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Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions
<p>These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested:</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 = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic"</p> <p>Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein.</p> <h1>Description</h1> <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></p> <p>The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12</p> <p>To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a></p> <p>If you use these data or model, we would be grateful if you could cite the related papers:</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: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></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: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (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” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (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; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> <p>All the codes and experimental results are in three directories:</p> <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters<br>2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range<br>2.1.1. 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.<br>2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI<br>2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H)<br>2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat<br>2.2.2. WarmStart = True is used to restart an inference<br>2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results<br>2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. 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 7)<br>2.3.2. ResKGEmu.py plots the evolution of elastic properties with time<br>2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat<br>2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code<br>2.3.5. uses local functions plotExp.py<br>2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters<br>3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges<br>3.1.1. 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.<br>3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI<br>3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H)<br>3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat<br>3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves<br>3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time<br>3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.</p>
CNRS Odeillo big solar furnace mockup for 3D printing
<p>Mockup of the big solar furnace in Odeillo, operated by CNRS and hosting the laboratory PROMES since 1969.</p> <p><strong>Now you can 3D print your own solar furnace!! </strong>we printed ours in PLA with a Ender 3 FDM printer.</p> <p><a href="https://www.promes.cnrs.fr">https://www.promes.cnrs.fr</a></p> <p><strong>Check also: </strong></p> <ol> <li>F. Trombe, A. Le Phat Vinh, <em>Thousand kW solar furnace, built by the National Center of Scientific Research, in Odeillo (France)</em>, Solar Energy; Volume 15, Issue 1, May 1973, Pages 57–61. <a href="https://doi.org/10.1016/0038-092X(73)90006-6">https://doi.org/10.1016/0038-092X(73)90006-6</a></li> <li>E. Guillot, R. Rodriguez, N. Boullet, J-L Sans, <em>Some details about the third rejuvenation of the 1000 kWth solar furnace in Odeillo: Extreme performance heliostats</em>, AIP Conf. Proc. 2033, 040016 (2018). <a href="https://doi.org/10.1063/1.5067052">https://doi.org/10.1063/1.5067052</a></li> <li>Bruno Rivoire, & Emmanuel Guillot. (2010). <em>Odeillo's big solar furnace: heliostats field plan (Version 2000)</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.8005925" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8005925</a></li> </ol>
Dataset for Unraveling the Versatility of Carbon Black – Polylactic Acid(CB/PLA) 3D-Printed Electrodes via SustainableElectrochemical Activation
<p>The dataset contains all relevant data and figures regarding the manuscript "Unraveling the versatility of CB/PLA 3d printed electrodes through electrochemical activation".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1A csv file corresponds to data used to plot graphs from Figure 1A etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Supporting Information for "Photothermal Laser Printing of Sub-micrometer Crystalline ZnO Structures"
<p>Supplementary information for the article <strong>"Photothermal Laser Printing of Sub-micrometer Crystalline ZnO Structures"</strong></p> <p>containing a video SEM-EBSD video of the rotating ZnO crystalline angles (mp4 file), raw and processed data of EDX and EBSD files (.bcf), raw and processed SEM and TEM images (.tif .emi .ser), processed images and plots (.svg .png .pdf), and evaluated data results (.txt .csv) .</p> <p> </p> <p>Link to publication: <a href="https://doi.org/10.1002/advs.202410771">https://doi.org/10.1002/advs.202410771</a></p> <p> </p> <p>An explanation of the data files can be found in the <em>Notes.docx </em>document</p>
Chapbooks_HTO: A Knowledge Graph for representing the "Chapbooks Printed In Scotland" (1671 - 1893) following the Heritage Textual Ontology
