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18 results for “CFRP”
Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer
<p>The fourth dataset dedicated to the <a href="http://openguidedwaves.de/">Open Guided Waves</a> platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. </p> <p>A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.</p> <p><br> Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.</p> <p>The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.</p> <p>The detailed description of the dataset is published in Data in Brief Journal [3].</p>
Temperature-dependent Lamb wave signals in highly anisotropic CFRP
<p>The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data.</p> <p>The following parameters were used during measurements:</p> <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> <p>The following equipment was used in the experiment:</p> <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> <p>Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.</p>
Electrical Potential Scans of unidirectional CFRP
<p>These are the results of a surface scan of the electrical potential field of CFRP materials. The gcode file was used to move the scanner to every position. For more information, please refer to the research article on the subject.</p>
CFRP Micro-computed tomography - twill weave
<p>A dataset containing three micro-computed tomography scans of a carbon fibre-reinforced polymer. The composites consist of twill weave reinforcement and epoxy matrix. Two samples (P1 and P3) have random stacking sequences, sample P5 has controlled stacking sequence. The uploaded dataset contains metadata files.</p>
Dataset: Long-Term Resistance of Gradient Anchorage for Prestressed CFRP Strips in Structural Concrete Retrofitting
<p>This dataset presents experimental research results on the long-term behaviour of a non-mechanical prestressed CFRP anchorage for concrete retrofitting.<br> The following data is included:<br> - Force-slip of lap-shear tests.<br> - Slip profiles (at last stage) of prestress force releasing and lap-shear tests.</p> <p> </p> <p>Further details on the experimental setup, as well as representation of datasets can be found in the following works.<br> Please cite the following works, if any part of the dataset is used within your research.</p> <p>https://doi.org/10.1016/j.compositesb.2017.11.062<br> https://doi.org/10.3390/polym10060565<br> Harmanci, Yunus Emre. Long-Term Resistance of Gradient Anchorage for Prestressed CFRP Strips in Structural Concrete Retrofitting. Diss. ETH Zurich, 2018.</p>
Dataset for semantic segmentation in NDT with step-heating thermography for CFRP laminates
<p>Dataset composed of 36 images (640x480 pixels) of 30 bands or channels from step-heating for the same Carbon Fiber Reinforced Polymer (CFRP) laminate.</p> <p>In each image the specimen is rotated 10º to generate new data, with different illumination, background, and heating/cooling sequences.</p> <p>Images are generated from a video composed of the heating process, which takes 10 seconds, and the cooling process, which takes another 10 seconds, to a total of 1000 frames per video. This video is then reduced to 30 images using different processing methods explained below.</p> <p>The 30 bands or channels consist of the following bands repeated for the heating and cooling processes:</p> <ol> <li>First PCT component</li> <li>Second PCT component</li> <li>Third PCT component</li> <li>Fourth PCT component</li> <li>PPT</li> <li>Kurtosis</li> <li>Skewness</li> <li>TSR Coefficient 7</li> <li>TSR Coefficient 6</li> <li>TSR Coefficient 5</li> <li>TSR Coefficient 4</li> <li>TSR Coefficient 3</li> <li>TSR Coefficient 2</li> <li>TSR Coefficient 1</li> <li>TSR Coefficient 0</li> </ol> <p>Please cite the original paper:</p> <p>LINK: TODO</p> <p>BibTex:</p> <p>TODO</p> <p> </p> <p> </p>
Characterisation Data - CFRP Composites
<p>unmodified-cfrp-composites_longitudinal-tensile-properties</p>
Characterisation Data - CFRP Composites
<p>modified-cfrp-composites_214-wt-gnps_dcbs</p>
Characterisation Data - CFRP Composites
<p>unmodified-cfrp-composites_flexural_01</p>
Characterisation Data - CFRP Composites
<p>modified-cfrp-composites-214-wt-ego_dcbs</p>
Characterisation Data - CFRP Composites
<p>modified-cfrp-composites-0089-wt-rgo_dcbs_1</p>
Characterisation Data - CFRP Composites
<p>modified-cfrp-composites_214-wt-gnps</p>
CFRP Composites
<p>SmartFan Project public data</p>
Characterisation Data - CFRP Composites
<p>unmodified-cfrp-composites_flexural_02</p>
Characterisation Data - CFRP Composites
<p>unmodified-cfrp-composites_dcbs</p>
Characterisation Data - CFRP Composites
<p>modified-cfrp-composites_0089-wt-rgo</p>
EXPERIMENTAL INVESTIGATIONS ON STEEL ANGLE BEAMS AND COLUMNS STRENGTHENED WITH CFRP PLATES
<p>The results of a testing campaign are presented for rolled steel equal angle sections strengthened with Carbon Fiber Reinforced Polymers (CFRP) plates, targeting the strengthening of existing steel lattice towers. Three-point bending and compression tests have been carried out in order to determine the mechanical properties of members, specifically the bending capacity to principal and geometric axis loading and the buckling strength in the absence or presence of loading eccentricity arising from the connection in one leg. The experimental data include actual material properties, geometrical imperfections, the recorded response to loading in terms of displacements, end-rotations, twist of section, strains in steel or CFRP material, and photos. Also, the experimental set-up, the instrumentation, as well as simple experimental results to indicate the strengthening effect and utility of them to further analyses are provided.</p>
Connecting Microscale And Macroscale Damage Models In A Bayesian Framework for Fatigue Damage Prognostics Of CFRP Composites
Composites offer unique advantages for aerospace structures and are increasingly being adopted into newer designs. However, it is also acknowledged that given current understanding of damage mechanisms in composites there is a significant risk with the extensive use of composites materials in aerospace applications. On one hand the uncertainty in damage evolution along lifetime is extremely large, and on the other hand there is a lack of knowledge about the mechanics of the onset, posterior growth, and interactions between several micro-scale damage modes. All these factors lead to the adoption of high safety margins in the design and costly inspection schedules along the service to mitigate the risks. Structural health monitoring for onboard damage diagnosis and prognosis of structural failures has the potential to reduce maintenance costs and improve the safety of the structure through a condition based maintenance scheduling. In this scheme the current damage state of a specific structural element is estimated and further used as the input for a prognostic algorithm that predicts the propagation of damage through time using updated models and based on some knowledge of the future load conditions. A novel damage prognostics framework for composites FRP under fatigue loadings is proposed in this work. The proposed methodology is grounded on physics-based models for evolution of damage at (1) micro-scale, i.e. micro-cracks and delamination, and (2) macro-scale such as stiffness reduction induced by micro-scale damage modes. Through stochastic embedding, these apriori deterministic models are converted to probabilistic models by introducing a modeling error term. This error term is controlled by a probability density function whose parameters are estimated in addition to the rest of "physical" parameters. The probabilistic damage models are then incorporated in a Bayesian filtering algorithm that sequentially updates both, a damage state variable and the set of model parameters, as fresh damage data become available along the fatigue cycling process. Next, these damage models are used to simulate fault propagation with this updated state information to generate a prognostic estimate of the remaining useful life of the structure in a probabilistic sense. The proposed methodology is demonstrated using experimental NDE damage data for micro-crack density, delamination area, and stiffness reduction from an extensive post-impact tension-tension fatigue test performed over several CFRP [0,90]4s laminates.
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International Brain Laboratory public data
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OpenNeuro
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