperature can be eliminated and then relative variation of corrosion height can be characterized. The mild steel (S275) samples providing coated real marine atmosphere corrosion with exposure from 1, 3 and 6 months were tested. The experimental results illustrate that EMIPPT has greater potential for in-service corrosion detection, sizing and monitoring than laser profilometry, pulsed eddy current, and microwave waveguide.