<p>This Knowlege Graph represents the information of the "<strong>Chapbooks Printed In Scotland"</strong> (years: 1671 - 1893) collection in RDF (ttl format). This dataset comprises more than 3,000 chapbooks printed in Scotland from the 17th to 19th century. They form part of the Lauriston Castle Collection, which was bequeathed to the Library in 1926. It includes some 500 chapbook volumes containing around 5,500 individual items, more than half of which were printed in Scotland. The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/">link</a>. As other NLS data collections, they are originally provided using two XMLs schemas: METS for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO for encoding the OCR text of a page.</p> <p> The KG uses the <a href="https://w3id.org/hto">HTO</a> to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation.</p>
New aesthetic in-house 3D-printed brackets: proof of concept and fundamental mechanical properties
<p>Dataset for all analyses</p>
3D printing lifts the lid on black box instruments
<p>Within analytical chemistry, chemical instruments involve numerous interconnected parts working cohesively towards a specific functionality. However, these highly complex mechanisms cannot be fully depicted with a 2-dimensional textbook model or image, leading to confusion or misconceptions during the learning process. To address this comprehension gap, an array of chemical instrument components have been designed and fabricated with 3D printing to create a hands-on learning experience. The models developed were created in interlocking parts to allow disassembly and investigation of the inner workings of each instrument component. This work produced a series of teaching aids and dynamic models for common instruments including a quadrupole, quadrupole-ion-trap, orbitrap, FTICR mass spectrometer; a GC injection port, FID, and ECD detectors; an HPLC injector; an ICP torch and nebulizer; a Michaelson interferometer; a basic monochromator; and a shatterbox sample preparation device. These models were designed to be interactive and hand-held 1:1 scale models of commercial instrument components. Using these models, students' learning may be expanded from 2 to 3 dimensions; providing hands-on experience with modern laboratory instrumentation impossible with commercial instrumentation. Models were used in a small Analytical Chemistry lecture (<em>n</em> = 8) which demonstrated positive student reception, especially for models with kinematic relations to instrument functionality.</p>
Euclid in print, 1482–1703. A catalogue of the editions of the Elements and other Euclidean works
<p>This catalogue lists, in intention, every edition of one or more works attributed to Euclid printed in any language down to the year 1703. It is intended to improve upon the previous Euclidean bibliographies of Riccardi, Duarte, Thomas-Stanford and Steck, which were compiled before the availability of digital catalogues. It was produced as part of the 2016–18 project 'Reading Euclid', based at the University of Oxford and funded by the Arts and Humanities Research Council of the UK. </p>
3D-Printed Encapsulation of Thin-Film Transducers for Reliable Force Measurement in Biomedical Applications
<p>Data csv</p>
Characterisation of unsteady flow in a 3D-printed Schwarz Diamond monolith using magnetic resonance velocimetry
<p>This data set contains the MRI velocity images presented in the article: Clarke, D, Galvosas, P, Holland, DJ. Characterization of unsteady flow in a 3D-printed Schwarz Diamond monolith using magnetic resonance velocimetry. <em>AIChE J</em>. 2023;e18097. doi:<a href="https://doi.org/10.1002/aic.18097">10.1002/aic.18097</a></p> <p>Images are in the .vtk format and may be viewed using suitable software (e.g. ParaView).</p> <p>*Description edited on 2023/04//21 to include eLocator.</p>
Disposable platform for bacterial lysis and nucleic acid amplification based on a single USB-powered printed circuit board
<p>Recent advances in electronics and microfluidics have enabled several research groups to develop fully integrated, sample-to-result isothermal nucleic acid amplification test (NAAT) platforms for the point of care. However, high component counts and costs have limited translation of these platforms beyond the clinic to low-resource settings—including homes. Many NAATs include complex, multi-component heater electronics based on flex circuits or multiple printed circuit boards (PCBs) to support essential NAAT steps such as lysis, sample deactivation, and nucleic acid amplification. In contrast, current commercial assays for home use, such as those for pregnancy or ovulation that include electronics, typically have just one onboard PCB. This work describes a generalizable strategy to integrate all heaters and the electronics needed to control them onto a single low-cost, USB-powered PCB. We built a multiplexable disposable NAAT ("MD NAAT") platform that applies these principles, integrating small-area heaters that heat small regions to near-boiling (for pathogen lysis and deactivation) and large-area heaters (for amplification) on the same PCB. We show that both classes of heaters have high intra-board and inter-device reproducibility despite only heating a NAAT cartridge from below. We validated the small-area heaters by lysing methicillin-resistant <em>Staphylococcus</em> <em>aureus</em> (MRSA) cells and the large-area heaters by performing two types of isothermal NAATs (isothermal strand displacement amplification (iSDA) and loop-mediated isothermal amplification (LAMP)). These results demonstrate the merit of integrating NAAT heaters and control electronics onto a single printed circuit board and are a step toward translating NAATs to the home.</p>
