3 results
Now showing1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Thermoset state estimation using infrared spectroscopy and predictive analytics(2023); By design fiber-reinforced plastics (FRP) show extraordinary specific strength and stiffness performance when compared to classic structural materials. Unfortunately, the extraordinary properties come at the price of complexity in design, manufacturing and operation. Most notably, manifold failure modes and complicated detectability of inherent damages represent a challenge for composite engineers until now. Throughout the life-cycle of composites, it is crucial to understand and track the material state in order to prevent premature failure. Due to the high-cost sensitivity of the composites sector implementing material health monitoring systems is usually not practical in application. Instead, non-destructive testing (NDT) methods are usually applied to assure structural integrity and damages within allowed limitations at planned maintenance events. Nonetheless, currently available NDT methods are insufficient to fully cover all possible damage scenarios. In this work a new approach to determine material properties of the epoxy resin component using Fourier-transform infrared spectroscopy (FTIR)-spectroscopy is presented. It is shown that by post-processing the acquired molecular information the material state can be determined and based on this material state inherent material properties such as strength can be predicted. First, starting with the evaluation of manufacturing parameters it is shown that by using classic chemometrics techniques FTIR measurements can be used to quantify the mixing ratio of an epoxy resin fast and reliable. Thereafter, material changes due to mechanical loading in creep are observed and described by applying advanced feature extraction methods. Finally, specimens exposed to thermal loads are analyzed and it is confirmed, that the residual strength can be determined solely by applying FTIR measurements and machine learning algorithms. Furthermore, it is demonstrated, that the method is suitable to reveal the material exposure history. It is shown that an accurate prediction of mechanical properties, as well as the processing and degradation parameters, can be derived from the measurements and significantly improved by applying data processing and machine learning methods.Publicationtype: Doctoral Thesis114 736 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Mechanical degradation estimation of thermosets by peak shift assessment: General approach using infrared spectroscopy(2021-04-14); ; ; ; Until now, detecting weak spots in composite structures remains a key challenge in the aviation industry. The correct assessment of the load-bearing capability after structural overloading or the occurrence of barely-visible damages is particularly important to maintain structural integrity. Nonetheless, a reliable and overarching non-destructive inspection method to estimate the residual mechanical properties while covering all major damage scenarios has not been found yet. Several non-destructive techniques have been proposed to approach these challenges and are already in place for specific damage cases. However, each technique has its sources of information and therefore, limitations in practice due to a lack of generalisation. In this work, we present a concept and approach to gain access to the residual mechanical properties of a thermosetting polymer solely based on its inherent material state independent from its life-cycle history. Therefore, the material state is obtained by combining Fourier-transformed infrared spectroscopy with feature extraction algorithms based on Gaussian peak fitting. As proof of concept, tensile, creep, and cyclic tests are conducted to demonstrate this approach's advantage. A complementary theoretical investigation using quantum chemical calculations is employed to support the experimental work by identifying the investigated polymer's characteristic vibrational modes and predicting their evolution during the experiments. The results show that the quantification of molecular changes can estimate the material state and that the method is suitable to improve the understanding of the degradation processes and severity. This publication shall particularly serve as the basis for further research to study the interaction between molecular forces and material properties.Publicationtype: Journal Article93 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Prediction of thermal exposure and mechanical behavior of epoxy resin using artificial neural networks and Fourier transform infrared spectroscopy(Multidisciplinary Digital Publishing Institute (MDPI), 2019-02-19); ; Thermal degradation detection of cured epoxy resins and composites is currently limited to severe thermal damage in practice. Evaluating the change in mechanical properties after a short-time thermal exposure, as well as estimating the history of thermally degraded polymers, has remained a challenge until now. An approach to accurately predict the mechanical properties, as well as the thermal exposure time and temperature of epoxy resin, using Fourier-transform infrared spectroscopy (FTIR)-spectroscopy, data processing, and artificial neural networks, is presented here. Therefore, an epoxy resin has been fully cured and exposed to elevated temperatures for different time periods. A FTIR-spectrometer was used to measure molecular changes, using mid-IR (MIR)-FTIR for film samples and near-IR (NIR)-FTIR for bulk samples. A quantitative analysis of the thermally degraded film samples shows oxidation, chain-scission, and dehydration in the FTIR spectra in the MIR-range. Using NIR spectroscopy for the bulk samples, only minor changes in the FTIR spectra could be detected. However, using data processing, molecular information was extracted from the NIR range and a degradation model, using an artificial neural network, has been trained. Even though the changes due to thermal exposure were small, the presented model is capable of accurately predicting the time, temperature, and residual strength of the polymer.Publicationtype: Journal ArticleScopus© Citations 55 346 364