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20 results for “Laser Deposition”
Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_213nm
<p>RHEED intensity image dataset of sample "<strong>treated_213nm"</strong> in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>RHEED raw dataset and Gaussia fitting parameter dataset for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
NEMARCO project: Dataset for the publication "Development of a new manufacturing route for NiCrSiFeB alloys by Direct Energy Deposition Laser Beam process (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based NiCrSiFeB alloy (Ni-Cr-Si-B self-fluxing family) are excellent candidates for replacing Cobalt-based alloys in aeronautical components such as sealing rings, valve seats, sliding bearing seats, etc. In this type of components, commonly manufactured by centrifugal casting and conventional processes, high temperature wear and stiffness under complex thermo-mechanical stresses cause lack of sealing and an increase in the wear rate. Metal additive manufacturing by direct laser metal deposition with powder (p-LMD) is presented as a potential manufacturing route for the complex processing of this type of alloys. This research work deals with the development of a new manufacturing route using p-LMD that ranges from the proper selection of the chemical composition for the starting powders, the development of the LMD process parameters to tackle the challenges associated to the wide solidification range and crack susceptibility of Ni-Cr-Si-B alloys, its monitoring and control, as well as the post- processing required to achieve the manufacture of aeronautical components.</p>
NEMARCO project: Dataset for the publication "Solidification Path, Strengthening Mechanisms and Hardness of Ni-Cr-Si-Fe-B Self-Fluxing Alloys Obtained by Laser-Directed Energy Deposition (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based Ni-Cr-Si-B self-fluxing alloys are excellent candidates to replace Cobalt-based alloys in aeronautical components. In this work, metal additive manufacturing by directed energy deposition using a laser beam (DED-LB, also known as LMD) and gas-atomized powders as a material feedstock is presented as a potential manufacturing route for the complex processing of these alloys. This research deals with the advanced material characterization of these alloys obtained by LMD and the study and understanding of their solidification paths and strengthening mechanisms.</p>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Resistive switching suppression in metal/Nb:SrTiO3 Schottky contacts prepared by room-temperature Pulsed Laser Deposition
<p>Datafiles of the article "Resistive switching suppression in metal/Nb:SrTiO<sub>3</sub> Schottky contacts prepared by room-temperature pulsed laser deposition".</p>
Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample untreated_162nm
<p>RHEED intensity image dataset of sample <strong>untreated_162nm</strong> in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_81nm
<p>RHEED intensity image dataset of sample <strong>t0.08</strong> in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - gaussian_fit_parameters - sample untreated_162nm
<p>RHEED raw dataset and Gaussia fitting parameter dataset for sample "untreated_162nm" in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Compressed Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>Compressed version of RHEED image datasets and Gaussia fitting parameter datasets for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Acoustic Monitoring Dataset for Robotic Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz.</p><p><strong>Laser Directed Energy Deposition:</strong></p><p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p><p> </p><p><strong>Folder Structure:</strong></p><ul><li><strong>/sample-1</strong>: The main folder for the experiment sample.<ul><li><strong>/audio_files</strong>: Contains 4624 <strong>.wav</strong> audio files, each representing a 40 ms chunk of the LDED process sound.</li><li><strong>/annotations_1.csv</strong>: A CSV file providing annotations for the audio files, labeling each as "Defect-free", "Defective", or "Laser-off".</li></ul></li><li>audio_features.h5: extracted acoustic features in time-domain, frequency-domain, and time-frequency representations (MFCC features). Feature extraction was conducted using Python Essentia Library.</li></ul><p> </p><p><strong>File Naming Convention:</strong></p><ul><li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li></ul><p><strong>Annotation Details:</strong></p><ul><li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li></ul><p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p><ul><li>Geometry: Single bead wall structure</li><li>Dimensions: 90 mm * 42.5 mm</li><li>Number of layers: 50</li><li>Laser beam diameter: 2 mm</li><li>Layer thickness: 0.85 mm</li><li>Stand-off distance: 12 mm</li><li>Laser profile: Gaussian</li><li>Laser wavelength: 1064 nm</li></ul><p><strong>Process Parameters:</strong></p><ul><li>Laser power: 2.3 kW</li><li>Speed: 25 mm/s</li><li>Dwell time: 0 s</li><li>Powder flow rate: 12 g/min</li></ul><p>This dataset aims to facilitate the development and testing of acoustic-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
The data set to paper: Hydrolytic instability of laser-ablatively deposited CaSi2 coatings in air and neutral water affects the behavior of bone healing-related cell types
<p>Main message of this study is focused on calcium silicide (CaSi<sub>2</sub>) instability in humid air or pH-neutral water remains unknown, although the topochemical formation of silicene from CaSi<sub>2</sub> in various aqueous phases is a well-known process. The reseach reports on laser ablation of CaSi<sub>2</sub> in the vacuum and ethanol and examine the instability of the deposited coatings in humid air and neutral water. We further investigate the behavior of human mesenchymal stromal cells (hMSCs), vascular cells (HUVECS, human umbilical vein endothelial cells), and macrophages (derived from THP-1 cell line) in contact with the deposited coatings submerged in cell culture medium. </p>
DATASET - Room Temperature Pulsed Laser Deposition of Aluminum Zinc Oxide (AZO): Enabling Scalable Indium-Free Transparent Conductive Oxides
Open the record for dataset details and reuse information.