ERRATAS database of editorial principles and practices in printed editions of historical correspondence
<p>The ERRATAS database is the primary output of the ERRATAS project, which surveyed all the sources of the <a href="http://www.helsinki.fi/varieng/CoRD/corpora/CEEC/"><em>Corpora of Early English Correspondence</em></a> (CEEC-400) in order to investigate their editorial principles and practices. These sources are mostly printed editions of English historical correspondence.</p> <p>The data in the ERRATAS database consists of surveys of:</p> <ul> <li>editorial principles (explicit statements or anything resembling such);</li> <li>editorial practices (textual features that can be found in the edited texts);</li> <li>editorial work (evidence of contributors other than the stated editor(s)).</li> </ul> <p>This updated release of the database is in the form of <strong>two MS Excel files</strong>, one of which includes data entry forms for surveying editorial principles and practices.</p> <p>This release also comes with documentation, in the form of <strong>three pdf files</strong>:</p> <ul> <li>A <strong>manual</strong> to the database, containing a brief history of the project, and a section demonstrating with several graphs what kinds of data can be extracted from the dataset;</li> </ul> <p>and two documents which place the database into the context of its development, but more importantly facilitate the application of the ERRATAS method to survey further material:</p> <ul> <li>A <strong>checklist</strong> of textual features (<a href="https://zenodo.org/record/1187074">an earlier version can be found here</a>); and</li> <li><strong>Guidelines</strong> for surveying editorial principles and edited texts following the ERRATAS system.</li> </ul> <p><strong>Related material:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3855451"><em>Corpus of Editorial Principles (in CEEC-400 Sources)</em></a> – pdfs of the editorial principles in the editions surveyed</li> <li><a href="https://www.doi.org/10.5281/zenodo.4134471">Editions and other sources used in the <em>Corpora of Early English Correspondence</em> (CEEC-400)</a> – detailed bibliography of the sources surveyed for the ERRATAS database</li> <li>Sairio, Anni, Samuli Kaislaniemi, Anna Merikallio & Terttu Nevalainen. 2018. “Charting orthographical reliability in a corpus of English historical letters”. <em>ICAME Journal</em> 42: 79–96. DOI: <a href="http://doi.org/10.1515/icame-2018-0005">doi.org/10.1515/icame-2018-0005</a>. Open access.</li> </ul> <p><strong>Version history:</strong></p> <p>26.5.2020 Version 0.9 – Database unfinished (due to coronavirus restrictions). The current data has been checked twice. Material to be added in v1.0: database manual; database survey guidelines.</p> <p>14.4.2023 Version 1.1 – Database finished, after conversion to MS Excel. Dataset checked, corrected, amended, edited, and cleaned for release. Documentation created. Survey guidelines and data survey forms included in release.</p>
Setup of a 3D printed wind tunnel: application for calibrating bi-directional velocity probes used in Fire Engineering Applications
<p>The research presented here focuses on the development of a 3D printed wind tunnel and the relevant equipment to be used for calibrating bi-directional velocity probes (BDVP). BDVP are equipment to be used for measuring velocity flow by determining the pressure difference of hot gases generated during fires. The manufactured probes require calibration to determine the calibration factor to achieve precise measurement. The calibration is usually performed in wind tunnels which can be difficult to access due to costs, complexity and the various pieces of equipment required. The aim of the current study is to develop and assemble an inexpensive and easy-to-build bench-scale wind tunnel, with a data-logging system and fan control functionalities for fast and effective calibration of BDVP. A 3D printer with a PET-G filament is used, able to produce parts for the wind tunnel system which are durable and easy to handle and assemble. The system additionally includes an Arduino-based measuring unit with a hot-wire anemometer and temperature correction: Rev. P. This takes precise measurements; continuously logging data on a computer through a USB interface and capable of saving data on an SD card. This design provides users with parameters of velocity flow up to 4 m/s with standard deviation of 1.2 % and turbulence intensity of 1 %. The main advantages of this wind tunnel are its simplicity to build and portability.</p> <p>The dataset contains design files for 3D printing of the wind tunnel, BOM and wiring of electronic components.</p> <p>The project is prototype under development and authors are not responsible for any damages or injuries caused by inappropriate construction or operation. This source is distributed WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING OF MERCHANTABILITY, SATISFACTORY QUALITY AND FITNESS FOR A PARTICULAR PURPOSE.</p>
