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Crack detection in metallic samples at high surface temperature, hostile and hazardous environments, etc. is challenging situation in any manufacturing industries. Most of the present NDE methods are suitable only for lower surface temperatures, especially room temperature. In this situation, we need a fast and non-contact NDT method which can be applied even in high sample surface temperature. Laser thermography is one of the techniques having a high potential in non-contact inspection. As a preliminary investigation, in this article, we have studied the potentiality of laser line thermography in crack detection at room temperature. In laser line thermography, a continuous wave (CW) laser is used to generate a laser line, which in turn is used to scan the metal surface. The heat distribution over the sample surface is recorded by an infrared thermal (IR) camera. Two different approaches are reported in this work. Firstly, a stationary laser line source and its interaction with cracks; secondly, moving laser line source scanning over a surface with crack. When the distance between crack centre to laser line centre increases, crack detectability will decrease; and when laser power increases, crack detectability will increase. A dedicated image processing algorithm was developed to improve the detectability of the cracks. To understand the heat transfer phenomenon, a simplified 3D model for laser thermography was developed for the heat distribution during laser heating and was validated with experimental results. Defects were incorporated as a thermally thin resistive layer (TTRL) in numerical modeling, and the effect of TTRL in heat conduction is compared with experimental results.
The detection of cracks before the failure is highly significant when it comes to safety-relevant structures. Crack detection in metallic samples at high surface temperature is one of the challenging situation in manufacturing industries.
Laser thermography has already proved its detection capability of surface cracks in metallic samples at room temperature. In this work a continuous wave (CW) laser use to generate a laser, which is using to scan the metal surface with notch.
The corresponding heat distribution on the surface monitored using infrared thermal (IR) camera. A simplified 3D model for laser thermography is developed and validated with experimental results. A dedicated image processing algorithm developed to improve the detectability of the cracks. To understand the dependency of surface temperature, laser power, laser scanning speed etc. in defect detection, we carried out parametric studies with our validated model. Here we Report the capability of laser thermography in crack detection at elevated temperature.
Online (passive) thermographic inspection of overlap joints of aluminium and zinc coated steel sheets made by cold metal Transfer weld brazing process was explored. Different experimental Trials were conducted for demonstrating the feasibility of thermographic inspection to detect the porosities, improper weld bead and to differentiate the pre weld temperature. The whole process was monitored using infrared cameras in different wavelength region.
Image analysis algorithms were developed to reconstruct the thermal images that contain the signatures of the weld defects and to extract the pre weld temperature and ist evolution with distance from the centre of the weld torch. Post-weld radiography lends strong Support to the observations.
In this study, the feasibility of using non-contact Infrared thermography as a potential tool to monitor the CMT welding process is explored. The presence of internal defects such as porosity, lack of filler material deposition and formation of improper weld bead produce perturbations in the surface temperature which can be identified using an Infrared thermography technique. We present recent results obtained from online monitoring of the the dissimilar joining using CMT weld brazing of Aluminum and Steel using a transmission mode measurement approach. The effect of loss of zinc coating on the weldability of the cold metal transfer joining of aluminum to galvanised steel was investigated. A correlation between measured online thermal indications with the weld anomalies is successfully attempted and the results are compared with the conventional post-weld NDT inspection methods.
The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component’s life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non- contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks.
Currently in laser thermographic inspection lacks a compromising data processing algorithm which is necessary for the fast crack detection and also the analysis of data is done as part of post processing. In this work we introduced a raw data based image processing algorithm which results precise, better and fast crack detection. The algorithm we developed gives better results in both experimental and modeling data. By applying this algorithm we carried out a detailed investigation Variation of thermal contrast with crack parameters like depth and width. The algorithm we developed is applied for various surface temperature data from the 2D scanning model and also validated credibility of algorithm with experimental data.
Honeycombs/compaction faults occur in the concrete structures due to improper solidification of the concrete, which may reduce the strength of the concrete and also act as a passage for the water/acids that further corrodes the reinforcements. This paper explores about the acoustic pulse-echo techniques for the detection of honeycomb defects in a laboratory specimen located at the Federal Institute for Materials Research and Testing (BAM), Berlin. Since concrete is an inhomogeneous medium, the defect Signals are masked by the material noise due to large amount of scattering/ reflections of acoustic waves. A filtering method using the discrete wavelet transforms is applied on the ultrasonic time Signals for the better localization of defects.
