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124 results for “Composite materials”
Probabilistic Delamination Diagnosis of Composite Materials Using a Novel Bayesian Imaging Method
In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.
Accelerated Aging Experiments for Prognostics of Damage Growth in Composite Materials
Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.
Spectral and thermometric properties altering through crystal field strength modification and host material composition in luminescent thermometers based on Fe3+ doped AB2O4 type nanocrystals (A= Mg, Ca; B=Al, Ga)
<p>The growing interest in the use of luminescence thermometry for noncontact temperature reading in very specific conditions imposes the need to develop an approach allowing modification of the luminescence parameters of the thermometer accordingly to the requirements. Therefore, in response to these expectations, this manuscript reports an approach to modulating the spectral position and the luminescence thermal quenching rate of Fe<sup>3+</sup> ions by modifying the crystal field strength and the host material composition of nanocrystalline AB<sub>2</sub>O<sub>4</sub> type nanocrystals (A= Mg, Ca; B=Al, Ga). It was proved that in a group of MgAl<sub>2</sub>O<sub>4</sub>, MgGa<sub>2</sub>O<sub>4</sub>, CaAl<sub>2</sub>O<sub>4</sub>, and CaGa<sub>2</sub>O<sub>4</sub> nanocrystals doped with Fe<sup>3+</sup> ions the emission spectral range, as well as the relative thermal sensitivity (from 0.2%/<sup>o</sup>C for MAO to 2.07%/<sup>o</sup>C for CGO) and the operating temperature range, can be easily modified by the host material composition. For instance, a maximal relative thermal sensitivity of 2.58%/<sup>o</sup>C is obtained for Fe<sup>3+</sup>, Tb<sup>3+</sup> co-doped CaAl<sub>2</sub>O<sub>4 </sub>nanocrystals. The proposed approach is a step toward the intentional designing of the highly sensitive luminescent thermometer.</p>
Predicting the tensile properties of Wood Plastic Composites using material extrusion with Meta-based Few-Shot Learning
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