Rebar-Free 3D Printing of Transportation Infrastructure
<p>This study investigates the fresh and hardened-state properties of printing mixtures including different dosages of steel fibers, especially at higher dosages which have not been investigated before. This study also considers the effects of other parameters such as sand-to-powder ratio and the limestone content on the properties of steel fiber reinforced printing materials, to reduce the Portland cement content which has a high carbon footprint. The obtained experimental results revealed that high-performance materials incorporating up to 2.5% steel fibers (by volume) can be successfully 3D printed. The mechanical properties of the reinforced mixtures improved significantly at high fiber dosages (2% and 2.5% vol.). By incorporating 2% and 2.5% vol. of steel fibers, the compressive strength results showed up to 109% and 50% increase in strength compared to the control mixture, at 7 days and 28 days, respectively. In addition, significant improvement in the flexural strength, tensile strength and toughness of printing materials were achieved at high dosages of steel fiber inclusion. The obtained results also reveal that the orientation and alignment degree of steel fibers in the composite is affected by different parameters such as the binder content and nozzle design. In addition, an electromechanical module was successfully designed, developed, and implemented with an existing printing system to enable a semi-automated screw insertion process in parallel with the layer deposition process. This highlights the feasibility of a fully automated layer deposition and reinforcement process for future real-life construction projects. </p>
Timelapse of Indoor Monitoring of 3D printed Panels installed in Living Prototypes Exhibition in Aedes Forum, Berlin
<p>The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The timelapse is part of Indoor Monitoring Framework developed for Living Prototype Exhibition at Aedes Forum in Berlin. </p>
Evaluation of Fresh and Hardened Properties of 3D-Printed Engineered Cementitious Composites (ECC) Designed for Sustainable and Resilient Infrastructure Systems
<p>3D concrete printing is a cutting-edge construction technique that has the potential to revolutionize the construction industry due to cost-saving in terms of labor and formwork costs, efficiency in construction, lower safety-related risks, and a higher degree of automation. However, several issues still make its adoption relatively slower on a large scale. Engineered cementitious composites (ECC), a class of ultra-high-strength concrete, can be a potential solution to some problems, such as reinforcement and durability. The preliminary phase of a Tran SET project focused on the design of 3D printable ECC, considering the concrete mix design proportions and their potential effects on fresh and hardened properties to achieve an optimized printable ECC mix. The type and content of various concrete ingredients such as cement, admixtures, aggregates, and fibers have considerable influence on the several properties in the fresh and hardened state. The replacement levels for cement were 0%, 50%, and 75% for mineral admixture, 10% for silica fume, and 0.4% for nano-clay by cement mass. Locally available fine aggregates were also used at 25% and 40% by mass of binder. In addition, the influence of fiber types and contents was also investigated. Two types of fibers, polyethylene (PE) and polyvinyl alcohol (PVA) fibers, were used at different levels, such as 0%, 1%, 1.5%, and 2% of the total volume of the mix. Moreover, four mixes, including FA50-MC, FA40SF10-MC, S50-MC, and FA40MK10-MC, were evaluated in terms of mechanical performance (flexural strength and direct tensile strength). The flowability of the ECC mixes was reduced with the incorporation of slag, metakaolin, higher aggregate content, and PE fibers. The 2% of both PVA and PE fibers had a reduction in compressive strength as compared to the low fiber content. Moreover, the PE-ECC exhibited superior tensile ductility as compared to the PVA-ECC due to the more fiber bridging by the PE fibers at the crack interface.</p>
Comparative Analysis of 3D Printed Bridge Construction in Louisiana
