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717 results for “manufacturer”
Dataset for paper entitled, 'The potential for grain refinement of Wire-Arc Additive Manufactured (WAAM) Ti-6Al-4V by ZrN and TiN inoculation'
<p>Dataset for paper entitled, 'The potential for grain refinement of Wire-Arc Additive Manufactured (WAAM) Ti-6Al-4V by ZrN and TiN inoculation'. Abstract: Wire-Arc Additive Manufacturing (WAAM) of large near-net-shape titanium components has the potential to reduce costs and lead-time in many industrial sectors including aerospace. However, with titanium alloys, such as Ti-6Al-4V, standard WAAM processing conditions result in solidification microstructures comprising large cm- scale, <001> fibre textured, columnar β grains, which are detrimental to mechanical performance. In order to reduce the size of the solidified β-grains, as well as refine their columnar morphology and randomise their texture, two cubic nitride phases, TiN and ZrN were investigated as potential grain refining inoculants. To avoid the cost of manufacturing new wire, experimental trials were performed using powder adhered to the surface of the deposited tracks. With TiN particle additions, the β grain size was successfully reduced and modified from columnar to equiaxed grains, with an average size of 300 µm, while ZrN powder was shown to be ineffective at low addition levels studied. Clusters of TiN particles were found to be responsible for nucleating multiple β Ti grains. By utilizing the Burgers orientation relationship, EBSD investigation showed that a Kurdjumov-Sachs orientation relationship could be demonstrated between the refined primary β grains and TiN particles.</p> <p>Paper doi: https://doi.org/10.1016/j.addma.2021.101928</p>
Replication Archive for "Chaos Before Order: Productivity Patterns in U.S. Manufacturing"
<p>This archive includes the public-use data and STATA code to replicate the analyses based on the public-use data in the referenced paper.</p>
Data and R files for the analysis of the innovative capacity and the network position of national manufacturing industries in world production
<p>Data and R files for the reproducibility of the results obtained in Kim and Ozaygen, Analysis of the innovative capacity and the network position of national manufacturing industries in world production.</p> <p>It also includes an R/Shiny application which runs at <a href="https://awekim.shinyapps.io/Manuf_shiny_R/">https://awekim.shinyapps.io/Manuf_shiny_R/ </a></p>
Dataset for paper entitled 'Microstructure transition gradients in titanium dissimilar alloy (Ti-5Al-5V-5Mo-3Cr/Ti-6Al-4V) tailored wire-arc additively manufactured components'
<p>Dataset for paper entitled 'Microstructure transition gradients in titanium dissimilar alloy (Ti-5Al-5V-5Mo-3Cr/Ti-6Al-4V) tailored wire-arc additively manufactured components'. doi: <a href="https://doi.org/10.1016/j.matchar.2021.111577">https://doi.org/10.1016/j.matchar.2021.111577</a></p>
A case study of the dimensional effects in an additive manufacturing process with multiple operations
<p>This dataset provides a case study for the dimension changes of the prints in an additive manufacturing process with multiple operations. The operations are Stereolithography (SLA) printing and its two post-processing processes (washing and post-curing). The dataset contains the measured dimensions of the prints after each operation, which allows the modeling of each operation individually.<br> </p>
Wear resistance of an additively manufactured high-carbon martensitic stainless steel
<p>Dataset supporting the study:</p> <p>'Wear resistance of an additively manufactured high-carbon martensitic stainless steel', E. Iakovakis et al., 2022</p> <p>The dataset contains the hardness, the friction values, the x,y,z coordinates from profilometry measurements for the wear rate calculation and the generation of the surface profile map of the track and the hardness data to generate the cross-sectional hardness map of the wear tracks of an additive manufactured (EBM-processed) high-carbon martensitic stainless steel (Vibenite®350). Most specifically, it incudes the following:</p> <ul> <li>Hardness measurement file (Hardness vibenite350 HV5.csv): The .csv file reports the hardness for 30 measurements.</li> <li>Reciprocating tests files (Vibenite 350 cof1/2/3 alumina 3N/10N.csv): The .csv files report the testing parameters and the friction forces of the reciprocating dry wear tests.</li> <li>Profilometry measurements files (Vibenite 350 profilometry1/2/3 3N/10N.dat and Vibenite 350 profilometry data map_5umscan at 3N/10N): The .dat files report the x,y,z coordinates of the wear track used for the calculation of the Vibenite®350 and for the generation of the surface profile maps.</li> <li>Hardness data in the cross-section of the wear track files (Vibenite 350 hardness data map at 3N/10N.spe): The .spe files report the hardness values measured in the cross-section of the wear track and used to generate the hardness maps.