Enhanced FHL2 and TGF-β1 expression is correlated with poor survival in human malignant melanoma. Protumorigenic effects of autocrine TGF-β1 secretion might be exerted by induction of FHL2 expression in melanoma cells. Since melanomas treated with targeted therapies often do not show sufficient response rates, inhibition of FHL2 and/or TGF-β1 might be a promising therapeutic approach.
Riveting and bolting are common assembly methods in aircraft production. The fasteners are installed immediately after hole drilling and fix the relative tangential displacements of the parts, that took place. A proper fastener sequence installation is very important because a wrong one can lead to a “bubble-effect”, when gap between parts after fastening becomes larger in some areas rather than being reduced. This circumstance affects the quality of the final assembly. For that reason, the efficient methods for determination of fastening sequence taking into account the specifics of the assembly process are needed. The problem is complicated by several aspects. First of all, it is a combinatorial problem with uncertain input data. Secondly, the assembly quality evaluation demands the time-consuming computations of the stress-strain state of the fastened parts caused by sequential installation of fasteners. Most commonly used strategies (heuristic methods, approximation algorithms) require a large number of computational iterations what dramatically complicates the problem. The paper presents the efficient methods of fastener sequence optimization based on greedy strategy and the specifics of the assembly process. Verification of the results by comparison to commonly used installation strategies shows its quality excellence.
Inductive thermography is an inspection technique that consists of the heating of a sample or a component by short induced eddy current pulses (duration of 50-100 ms) while an infrared camera records the evolution of the surface temperature. Surface cracks in metals can be excellently detected by this inspection technique. Both the eddy currents and the heat diffusion are disturbed by cracks, therefore becoming visible in the infrared images. The recorded image sequence is processed using the Fourier transform, and the resulting phase image is analysed directly by technicians (human-based detection) or developed defect detection algorithms (computer-based), to localize the defects. For industrial applications, the reliability of an inspection technique is very important. This is why it is necessary to calculate the probability of detection (POD) of the technology considered for each case study. The goal of this investigation is to compare the two standard parametric POD calculation techniques, ‘hit/miss’ and ‘â versus a’, for detecting surface cracks with inductive thermography on the nickel-based austenitic superalloy Inconel 718. First, artificial defects are considered for an ‘â vs a’ POD analysis and results are compared to finite element simulations. Additionally, real cracks on TIG welds, created with a Varestraint test machine, have also been considered and corresponding ‘hit/miss’ POD calculations have been performed. However, it is important to note, that the deeper a crack, the larger obstacle it creates for the eddy current and for the heat flow. Hence not only the defect length, but also its depth affects the signal around it. This implies that the calculated POD depends on both the crack length and depth and that in some cases ‘hit/miss’ POD analysis will not be enough.
This work presents a convolutional neural network (CNN), trained on simulated data and used for the detection of cracks resulted by inductive thermography measurements. In inductive thermography the sample under study is heated with a short heating pulse and an infrared (IR) camera records the emitted surface radiation during both heating and cooling. The recorded IR sequence is then evaluated to a phase image using Fourier transform. In phase images, short surface cracks become visible due to the hot spots around the defect tips and due to the low phase value along the crack line. For the training of a deep neural network many images are necessary, which should be different from the images to be evaluated. This is why FEM simulations have been carried out varying crack length, depth and inclination angle. Additional Gaussian noise and augmentation have been added to these simulated images before using them to train a CNN. Samples with real cracks along a weld have been created in Inconel 718, and the CNN, trained on the simulation results, has been used for semantic segmentation of these real samples’ phase images, in order to identify the defects. Additionally, the samples were investigated by computer tomography, and this 3D information of the cracks is compared to the phase image results.
Riveting and bolting are common assembly methods in aircraft production. The fasteners are installed immediately after hole drilling and fix the relative tangential displacements of the parts, that took place. A proper fastener sequence installation is very important because a wrong one can lead to a “bubble-effect”, when gap between parts after fastening becomes larger in some areas rather than being reduced. This circumstance affects the quality of the final assembly. For that reason, the efficient methods for determination of fastening sequence taking into account the specifics of the assembly process are needed. The problem is complicated by several aspects. First of all, it is a combinatorial problem with uncertain input data. Secondly, the assembly quality evaluation demands the time-consuming computations of the stress-strain state of the fastened parts caused by sequential installation of fasteners. Most commonly used strategies (heuristic methods, approximation algorithms) require a large number of computational iterations what dramatically complicates the problem. The paper presents the efficient methods of fastener sequence optimization based on greedy strategy and the specifics of the assembly process. Verification of the results by comparison to commonly used installation strategies shows its quality excellence.
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