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77 results for “additive manufacturing”
Reproducibility Case Study and Survey: Machine Learning-based Additive Manufacturing Process Monitoring and Quality Prediction
<p><span>Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). However, the reproducibility of the proposed ML-based AM monitoring systems in published works has not been investigated due to a lack of evaluation methods. In the paper 'Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing,' we propose a reproducibility investigation pipeline and conduct two case studies to validate the pipeline. This dataset records the data generated by one of the case studies. This dataset also contains the reproducibility survey results.</span></p>
Neutron Bragg edge imaging for strain characterization in powder bed additive manufacturing environments
<p>The paper describes an approach to disentangle the recorded transmission spectrum obtained from Bragg edge imaging. The transmission spectrum of the samples embedded in their corresponding powder was successfully extracted, proven by the coinciding strain maps produced. The horizontal profiles from the maps were plotted together with neutron diffraction results and they agree very well. The uploaded data were the raw data obtained from Bragg edge imaging and neutron diffraction experiments used to construct the strain maps and plots shown in the paper.</p>
STL files: Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing
<p>STL files for paper titled modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing</p>
Dataset_Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology
<p>Original micro-CT imaging data and output of processing via the OpenFiberSeg software of additively manufactured carbon fiber reinforced composites. </p> <p>This dataset accompanies the publication in Composite Science and Technology available at https://doi.org/10.1016/j.compscitech.2023.110261</p> <p> </p>
High absorptivity nanotextured powders for additive manufacturing
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Kikuchi pattern dataset from wrought and as-built additively manufactured superalloys
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Strong Scaling Tools 2 Additive manufacturing case study
<p>All data, results, and scripts from the Al-Si-Sc additive manufacturing alloy design high-throughput case study.</p> <p>All data, results, and scripts from Al-Zn-Mg-Cu high-throughput case study.</p> <p><strong>*.zip</strong> are the specimen-specific PARAPROBE results and POS rawdata.</p> <p><strong>*.pdf</strong> are the PDF reports that were created with the automation of paraprobe-autoreporter.</p> <p><strong>src.zip</strong> contains the source code of the paraprobe-autoreporter Python classes with which the results were computed. This source code is meant as a documentation of the results only!<br> <strong>Instead, all users should always use the latest versions of the PARAPROBE tools from the GitLab repository https://gitlab.mpcdf.mpg.de/mpie-aptfim-toolbox</strong>/<strong>paraprobe</strong><br> <strong>https://paraprobe-toolbox.readthedocs.org</strong></p>
Original research data for the paper "Additive Manufacturing of Polymeric Gradient Index Optics via Grayscale Digital Light Processing Vat Photopolymerization Technology"
<p>[CMOS camera data]</p> <p>[conversion maps]</p> <p>[cure kinetics]</p> <p>[GRIN profiles arbitrary]</p> <p>[MonoPrinter grayscale power density]</p> <p>[MonoPrinter print files]</p> <p>[pictures of arbitrary GRINs]</p> <p>[predicted printing param matrices]</p> <p>[refractive index vs conversion]</p> <p>[working curve]</p>
Dataset for "The effect of the unique microstructure of additively manufactured Fe-Mn-Si-Cr shape memory alloys on recovery stress"
<p>The uploaded dataset contains all relevant primary data for the publication titled "The effect of the unique microstructure of additively manufactured Fe-Mn-Si-Cr shape memory alloys on recovery stress". </p> <p>The data is structured in subfolders:</p> <p>01_Recovery stress:</p> <p>Primary data of thermo-mechanical experiments including force, strain and temperature measurements and a data description file.</p> <p>02_Micrographs:</p> <p>Image files of etched and unetched sample cross-sections of additively manufactured samples with different Mn content.</p> <p>03_EBSD:</p> <p>OIM files of electron backscatter diffraction measurements for all samples. </p> <p>04_Chemical analysis</p> <p>Analysis report of externally perfomed chemical analysis of the used metal powders and the resulting additively manufactured parts.</p> <p> </p> <p>For further information see the publication as soon as published.</p> <p> </p>