Audio Visual in-situ Monitoring Dataset for Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals and coaxial CCD images captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz. The coaxial CCD melt pool images are captured at 30 Hz.</p> <p><strong>Laser Directed Energy Deposition:</strong></p> <p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p> <p> </p> <p><strong>File Naming Convention:</strong></p> <ul> <li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample provided in this demo dataset, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li> <li>Coaxial melt pool image files within the <strong>images </strong>folder are named following the pattern <strong>sample_ExperimentID_SampleID.jpg</strong>. </li> </ul> <p><strong>Annotation Details:</strong></p> <ul> <li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file and image file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li> </ul> <p><strong>Handcrafted features for ML modelling:</strong></p> <ul> <li>The <strong>audio_features.h5</strong> file contains various physics-informed acousitc feature extracted through Python, which can be used for baseline ML modelling purpose.</li> </ul> <p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p> <ul> <li>Geometry: Single bead wall structure</li> <li>Dimensions: 90 mm * 42.5 mm</li> <li>Number of layers: 50</li> <li>Laser beam diameter: 2 mm</li> <li>Layer thickness: 0.85 mm</li> <li>Stand-off distance: 12 mm</li> <li>Laser profile: Gaussian</li> <li>Laser wavelength: 1064 nm</li> </ul> <p><strong>Process Parameters:</strong></p> <ul> <li>Laser power: 2.3 kW</li> <li>Speed: 25 mm/s</li> <li>Dwell time: 0 s</li> <li>Powder flow rate: 12 g/min</li> </ul> <p>This dataset aims to facilitate the development and testing of acoustic-based, or multi-sensor fusion-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
Titanium dioxide (TiO2) thin films study, deposited by laser ablation on glass substrates in the form of prismatic figures type pyramidal
<p>Thin films are something that is very close to the atomic dimensions in thickness, therefore if it is possible to further reduce the width of these, in a controlled way and monitor the behavior of some physical law associated with the variation in thickness, the results can be as expected or maybe adjustments must be applied. For this reason in this work, thin films of titanium dioxide (TiO2) are studied, deposited by laser ablation, on glass substrates in the form of prismatic figures. The main objective was to confine the plasma plume to produce a continuous trail with varying thicknesses that could be analyzed. Growth times of the films were 5, 10, 15, 20 and 30 minutes at room temperature in a low vacuum. The deposited films presented a color pattern related to the expected thickness variation, thus the theory of optical interference was applied to the continuous trace to calculate thicknesses. Characterization process was carried out by: X-ray diffraction to know the crystalline structure of the films, ultraviolet-visible spectroscopy to verify the variation of thicknesses, X-ray photoelectron spectroscopy to know the composition and scanning electron microscopy, to measure the thicknesses of the films transversely.</p>
Titanium dioxide (TiO2) thin films study, deposited by laser ablation on glass substrates in the form of prismatic figures type pyramidal
Open the record for dataset details and reuse information.
Laser-microdissected tissue from Tg2576 and APPPS1 plaque-depositing mouse model of Alzheimer's disease
GEO Series GSE15128. Mus musculus. 8 samples. Type: Expression profiling by array.
Expression profiling of laser-microdissected tissue from Tg2576 plaque-depositing mouse model of Alzheimer's disease
GEO Series GSE15056. Mus musculus. 4 samples. Type: Expression profiling by array.
Expression profiling of laser-microdissected tissue from APPPS1 plaque-depositing mouse model of Alzheimer's disease
GEO Series GSE15058. Mus musculus. 4 samples. Type: Expression profiling by array.
Pulsed laser deposition of AuGe alloy on polyethylene terephthalate
<p>Spectra recorded by Faraday cup and ion energy analyzer detectors. Spectra and images related the compositional characterization of the polymeric foil covered with AuGe alloy. Measurements related the performance of the manufactured foils for both the growth of cells and as humidity sensor.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.