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4 results for “Acoustic signatures”
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Identification of Ionospheric Acoustic Wave Signatures from Conventional Surface Explosions Using MF/HF Doppler Sounding
<p>These HDF5 files contain complex time series data from HF receptions of a Digisonde Portable Sounder 4D (DPS4D). Each data point is the phase and amplitude of a decoded Sky Map mode pulse. </p>
Original data for "Experimental signatures of the transition from acoustic plasmon to electronic sound in graphene"
<p>This repository contains the original SNOM data and fits to them used in "Experimental signatures of the transition from acoustic plasmon to electronic sound in graphene" by D. Barcons Ruiz et al.<br> - The Raw_data folder contains the original scans recorded with a neaSNOM (neaspec Gmbh) microscope for the two devices in .dump format. <br> These data are easily accesible with Gwyddion. <br> The first part of the filename indicates the date of acquisition and the scanning conditions.<br> The suffix meaning of the individual scans within the raw data folders is:<br> - First element:<br> CT: topography<br> MX: X-harmonic of the mechanical signal (piezo)<br> BX: X-harmonic of the photocurrent signal<br> - Second element:<br> B/F: Backwards/Forward scans<br> - Third element:<br> abs: absolute value<br> arg: phase (-pi to pi)<br> <br> Ex:<br> 2022-02-02_10-48-16_2D-scan_4.25THz_GRp0.1V_GLsweep_m3Vtop1V_B1-B-abs.dump<br> 2022-02-02: date<br> 10-48-16: time<br> 2D-scan: scan type<br> 4.25THz: laser freq.<br> GRp0.1V_GLsweep_m3Vtop1V: scan conditions<br> B1-B-abs: 0th harmonic of the photocurrent signal, backwards trace, absolute value<br> <br> - The folder Figure2 contains the example traces extracted from the scans and shown in Fig.2 of the paper, and the fits to the optical signal, for both device 1 and 2.</p> <p>- The folder Figure3 contains the parameters obtained from the fitting of the plasmon fringes realizad as described in the Supplementary Information for both devices.<br> The real part of the plasmon wavevector is used to calculate the plasmon velocity data appearing in Fig.3 A-B of the paper and constitutes the most important dataset.<br> For every doubt on how to read or interpret these data please refer to the corresponding authors of the paper: <br> Prof. F.H.L. Koppens (frank.koppens@icfo.eu), <br> Dr. Iacopo Torre (iacopo.torre@icfo.eu).</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.