<p>A construction 3D printing system could result in automated infrastructure development at reduced cost and time, significantly boosting overall productivity. Although there has been a growing interest in using construction 3D printing for projects such as house construction, implementing this innovative technology for infrastructure development, particularly bridge construction, has not been investigated as extensively. This study aims to compare the environmental impact of precast and 3D concrete printing (3DCP) techniques with a pedestrian bridge case study, located in Louisiana, where the bridge elements were 3D printed off-site and then transported and assembled on the bridge site. A detailed cradle-to-site life cycle assessment has been performed from the standpoint of material, construction, and installation stages, using an open-source software called OpenLCA. The results of this study showed that the mixtures commonly used in 3DCP have a higher negative environmental impact compared to the precast method due to the higher percentage of cement used in these materials. However, since 3DCP used less material than the precast technique, there is no significant difference in the environmental impact of the total concrete used between the 3DCP and precast bridges. In addition, due to the use of reinforcement and formwork in the precast technique, the environmental impact of the total materials used in the precast bridge was more adverse than the 3DCP bridge. Notably, due to use of electricity for printing, the negative environmental impact of the construction process in 3DCP was significantly higher than in the precast technique. Finally, the total carbon dioxide equivalent emitted during the construction of the 3DCP bridge was 80% of the precast bridge.</p>
A dataset on in-situ electromagnetic wave integrity control of selective laser fusion printed parts using Machine Learning
<p><strong>DeepSLM Dataset</strong></p> <p>This dataset contains signals from metal parts printed in 3D using SLM (Selective laser melting) technology, produced as part of the DeepSLM project:<em> In-situ monitoring of Selective-Laser-Melted Ti6Al4V Parts Using Eddy Current Testing and Machine Learning</em>.</p> <p><em>Sensor and data collection</em></p> <p>An impedance-based non-destructive testing device is installed on the LPBF coating device. It is in the form of sensors with the following characteristics:</p> <p><em>Spectrum of analysis of the sensors</em></p> <ul> <li>Minimal length: 3-5 mm</li> <li>Minimal width acquisition: 2-3 mm (Without borders effects)</li> <li>Minimal number of layers: 50 layers</li> <li>Signal minimal penetration depth (TiAl<em>6</em>V<em>4</em>/875kHz) = 1.5 mm</li> </ul> <p>The sensors record the signal during printing, while simultaneously sending it to a computer via Bluetooth. The signals are first stored in the database. Next, a dataset is constructed from the extracted signals related to each part, and from the added semi-automatic annotations which describe the porosity of the part and layers.</p> <p><em>Annotations</em></p> <p>In the annotation process, there are 2 different types of labels and a metadata summary for these prints:</p> <p>1. Archimedean porosity measurement for the whole part<br>2. Layer porosity obtained after image processing applied on the resulting metallography images<br>3. Print-related metadata such as print parameters, part dimensions, layer thickness, etc</p> <p>Size: 66 parts<br>Set of printing parameters: 250</p> <p> --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Dataset organization</strong></p> <p>The dataset is composed of 7 different printings and is organized as follows:<br>- signals: contains collected signals<br>- metallography: contains porosity extracted by metallography<br>- archimedean: contains porosity extracted by archimedean weighing<br>- metallography-images: contains metallography process images<br>- metadata.csv: contains print metadata (print parameter, part name, strategy, etc.)</p> <p>Each printed part has a unique ID ranging from 0 to 65. Each sub-part has an ID which is the ID of the base part suffixed with the sub-part position: 14_1 is the first sub-part of part 14. This nomenclature is uniform throughout the dataset.</p> <p>The README.md file in the dataset provides more detailed information on the structure of the dataset and the format of each of the files making up the dataset.</p> <p> </p> <p><strong>Relative article</strong></p> <p><a title="https://doi.org/10.1007/978-3-031-47784-3_18" href="https://doi.org/10.1007/978-3-031-47784-3_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-47784-3_18</a></p> <p>Sallem, H., Ghorbel, H., Goffinet, E., Cinna, A., Pralong, J., Wicht, J., & Revaz, B. (2023, May). In-Situ Monitoring of Selective Laser Melted Ti–6Al–4V Parts Using Eddy Current Testing and Machine Learning. In <em>Advances In Additive Manufacturing Conference</em> (pp. 139-148). Cham: Springer Nature Switzerland.</p>
On-Demand Inkjet Printed Hydrophilic Coatings for Flow Control in 3D-Printed Microfluidic Devices Embedded with Organic Electrochemical Transistors
<p>Dataset of the journal article "On-Demand Inkjet Printed Hydrophilic Coatings for Flow Control in 3D-Printed Microfluidic Devices Embedded with Organic Electrochemical Transistors", article DOI: 10.1002/admt.202300127</p>
Characterisation of moisture sorption of materials used in 3D printing
<p>Investigation of environmental effects on properties of polymers and 3D printed structures was performed. The research methodology of moisture sorption and swelling processes was developed and implemented. Full-scale experiments were performed on 12 materials. Degradation of filaments’ mechanical properties under moisture effect was evaluated. A complex analysis of the results was performed, and sorption characteristics of filaments were defined.</p>
ScienceDex guides
Understand access before you commit
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.