Chemie und Prozesstechnik
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- Composite structures (2)
- Damage detection (2)
- Explainable machine learning (2)
- Guided waves (2)
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- Structural health monitoring (2)
- Thermography (2)
- Barker codes (1)
Organisationseinheit der BAM
Novel Technique for On-Line Monitoring of the Curing Process of Fiber Reinforced Polymer Composites
(2021)
A specially developed experimental set up has been integrated into a commercially available FT IR Spectrometer to study the curing of Epoxy Systems. MIR and FIR spectra measured during the curing process at different temperatures of an epoxy system will be presented in dependency of the curing time. The experiments and the selected set up are discussed to demonstrate its potential for future monitoring and ageing control applications during a manufacturing process of polymer composites.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.
In this work we introduce an algorithm based on the well-known Canny approach for effectual crack detection in thermographic films obtained using flying spot thermography (FST) or flying line thermography (FLT). The proposed algorithm performs faster than another algorithm, for crack detection, based on the application of two Sobel filters (one in x and another one in y directions). For FLT it is shown that processing 10-25 % of the thermograms of a thermographic film required to scan a whole sample is enough to obtain good results. In contrast, using the Sobel filter approach requires the processing of twice the thermographic film length. Experimental measurements are performed on a metallic component of complex shape which contains real defects, that is, surface breaking cracks due to industrial use. The specimen is tested using flying line thermography. Three different scanning speeds are tested: 10, 30 and 60 mms-1 with laser powers of 50, 60 and 120 W respectively. The sample and an infrared camera are aligned and fixed on a motorized linear stage. The diode laser LDM500 (500 W max power) is fixed on an optical bench separately from the linear stage. The results obtained
with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography based on the Sobel filter. It is shown that the proposed algorithm based on the Canny approach, can be used in automated systems for thermographic non-destructive testing.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
The duraBASt area at the highway intersection “Köln-Ost” is a large test field for road testing.
It includes the duraBASt test bridge which is approximately 66 m long and 14.25 m broad. This bridge is part of the “road in the 21st Century” project and is equipped with several sensors for structural health monitoring. This feasibility study is carried out to investigate the prerequisites necessary for the constraction of intelligent bridges in the foture. Embedded sensors are able to measure quantities such as temperature, strain, moisture, corrosion, etc. In the presented work, the focus is set on passive RFID-based sensors. The communication and energy supply are realised by a mobile transmitter through the concrete cover. Hence, no cables or batteries are required. The sensors are designed to withstand the alkaline environment in concrete. Thus, moisture and corrosion values are measurable for several decades.