Development of reliable age prediction models are crucial in monitoring the formation of oxide layer and degradation of TBC at regular intervals. This study proposes an automated classification of isothermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of thermally grown oxide is monitored using a non-destructive thermal imaging technique. The proposed study integrates data-driven AI (DAI) models and feature extraction techniques to interpret complex thermal patterns measured from the TBC coating surface. The performance of the proposed classification framework is tested using deep learning and classical machine learning models with different types and window sizes of input data. Input data used for validation are raw experiment data, logarithmic of experiment data, polynomial fit data, and thermal signal reconstruction fit coefficients. The maximum c
Thermal Barrier Coatings (TBCs) are critical components in high-temperature applications, such as gas turbines and aerospace engines, where they protect the underlying substrate metals from extreme thermal stress and extend component life. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Popular non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth, and challenges in inspecting multi-layer coatings and detecting subsurface defects. To address these challenges, our study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that integrates the strengths of IRT and THz imaging for a comprehensive evaluation of TBCs. To generalize the study to a range of thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. The data from IRT and THz modalities are fused in the Sim-MDL models to enable characterization of the TBC topcoat layer. IRT and THz experimental data, together with simulations, form a large dataset that is used to train deep learning models. The framework is tested and optimized for multimodal data fusion using two DL architectures based on convolutional neural networks (CNN) and long short-term memory (LSTM), allowing the model to learn correlations and complex patterns across the IRT and THz modalities. The study is conducted on four newly coated samples ranging in thickness from 24 to 120 µm. An attention-based LSTM model trained on both simulation and experimental data shows high prediction accuracy with MAPE values ranging from 2.06–4.43% for thermal conductivity, 2.05–3.57% for heat capacity, 11.53–1.75% for topcoat thickness, and 0.27–1.05% for refractive index, respectively, for the topcoat layers of four samples. Our model outperformed the single-modality models and conventional parameter estimation methods in terms of accuracy and robustness, highlighting the potential of multimodal data for automated analysis of TBC in industrial settings.
Thermal Barrier Coatings (TBCs) are critical for high-temperature applications, such as gas turbines and aerospace engines, protecting metallic substrates from extreme thermal stress and degradation. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Conventional non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth mainly in multi-layer coatings. This study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that combines IRT and THz data for a comprehensive evaluation of TBCs. To generalize the study to varying thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. Two deep learning frameworks based on a 1D convolutional neural networks (CNN) and a long short-term memory (LSTM) with attention were developed for the multimodal feature fusion. The IR-THz fused frameworks enable simultaneous prediction of key TBC topcoat properties including thermal conductivity, heat capacity, topcoat thickness and refractive index. Experiments were conducted on four newly coated samples topcoat thicknesses ranging from 24 to 120 μm. An attention-based LSTM model trained on both simulation and experimental data shows high prediction accuracy with MAPE values ranging from 2.06% to 4.43% for thermal conductivity, 2.05% to 3.57% for heat capacity, 11.53% to 1.75% for topcoat thickness, and 0.27% to 1.05% for refractive index, respectively, for the topcoat layers of four samples. The proposed Sim-MDL framework outperformed single-modality and conventional parameter estimation methods in accuracy and robustness, highlighting the potential of multimodal data for automated analysis of TBC in industrial settings.
This article presents an analytical approach for simulation of ultrasonic diffracted wave signals from cracks in two-dimensional geometries based on a novel Huygens–Fresnel Diffraction Model (HFDM). The model employs the frequency domain far-field displacement expressions derived by Miller and Pursey in 2D for a line source located on the free surface boundary of a semi-infinite elastic medium. At each frequency in the bandwidth of a pulsed excitation, the complex diffracted field is obtained by summation of displacements due to the unblocked virtual sources located in the section containing a vertical crack. The time-domain diffracted wave signal amplitudes in a general isotropic solid are obtained by standard Fast Fourier Transform (FFT) procedures. The wedge based finite aperture transducer refracted beam profiles were modelled by treating the finite dimension transducer as an array of line sources. The proposed model is able to evaluate back-wall signal amplitude and lateral wave signal amplitude, quantitatively. The model predicted range-dependent diffracted amplitudes from the edge of a bottom surface-breaking crack in the isotropic steel specimen were compared with Geometrical Theory of Diffraction (GTD) results. The good agreement confirms the validity of the HFDM method. The simulated ultrasonic time-of-flight diffraction (TOFD) A-scan signals for surface-breaking crack lengths 2 mm and 4 mm in a 10 mm thick aluminium specimen were compared quantitatively with the experimental results. Finally, important applications of HFDM method to the ultrasonic quantitative non-destructive evaluation are discussed.