Dataset for: Tomographic Volumetric Additive Manufacturing of Silicon Oxycarbide Ceramics
<p>Ceramics are highly technical materials with properties of interest for multiple industries. Precisely because of their high chemical, thermal, and mechanical resistance, ceramics are difficult to mold into complex shapes. A possibility to make convoluted ceramic parts is to use preceramic polymers (PCP) in liquid form. The PCP resin is first solidified in a desired geometry and then transformed into ceramic compounds through a pyrolysis step that preserves the shape. Light-based additive manufacturing (AM) is a promising route to achieve solidification of the PCP resin. Different approaches, such as stereolithography, have already been proposed but they all rely on a layer-by-layer printing process which sets limitations on the printing speed and object geometry.<br> Here, we report on the fabrication of complex 3D centimeter-scale ceramic parts by using tomographic volumetric printing which is fast, high resolution and offers a lot of freedom in terms of geometrical design compared to state-of-the-art AM techniques. First, we formulated a photosensitive preceramic resin that was solidified by projecting light patterns from multiple angles. Then, the obtained 3D printed parts were converted into ceramics by pyrolysis. We demonstrate the strength of this approach through the fabrication of dense microcomponents exhibiting overhangs and hollow geometries without the need of supporting structures, and characterize their resistance to high heat and harsh chemical treatments.</p>
Postural stability metrics associated to the publication: Additive manufacturing of spinal braces: evaluation of production process and postural stability in patients with scoliosis
<p>Data was collected for each condition (3D-printed brace, conventional brace, unbraced) for 60 seconds with patients in a standing posture, open eyes and both feet together.</p>
Characteristics and Processing of Hydrogen Treated Copper Powders for EB-PBF Additive Manufacturing
<p>Supplemental videos for article titled "Characteristics and Processing of Hydrogen Treated Copper Powders for EB-PBF Additive Manufacturing" in Applied Sciences</p>
Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework
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Experimental data used in the article entitled "Tuning the defects density in additively manufactured fcc aluminium alloy via modifying the cellular structure and post-processing deformation"
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Experimental data used in the article entitled "Electron Microscopy Study of Structural Defects Formed in Additively Manufactured AlSi10Mg Alloy Processed by Equal Channel Angular Pressing"
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Ptychotomography datasets of an ultrafine eutectic Ti-Fe-based alloy processed by additive manufacturing
<p>The development of metals tailored to the metallurgical conditions of laser-based additive manufacturing is crucial to advance the maturity of these materials for their use in structural applications. While efforts in this regard are being carried out around the globe, the use of high strength eutectic alloys have, so far, received minor attention, although previous works showed that rapid solidification techniques can result in ultrafine microstructures with excellent mechanical performance, albeit for small sample sizes. In the present work, a eutectic Ti-32.5Fe alloy has been produced by laser powder bed fusion aiming at exploiting rapid solidification and the capability to produce bulk ultrafine microstructures provided by this processing technique.</p> <p>The uploaded datasets are a raw dataset of the 3D microstructure of the eutectic Ti-32.5Fe alloy acquired by ptychotomography at the beamline ID16A of the ESRF in Grenoble, France and a corresponding binary dataset for the bright Ti-Fe phase.</p> <p>The datasets have a dimension of 1173 x 1182 x 1356 px³ with a voxel-size of (10 nm)³.</p> <p>raw dataset: "RAW_10nm_ptycho_1173x1182x1356.tif"</p> <p>binary dataset of the Ti-Fe phase: "TIFE_BINARY_10nm_ptycho_1173x1182x1356.tif"</p> <p>more details can be found in J. Gussone, K. Bugelnig and P. Barriobero-Vila et al. / Applied Materials Today 20 (2020) 100767. https://doi.org/10.1016/j.apmt.2020.100767<br> </p>
