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717 results for “manufacturer”
Fig. 2 in Integrated pest management of the German cockroach (Blattodea: Blattellidae) in manufactured homes in rural North Carolina
Fig. 2. Trend of monthly mean cockroach trap catches per participant over the sampling period (Oct 2011 to Mar 2014) during the Pre-IPM, IPM-education, and IPM-education plus bait intervention phases.
Fig. 3 in Integrated pest management of the German cockroach (Blattodea: Blattellidae) in manufactured homes in rural North Carolina
Fig. 3. Total population levels and decrease (%) of German cockroach populations from all participants during the Pre-IPM, IPM-education, and IPM-education plus bait intervention phases for 6 manufactured homes in rural North Carolina.
Fig. 1 in Integrated pest management of the German cockroach (Blattodea: Blattellidae) in manufactured homes in rural North Carolina
Fig. 1. Population fluctuations of German cockroaches from Oct 2011 to Mar 2014 in 6 manufactured homes in rural North Carolina during the Pre-IPM, IPMeducation, and IPM-education plus bait intervention phases.
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 10. The educational blog on Google+ Time Maps page – the glass manufacturing techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D<br> reconstruction), slightly different from those available on the Time Maps web site, but containing<br> the same information. The children had to make a little effort to relate this information with the one<br> presented on the site, to make a connection between the questions, the fragments from videos at<br> which the answers referred to and the information from the site.<br> The set of questionnaires lead the school children through the majority of data offered by the<br> web site regarding to the two historical periods (Figures 9, 10).</p>
Detection of a Timing Channel in an UPPAAL Model of a Cyber-Manufacturing System
<p>Model of a cyber-manufacturing system for the UPPAAL model checker, including a mitigation of a timing channel.</p>
Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment — IROS 2019
<p>This video accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Robot view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>robot view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Fibre view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>fibre view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Techno Economic Analysis of Biogas Purification by Methane and Acetate Manufacturing CO2 to CH4 2024 SuppInfo
<p>Techno Economic Analysis calculations for manuscript of Biogas Purification by Methane and Acetate Manufacturing to convert CO2 to CH4: Wastewater treatment plants have two persistent financial and energetic drains, the carbon dioxide content of biogas, which limits its commercial sale, and the presence of trace organics in the wastewater effluent, which damages the aquatic ecosystem. Biogas is a renewable methane resource that is underutilized due to the variable CO2 content (~40%). Biogas is energy intensive to purify and limited by the economy of scale (>8.85 GJ/hour) to large-scale purification methods, thus small-scale processes require development. Electrocatalytic microbes native to wastewater have been shown to convert CO2 to CH4 and acetate, however complete conversion of the CO2 content to CH4 is energy intensive. Here we show a low power bioelectrochemical fuel cell design to purify biogas to pipeline quality methane (98%), manufacture methane and/or acetate, and remove trace organics, using HCO3- as the transport charge carrier from dissolved CO2 from the biogas through an anion exchange membrane. This decreased the power required to separate CO2 from methane in biogas on a molar basis, resulting in a net energy recovery similar to current industrial systems. Magnesium anode use resulted in an energy positive system. Tests evaluated the influence of cathode potential on the current density, HCO3- ion flux and the rates and efficiencies of methane production, resulting in optimization at -0.7V vs Standard Hydrogen Electrode (SHE). A techno-economic analysis modeled a positive return on investment for scaled-up production to purify small biogas streams that are otherwise financially unrecoverable. Carbon sequestration by production of methane, acetate and solid fertilizers demonstrated profitable and energy efficient waste-to-resource conversion.</p>
Residual stress in 316L stainless steel benchmark additively manufactured arches determined by neutron diffraction and snychtron X-ray diffraction
<p>Residual stress data recorded as part of the EASI-STRESS project. </p> <p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon and ID15A at ESRF). The data is for residual stress in a benchmark 316L stainless steel arch manufactured by laser powder bed fusion using a MetalFAB1 additive manufacturing machine. The arch is square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for two lines is presented: line 1 runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction). Line 2 runs along to the x axis at a depth of 1 mm into the arch. </p> <p>The stress-free reference used was a reference comb cut from an identically made arch.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 ´ 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
As built and post heat treatment residual stresses in 316L stainless steel additively manufactured benchmark arches
<p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon). The data is for residual stress in a benchmark 316L stainless steel arches manufactured by laser powder bed fusion using MetalFAB1 and EOS M290 additive manufacturing machines. These arches are square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for part in both an as built and after a 700 °C 2 hour heat treatment are presented. The measurement line runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction).</p> <p>The stress-free reference used was a reference comb cut from an identically made arches. For heat treated samples, heat treated combs were used.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 x 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
Batch or Flow Manufacturing in Chemistry? Problems & Opportunities in Switching to Flow
<p>Overview of continuous processing in chemistry and how challenges and opportunities arise in moving away from batch technologies.</p>
Dataset of Manufacturing Tasks - DMT22
<p>Description: The dataset of manufacturing tasks (DMT22), features five representative assembly primitive actions (PickUp, Place, Screw, Idle and Hold), collected from 5 different subjects featuring 3 different objects, a Wii remote, a hard disk drive, and an electric screwdriver.</p> <p>Available Data: The DMT22 dataset contains recorded data from an event camera (DAVIS 240C), a depth camera (Intel RealSense D435) and a magnetic tracking system (Polhemus Liberty).</p> <ul> <li>The event camera captured events and APS frames with a resolution of 240 pixels × 180 pixels. DAVIS APS data are captured at 30 fps (frames per second). Event data is available in the original recorded format, .aedat, together with a .txt file containing label and its corresponding timestamp range, and a .mat file which contains the information from the events (coordinates x and y, timestamp and polarity).</li> <li>The electromagnetic tracker was used to capture pose data, at 20 Hz, from a sensor attached to the subject’s<br> wrist. The data is available in .mat format. It contains 6DOF data (position and orientation) followed by the timestamp of measured data (relative to the start of the recording).</li> <li>RGB-D camera data is available, at 30fps, in the original recorded format, .bag.</li> </ul> <p>The dataset features a total of 72 recordings: 4 right-handed subjects × 3 tasks × 4 performances + 1 left-handed subject × 2 configurations (left and right) × 3 tasks × 4 performances. Each task recording lasts approximately 25 seconds.</p> <p> </p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Advanced photochemical processes for the manufacture of nanopowders: an evaluation of long-term pilot plant operation
<p>Dataset for the publication:</p> <p>Advanced photochemical processes for the manufacture of nanopowders: an evaluation of long-term pilot plant operation</p> <p>React. Chem. Eng., 2022, 7, 968–977</p> <p>DOI: 10.1039/d1re00374g</p>
Data from: Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing"</p>
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
Dataset for Additive Manufacturing of Porous Biominerals
<p>This dataset contains results of rheology, SEM, polarized microscopic and X-ray tomography pictures,XRD results and mechanical testing. There are also results of EDX mapping and 2D scanning synchrotron XRD maps.</p>
Microcoils manufacturing process H2020 UWIPOM2 PROJECT
<p>Microcoils manufacTuring process</p> <p>https://www.youtube.com/watch?v=gwXPUAHZKFk&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Miniaturized helical antenna manufacturing process - H2020 UWIPOM2 PROJECT
<p>Miniaturized helical antenna manufacturing process</p> <p>https://www.youtube.com/watch?v=Va-iSQlFMnY&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</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)
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.