Inversion technique for quantitative infrared thermography evaluation of delamination defects in multilayered structures

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without pre-inspections for characterization criteria. For traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modelling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduces its practical applicability. In this paper, we propose and experimentally validate an inversion technique for the reconstruction of complexly-shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Secondly, the multi-medium element modelling method is applied to enhance a FEM fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Thirdly, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically-simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in Infrared thermography NDT validated, but the practical applicability of inversion reconstruction analysis is significantly improved

Fusion of EML4 and ALK is associated with development of lung adenocarcinomas lacking EGFR and KRAS mutations and is correlated with ALK expression

<p>Abstract</p> <p>Background</p> <p>The anaplastic lymphoma kinase (<it>ALK</it>) gene is frequently involved in translocations that lead to gene fusions in a variety of human malignancies, including lymphoma and lung cancer. Fusion partners of <it>ALK </it>include <it>NPM</it>, <it>EML4</it>, <it>TPM3</it>, <it>ATIC</it>, <it>TFG</it>, <it>CARS</it>, and <it>CLTC</it>. Characterization of ALK fusion patterns and their resulting clinicopathological profiles could be of great benefit in better understanding the biology of lung cancer.</p> <p>Results</p> <p>RACE-coupled PCR sequencing was used to assess <it>ALK </it>fusions in a cohort of 103 non-small cell lung carcinoma (NSCLC) patients. Within this cohort, the <it>EML4</it>-<it>ALK </it>fusion gene was identified in 12 tumors (11.6%). Further analysis revealed that <it>EML4</it>-<it>ALK </it>was present at a frequency of 16.13% (10/62) in patients with adenocarcinomas, 19.23% (10/52) in never-smokers, and 42.80% (9/21) in patients with adenocarcinomas lacking <it>EGFR </it>and <it>KRAS </it>mutations. The <it>EML4</it>-<it>ALK </it>fusion was associated with non-smokers (<it>P </it>= 0.03), younger age of onset (<it>P </it>= 0.03), and adenocarcinomas without <it>EGFR</it>/<it>KRAS </it>mutations (<it>P </it>= 0.04). A trend towards improved survival was observed for patients with the <it>EML4</it>-<it>ALK </it>fusion, although it was not statistically significant (<it>P </it>= 0.20). Concurrent deletion in <it>EGFR </it>exon 19 and fusion of <it>EML4</it>-<it>ALK </it>was identified for the first time in a Chinese female patient with an adenocarcinoma. Analysis of ALK expression revealed that ALK mRNA levels were higher in tumors positive for the <it>EML</it>-<it>ALK </it>fusion than in negative tumors (normalized intensity of 21.99 vs. 0.45, respectively; <it>P </it>= 0.0018). However, expression of EML4 did not differ between the groups.</p> <p>Conclusions</p> <p>The <it>EML4</it>-<it>ALK </it>fusion gene was present at a high frequency in Chinese NSCLC patients, particularly in those with adenocarcinomas lacking <it>EGFR</it>/<it>KRAS </it>mutations. The <it>EML4</it>-<it>ALK </it>fusion appears to be tightly associated with ALK mRNA expression levels. RACE-coupled PCR sequencing is a highly sensitive method that could be used clinically for the identification of <it>EML4</it>-<it>ALK</it>-positive patients.</p

An efficient electromagnetic and thermal modelling of eddy current pulsed thermography for quantitative evaluation of blade fatigue cracks in heavy-duty gas turbines

The blade surface fatigue cracks often occur during service of Heavy-Duty Gas Turbines (HDGT) in high temperature, high rotational velocity and high frequency vibration environment. These fatigue cracks seriously threaten the safe operation of heavy-duty gas turbines, which would cause significant hazard or economic loss. The quantitative evaluation of blade surface fatigue cracks is extremely significant to HDGT. Eddy current pulsed thermography (ECPT) is an emerging non-destructive testing technology and show great potential for fatigue crack evaluation. This paper proposes a novel electromagnetic and thermal modelling of ECPT to achieve fast and effective quantitative evaluation for surface fatigue cracks. First, the proposed numerical method calculates electromagnetic field using the reduced magnetic vector potential method in the frequency domain based on frequency series method. The thermal source is transformed to an equivalent and simple form according to the energy equivalent method. Second, the temperature signals of ECPT are calculated through the time-domain iteration strategy with a relatively large time step. Then the ECPT experimental setup is established and the developed simulator is validated numerically and experimentally. The developed simulator is five times faster than the previous one and can be applied to eddy current thermography (ECT) with any kind of excitation waveforms. Finally, the depth of surface fatigue crack is quantitatively evaluated by means of the developed simulator, which is not only a promising simulation progress for ECPT, but also can be an effective tool embedded HDGT though-life maintenanc

A versatile and fast 3D thermography simulator with open GUI

Optical infrared thermography (IRT) has shown significant potential and advantages in defect detection in fiber reinforced polymers(FRPs) due to its capabilities of realizing non-contact and quasi real-time measurements over a large detection area. Simulation of optical IRT is of great importance as it provides support for model-based development, implementation and optimization of the experiment. To this end, the authors have developed and implemented a versatile and fast 3D simulator, based on finite element (FE) approach, for multi-layer anisotropic materials. In order to model defects, an interface element formulation has been developed which has several advantages over the standard volume element approach. The developed simulator has been benchmarked for different cases with commercial FE software. It is found that our simulator provides identical results, but more importantly, it is shown that the use of interface elements (instead of the standard volume elements) is computationally much more efficient. To better represent experiments, realistic non-uniform optical heating conditions are also implemented in the simulator. Besides, a stochastic defect modelling technique, on the basis of a morphological approach, is proposed to generate arbitrary, yet realistic, defect geometries. Finally, the fast simulator is programmed in a fully parametrized manner, which makes it suitable for generating large and diverse virtual databases which could be employed for deep learning purposes in thermography. A fully functional GUI module will be made available to the community

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

Automated delamination detection in CFRP using flash infrared thermography and deep learning method

Abstract: Carbon fiber reinforced polymer (CFRP) composites have become an important material for many industry applications due to their high specific stiffness and specific strength. Inevitably, defects generated during manufacturing and/or inservice process could compromise the structural health of the CFRP components. Flash infrared thermography(IRT) is a promising NDT technique, which has already been successfully applied for the detection of various defects in a range of materials. In order to bring this technique to the next level, this study aims to accomplish automatic detection and localization of delaminations in CFRP using flash IRT experiments and deep learning-based object detection method. A virtual dataset has been generated by means of a custom developed fast numerical simulator (programmed in Fortran) of flash IRT. Then, a pre-trained object detection framework from literature, i.e. Faster-RCNN, is applied to the virtual dataset using the concept of transfer learning. A comparison with classical post-processing methodologies, e.g. thermographic signal reconstruction, and principal component thermography, is presented. Finally, a CFRP slab including twelve artificial rectangular delaminations was inspected using flash IRT and evaluated through trained Faster-RCNN