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77 results for “additive manufacturing”
A uniaxial hysteretic superelastic constitutive model applied to additive manufactured lattices - data and postprocessing tools
<p>This data set contains all result data obtained during the implementation of an uniaxial hysteretic superelastic constitutive model and its application to additive manufactured lattices.</p> <p>Furthermore, it contains all ABAQUS .inp files, the implemented subroutine of the hysteretic superelastic constitutive model, diagrams generated from the data, as well as postprocessing tools for generating the diagrams.</p>
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
Contour method dataset for as-deposited and rolled wire+arc additive manufacturing Ti–6Al–4V components
<p>This is an archive of the raw metrology of the EDM cut surface data files used for the contour method analysis of Wire+Arc Additive Manufacture (WAAM) Ti6Al4V components appearing in: "Residual stress of as-deposited and rolled wire+arc additive manufacturing Ti–6Al–4V components" by F. Martina, M. J. Roy, B. A. Szost, S. Terzi, P. A. Colegrove, S. W. Williams, P. J. Withers, J. Meyer and M. Hofmann.</p> <p>The files are described by their filenames and side of each EDM cut. For example, 'Control_1.dat' refers to one side of the cut performed on the as-deposited specimen, while '50kN_1.dat' refers to one side of a specimen rolled at 50 kN load, etc.</p> <p>Data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p>
Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing
<p>With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the <em>in situ</em> printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated <em>in situ</em> strain sensing elements, showed that material selection affects the sensor signal significantly.</p>
A soft pneumatic actuator with integrated deformation sensing elements produced exclusively with extrusion based additive manufacturing
<p>In recent years, soft pneumatic actuators have come into the spotlight because of their simple control and the wide range of complex motions. To monitor the deformation of soft robotic systems, elastomer-based sensors are being used. However, the embedding of sensors into soft actuator modules by polymer casting is time consuming and difficult to upscale. In this study, it is shown how a pneumatic bending actuator with an integrated sensing element can be produced using an extrusion-based additive manufacturing method, e.g., fused deposition modeling (FDM). The advantage of FDM against direct printing or robocasting is the significantly higher resolution and the ability to print large objectives in a short amount of time. New, commercial launched, pellet-based FDM printers are able to 3D print thermoplastic elastomers of low shore hardness that are required for soft robotic applications, to avoid high pressure for activation. A soft pneumatic actuator with the in situ integrated piezoresistive sensor element was successfully printed using a commercial styrene-based thermoplastic elastomer (TPS) and a developed TPS/carbon black (CB) sensor composite. It has been demonstrated that the integrated sensing elements could monitor the deformation of the pneumatic soft robotic actuator. The findings of this study contribute to extending the applicability of additive manufacturing for integrated soft sensors in large soft robotic systems.</p>
Effect of MgO sintering additive on mullite structures manufactured by fused deposition modeling (FDM) technology
<p>An optimized recipe for 3D printing of Mullite-based structures was used to investigate the effect of MgO sintering additive on the processing stages and final ceramic properties. To achieve dense 3:2 mullite, ceramic filaments were prepared based on an alumina powder, a methyl silicone resin, EVA elastomeric binder and MgO powder. Using 1 wt% MgO and a dwell time of 5 h at 1600 °C, a dense mullite structure could be obtained from filaments with a diameter of 1.75 mm. Ceramic structures with and without sintering additive were printed in vertical and horizontal direction, to investigate the effect of printing direction on mechanical strength after sintering. Using four-point bending test, it was demonstrated that by using MgO, the printing orientation did not affect the mechanical strength significantly anymore. The low Weibull modulus could be explained by the closed porosity that emerge during the degassing of the preceramic polymer due to cross-linking.</p>
Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction
<p>The dataset contains raw data that complements the article:</p> <p>High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081.</p> <p>C. Iffelsberger and M. Pumera*</p> <p>https://doi.org/10.1039/D1TA05581J</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering
<p>A dataset for the publication: T. Puttonen, M. Salmi, J. Partanen, Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering, 2021.</p> <p>The paper studies the mechanical properties and fracture mechanics of Additive Manufacturing (AM) polyamide 12 (PA12) in two build orientations exposed to a 1500-hour accelerated weathering cycle (ISO-4982-3) followed by tensile testing (ISO-527). Fracture surfaces of X and Z build orientation AM PA12 and X build orientation AM glass-filled PA12 were studied with scanning electron microscopy. The tested AM materials were PA12, glass-filled PA12, and carbon-reinforced PA12. The reference materials cut from sheet included glass-filled and molybdenum disulfide-filled PA66, PMMA, ABS, PC, and cast PA12.</p> <p>The dataset contains:</p> <p>- Full tensile test results in PDF format, and individual CSV files</p> <p>- A python script for tensile CSV data plotting</p> <p>- Overall pictures of all samples after tensile tests</p> <p>- 3D models and drawings for tensile samples, manufacturing files for a custom QUV holder assembly</p> <p>- SEM images of fracture surfaces for AM polyamide 12 (SLS), X and Z build orientation, and glass-filled polyamide 12 (SLS) in the X build orientation</p> <p> </p> <p>Version history:</p> <p>1.0.1: A partially corrupted version of the tensile test results PDF file replaced (Tensile_test_results.pdf)</p>
Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load
<p>The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.</p>
Metal Additive Manufacturing Open Repository
<p><strong>Metal Additive Manufacturing Open Repository</strong></p> <p>This dataset gathers data from different parts of Additive manufacturing processes (Laser metal deposition - LMD, and Wire-arc additive manufacturing - WAAM). The dataset covers not only the process data, but also the design, NDT (Non-Destructive Testing) and dimensional inspection.</p> <p><br> <strong>Motivation</strong></p> <p>The industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. The presented dataset is part of an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain.</p> <p>The goal is to develop a new manufacturing methodology capable of ensuring the manufacturability, reliability and quality of a target metal component from initial product design via Direct Energy Deposition (DED) technologies, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatibility of the process.</p> <p>To that end, we present the Metal Additive Manufacturing Open Dataset, the first holistic dataset for AM manufacturing, covering all engineering stages from desing to validation. We hope that this dataset will be the first step for the development of new data pipelines aimed to optimize and improve the AM processes and to speed up their digital transformation.</p> <p><br> <strong>Authors</strong></p> <ul> <li>Carlos Gonzalez-Val: Main contact (carlos.gonzalez@aimen.es)</li> <li>Baltasar Lodeiro</li> <li>Marcos Diez</li> </ul> <p> </p> <p><strong>Entities</strong></p> <p>This dataset was collected under the INTEGRADDE project. Attributions:</p> <ul> <li>AIMEN: Process data collection and manufacturing of T-Coupons, CC-Coupons-AIMEN and Jet Engine.</li> <li>MX3D: Process data collection and manufacturing of CC-Coupons-MX3D and Plates.</li> <li>University of West: Process data collection and manufacturing of CC-Coupons-WEST.</li> <li>IREPA: Process data collection and manufacturing of CC-Coupons-IREPA.</li> <li>CEA: Tomography analysis.</li> <li>DATAPIXEL: Dimensional inspection.</li> </ul> <p><br> <strong>Structure</strong></p> <p>The dataset follows this structure:</p> <ul> <li>Dataset <ul> <li>[SAMPLE 1 NAME] <ul> <li>README: metadata and information about the sample. Format: txt.</li> <li>Photo: a photo of the manufactured sample. Format: jpg.</li> <li>Design: a 3D design file of the piece before manufacturing (original design). Format: stl.</li> <li>Trajectories: the trajectories followed for the manufacturing. Format: gcode.</li> <li>Process data: data recorded from the process. Format hdf5.</li> <li>Tomography: data from a 3D tomographic reconstruction. Format: raw.</li> <li>Dimensional inspection: A comparison</li> </ul> </li> <li>[SAMPLE 2 NAME] <ul> <li>...</li> </ul> </li> </ul> </li> </ul> <p>Further information and metadata is contained in each stage's subdirectory.</p> <p>Note that not all the samples contain all the stages.</p> <p><br> <strong>Software</strong></p> <p>To open the different files that conform the dataset, we recommend the following Open softwares:</p> <ul> <li> hdf5 -> HDF5 Viewer: https://www.hdfgroup.org/downloads/hdfview/</li> <li> stl/amf -> Slic3r: https://slic3r.org / OpenJScad: https://openjscad.org/</li> <li> stp -> ShareCad: https://beta.sharecad.org/</li> <li> gcode -> Text editor / Slic3r: https://slic3r.org/</li> <li> raw -> ImageJ: https://imagej.net/</li> </ul> <p>More information on how to open the files of the dataset can be found in the README.</p>
Datasets and images of publication: Additive manufacturing for self-healing soft robots
<p>This entry contains the images and data used for the publication: Additive manufacturing for self-healing soft robots (DOI: 10.1089/soro.2019.0081). The datasets are named after the image they refer to and are available under the CC-BYSA 4.0 International license.</p>
Dataset for: "Additively manufactured degradable piezoelectric microsystems for sensing and actuating"
<h2>Dataset for "Additively manufactured degradable piezoelectric microsystems for sensing and actuating"</h2><p><strong>Morgan Monroe1,2, Nicolas Fumeaux1, L. Guillermo Villanueva2, and Danick Briand1</strong></p><p><strong>1Soft Transducers Laboratory (LMTS), EPFL, Switzerland</strong></p><p><a href="mailto:morgan.monroe@epfl.ch">morgan.monroe@epfl.ch</a>, <a href="mailto:danick.briand@epfl.ch">danick.briand@epfl.ch</a></p><p><strong>2Advanced NEMS Laboratory (A-NEMS), EPFL, Switzerland</strong></p><p> </p><p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled "Additively manufactured degradable piezoelectric microsystems for sensing and actuating" </p><p><strong>DOI: 10.1002/admt.202300745</strong></p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>Manuscript Abstract: </strong></h3><p>The increasing global overabundance of electronic waste and concerns regarding the energy and material-intensive processes associated with traditional electronics manufacturing is driving the development of