</li> <li>SEM-EDX analysis of the wear track at 10N</li> </ul> <p>The dataset also includes tribological data for an additive manufactured (EBM-processed) high carbon martensitic tool steel (Vibenite®150) which is used to compare the wear rate data of the EBM-processed martensitic stainless steel. Most specifically, it incudes the following:</p> <ul> <li>Reciprocating tests files (Vibenite 150 cof1/2/3 alumina 3N/10N.csv): The .csv files report the testing parameters and the friction forces of the reciprocating dry wear tests (CoF-sliding distance plot included).</li> <li>Profilometry measurements files (Vibenite 150 profilometry1/2/3 3N/10N.dat ): The .dat files report the x,y,z coordinates of the wear track used for the calculation of the wear rate for Vibenite®150 and for the generation of the surface profile maps (included maps for 3 N and 10N).</li> </ul>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Investigation on anodes from anode supported cells manufactured by Solidpower and aged by IEES-BAS with electrochemical characterization.
<p>samples are mounted in epoxy-resin and polished up to 250nm using diamond paste on specific tissue (MD-NAP, Struers). Observation by SEM (ZEISS EVO 40) using backscattered electrons as the contrast method. Chemical analysis performed using EDS after calibration with Co standard and using ZAF algorithm correction. The standard magnification is 5000. The pictures are further threated using ImageJ software for quantitative image analysis. Porosity and Nickel particles are highlighted by using Coreldraw software image enhancements.</p>
DATA - Modern manufacturing enables magnetic field cycling experiments and parahydrogen induced hyperpolarization with a benchtop NMR
<p>Datasets and software for the publication "Modern manufacturing enables magnetic field cycling experiments and parahydrogen induced hyperpolarization with a benchtop NMR"</p>
Data for 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'
<p>All data supporting the paper 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'. Includes gifs (movies) of the high temperature in-situ EBSD experiments and the scripts used for analysis.</p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers - Dataset
<p>This dataset contains data from 43 subjects following two authentic manufacturing protocols and their self-reported fatigue score. The system employs 6 wearable sensors to continuously track vital and locomotive signs from multiple body locations.</p> <p> </p> <p>The goal of the experiment is to predict fatigue trends in a subject, while they are asked to perform pre-defined manual tasks simulating a manufacturing environment, using data from soft, flexible, wearable sensors and a vision system. The tasks in this study are repetitive and physically exerting involving intricate steps taken in real manufacturing settings. The iterative nature of the tasks facilitates comparative analyses of distinct temporal segments to characterize fatigue. The two manufacturing tasks are (1) Task Composite: Composite Sheet Layup, and (2) Task Harnessing: Wire Harnessing. The task protocol requires the subject to wear sensors to monitor vital and locomotive signs continuously. Additionally, we incorporate a weighted vest to exaggerate the induced fatigue in a reasonable duration for the study to mimic a full shift for a manufacturing worker. Each task consists of two rest periods of 5 minutes each at the start and end, as well as five segments of physical tasks. On average, each task takes a total of 1 hour. Before each data segment, the subject fills out a survey form to indicate their current fatigues as per the Borg scale. </p>
Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models
<p>The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data:</p> <p>Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures.</p> <p>Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths.<br><br>The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design. </p>
Backscatter tuned laser absorption spectroscopy in additive manufacturing
<p>Raw data accompanying our paper </p> <p><strong>Backscatter absorption spectroscopy for process monitoring in powder bed fusion</strong></p> <p> </p>
Digital technologies for improving productivity in food manufacturing - graphical capture
<p>The Centre for SMART at Loughborough University collaborated with the Internet of Food Things Network Plus to host an event that targeted players in the food manufacturing industry, as well as policy makers, and aimed to provide attendees with the opportunity to learn from companies and technology providers on best practices to successfully implement current digital technologies in food manufacturing and to discuss policy and regulatory challenges to the uptake of such technologies within the UK Food Sector.</p> <p>A full report will be available later, but this drawing was produced by Becky James from Natalka Design who was commissioned to capture the complexity of the presentations and discussions that took place at the event.</p> <p>We therefore acknowledge the contribution that all attendees at the event made to this illustration.</p>