Mechanical properties on Metal Additive manufacturing
<p><strong>Mechanical properties on Metal Additive manufacturing</strong></p> <p><strong>Abstract</strong></p> <p>This dataset contains the results on the experimental procedure to gather data on the manufacturing of Additive Manufacturing (AM) pieces for Laser Metal Deposition (LMD) processes.To create this dataset, a set of 37 pieces were manufactured using the same material and procedure but different manufacturing parameters and strategies. The process was monitored and recorded, capturing thermal images and sensor readings on each point of the manufacturing. Each one of the pieces was divided in 4 coupons, that were tested to obtain mechanical properties of resistance and hardness.</p> <p> </p> <p><strong>Contents</strong></p> <p>This dataset contains data related to 37 coupons (named T1 - T37), including the monitoring data gathered during manufacturing, the process parameters, results of tensile testing, results of hardness testing and pictures of the coupons.</p> <p>The dataset is structured as follows:</p> <ul> <li>[Piece ID] <ul> <li>Hdf5 file</li> <li>Piece picture</li> <li>Piece crosssection</li> </ul> </li> <li>Testing <ul> <li>Parameters.csv: tabular data with process parameters and piece measurements as a direct result of the manufacturing</li> <li>Coupons.csv: tabular data with tensile and hardness metrics on the tested coupons for each piece</li> </ul> </li> <li>README: explicative document</li> </ul> <p> </p> <p><strong>Formats</strong></p> <p>The data is provided in the following formats:</p> <ol> <li>Monitoring: HDF5 file format. This format allows the recording of multimodal manufacturing data.</li> <li>Pictures: jpg format</li> <li>Parameters: csv file containing process parameters</li> <li>Coupons: csv file with the material testing results.</li> </ol> <p>More information on the contents and formats of the dataset is contained in the attached README document.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work has been funded under the "Red de Excelencia en Fabricación Aditiva" (READI) with the support of the Spanish Ministry of Science and Innovation and the Centre for the Development of Industrial Technology (CDTI), under the program Cervera (CER-20191020).</p> <p> </p>
A Descriptive, Prospective Clinical Study to Evaluate Full Dentures Fabricated by Additive Manufacturing
ClinicalTrials.gov study NCT03997604. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluation of wear mechanisms in additive manufactured carbide-rich tool steels
<p>Dataset supporting the paper:</p> <p>E. Iakovakis, M. J. Roy, M. Gee, and A. Matthews, “Evaluation of wear mechanisms in additive manufactured carbide-rich tool steels,” <em>Wear</em>, vol. 462–463, no. March, p. 203449, 2020, doi: <a href="https://doi.org/10.1016/j.wear.2020.203449">10.1016/j.wear.2020.203449</a></p> <p>The dataset contains the macro-hardness and friction values, the x,y,z coordinates from profilometry and the videos from the in-situ scratch wear tests of three additive manufactured carbide-rich tool steels (Vibenite®150, Vibenite®280 and Vibenite®290).</p> <ul> <li>Macro-hardness measurement files(Vibenite 150/280/290 30 meas macro hardness.csv): The .csv files report the macro hardness for 30 measurements.</li> <li>Reciprocating tests files (Vibenite 150/280/290 cof/cof 2nd/cof 3rd.csv): The .csv report the parameters of the reciprocating dry wear tests and the friction forces.</li> <li>Profilometry measurements files (Vibenite 150/280/290 profilometry.dat): The .dat files report the x,y,z coordinates of the wear track from the reciprocating tests.</li> <li>Videos from in-situ scratch wear tests(Vibenite 150/280/290 100 passes.avi): The .avi files show the surface degradation effects during the scratch wear test.The sequence of scanning electron microscopy images were registered with ImageJ to generate a video.</li> </ul>
Data for the strength-ductility enhancement of 316L stainless steel meta-crystal lattice of architected materials by harnessing the non-equilibrium solidification in metal additive manufacturing
<p>Raw (tabular) CALPHAD data for SLM produced 316L stainless steel.</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
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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.