Graphical capture from: Digital technologies for improving productivity in food manufacturing
<p>The Centre for SMART at Loughborough University collaborated with the Internet of Food Things Network Plus to host an event that targeted players in the food manufacturing industry, as well as policy makers, and aimed to provide attendees with the opportunity to learn from companies and technology providers on best practices to successfully implement current digital technologies in food manufacturing and to discuss policy and regulatory challenges to the uptake of such technologies within the UK Food Sector.</p> <p>A full report will be available later, but this drawing was produced by Becky James from Natalka Design who was commissioned to capture the complexity of the presentations and discussions that took place at the event.</p> <p>We therefore acknowledge the contribution that all attendees at the event made to this illustration.</p>
Supplemental Material to Article "Development of thermal residual stresses during manufacture of wind turbine blades"
<p>This set supplements the figure data to the article "Development of thermal residual stresses during manufacture of wind turbine blades", DOI: .</p>
Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing
<p>The Excel file is structured to provide a comprehensive overview of the design of experiments (DoE) for the research project titled <em>"Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing"</em>, conducted under the BALSAM project. This research specifically contributes to deliverables D1.2 and D3.1.</p>
Dataset of Listed A-Share Manufacturing Companies in China (2011-2022) for ESG and Innovation Capability Analysis
<p>This dataset was constructed for a study on the relationship between Environmental, Social, and Governance (ESG) scores and innovation capability among listed A-share manufacturing companies in China. The data were sourced from the CMSAR database, focusing on companies within the equipment manufacturing industry, while financial industry samples were excluded. The final dataset comprises 851 samples from 84 listed companies spanning the period from 2011 to 2022. Continuous variables in the dataset were winsorized at the 1% level to mitigate the influence of outliers.</p>
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>
Utilization of high-performance concrete mixtures for advanced manufacturing technologies
<p><span>The presented experimental program focuses on the design of high-performance dry concrete mixtures, which could find application in advanced manufacturing technologies, for example additive solutions. The combination of high-performance concrete (HPC) with advanced or additive technologies provides new possibilities for constructing architecturally attractive buildings with high material requirements. The purpose of this study was to develop a dry mixture made from high-performance concrete that could be distributed directly in a advanced or additive technologies of solutions in the pre-prepared condition with all input materials (except for water) in order to reduce both financial and labor costs. This research specifically aimed to improve the basic strength characteristics, including mechanical (assessed using compressive strength, tensile splitting strength, and flexural strength tests) and durability properties (assessed using tests of resistance to frost, water, and defrosting chemicals), of hardened mixtures, with partial insight into the rheology of fresh mixtures (consistency as assessed using the slump-flow test). Additionally, the load-bearing capacity of the selected mixtures in the form of specimens with concrete reinforcement were tested using a three-point bending test. A reference mixture with two liquid plasticizers—the first based on polycarboxylate and polyphosphonate and the second based on polyether carboxylate—was modified using a powdered plasticizer, based on the polymerization product Glycol, to create a dry mixture; the reference mixture was compared with the developed mixtures with respect to the above-mentioned properties. In general, the results show that the replacement of the aforementioned liquid plasticizers by a powdered plasticizer based on the polymerization product Glycol in the given mixtures is effective up to 5 % (of the cement content) with regard to the mechanical and durability properties. The presented work provides an over-view of the compared characteristics, which will serve as a basis for future research into the development of additive manufacturing technologies in the conditions of the Czech Republic while respecting the principles of sustainable construction.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.