solution processed, degradable electronics. In particular, solution processed, degradable piezoelectrics have widespread potential in sustainable electronics, due to their diverse use in both sensing and actuating applications and the current industry predominance of lead-based materials. Yet current eco-friendly multi-material printing processes are limited by both the conventional challenges of multilayer process integration as well as the low-temperature thermal constraints of biodegradable materials. In this study, we present a novel approach to the fabrication of additively manufactured and sustainable piezoelectric devices made with degradable electrode materials on paper substrates. The screen-printed, eco-friendly KNbO3 piezoelectric transducers are combined with degradable carbon- or zinc-based conductive inks. We evaluate the physical, dielectric, and piezoelectric properties of the devices, assessing the influence of electrode material on device performance. We report on effective piezoelectric coefficients as high as 4.6 pC N-1 and 5.1 pC N-1 for printed piezoelectric devices on paper substrates with carbon and zinc electrodes respectively. We then demonstrate the applicability of the developed technology in both sensing and actuating applications. Thus, we present the first instance of sustainable fully additively manufactured piezoelectric force sensors and acoustic speakers. By demonstrating entirely printable piezoelectric devices compatible with various green electrode materials, we work to develop more complex sustainable printed piezoelectric technologies in the future.</p><p> </p><h3>The data set consists of the following folders:</h3><ul><li>Device design files</li><li>Physical characterization data</li><li>Dielectric characterization data</li><li>Force Sensor Demonstrator data</li><li>Speaker Demonstrator data</li></ul><p>Below is a detailed description of the data types and contents of each folder. In many files, the naming convention includes one of two key material indicators. In reference to ink properties, the term "Ink Active Material" indicates the primary ingredient in the ink being characterized. This is either KNbO3, Zinc, or Carbon. The specific ink compositions can be found in the Methods portion of the associated manuscript. In reference to devices being characterized, the term "Electrode Material" indicates the primary component of the printed or deposited electrode layers of the devices (as the piezoelectric layer is always the printed KNbO3 film). The electrode materials are either "Gold" (referring to thermally evaporated Gold of approximately 100nm thickness) which was used as a reference electrode material, "Zinc" (referring to screen printed zinc ink as described in the manuscript), or "Carbon" (Referring to screen printed carbon ink as described in the manuscript).</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>Data types</strong></h3><p>There are 13 file types in this data set: .pdf, .png, .tif, .txt, .dat, .csv, .svg, .stl, .py, .vi, .dwf3work, .aup3, .wav</p><ul><li><strong>.pdf files </strong><ul><li>pdf files in this repository contain summaries of images used in physical characterization, aggregated for ease of visualization. These are exported from Photoshop files and thus include full image information.</li></ul></li><li><strong>.png and .tif files </strong><ul><li>These files contain scanning electron microscopy images of the printed samples on silicon, in cross-section.</li></ul></li><li><strong>.txt, .csv, and .dat files </strong><ul><li>These files contain raw data from device characterization. These file types are comma delimited and the files can be opened with text editors such as Notepad++. Column headers elaborate on the data contained within each file. </li></ul></li><li><strong>.svg files </strong><ul><li>These files contain vector data displaying the full designs of devices fabricated in this study. This data can be opened with a vector graphics editor such as InkScape. The various layers of each file denote a different layer of the device design, and can be treated individually as masks for the relevant layers.</li></ul></li><li><strong>.stl files </strong><ul><li>These files contain design information for 3D components. These files can be opened with any 3D design software such as FreeCAD. All .stl files included in this repository are reproduced from Shannon Ley (<a href="https://pinshape.com/items/36134-3d-printed-3d-printed-headphones">https://pinshape.com/items/36134-3d-printed-3d-printed-headphones</a>).</li></ul></li><li><strong>.py files </strong><ul><li>These files contain python scripts used to process the raw data after collection. These scripts can be opened and edited with standard python scripting interfaces. Each script is commented with descriptions of the overall file as well as in-line comments for user orientation. </li></ul></li><li><strong>.vi files </strong><ul><li>These files contain LabVIEW scripts used to collect raw data during measurements. These scripts can be opened and edited with LabVIEW from version 2020. The script is commented with descriptions of the overall file as well as in-line comments for user orientation. Only one file in this dataset is of this filetype.</li></ul></li><li><strong>.dwf3work files</strong><ul><li>These files contain Digilent Waveforms workspaces used to collect raw data during measurements. These scripts can be opened using the opensource Digilent Waveforms software (<a href="https://digilent.com/shop/software/digilent-waveforms/">https://digilent.com/shop/software/digilent-waveforms/</a>) to view the recorded data and all relevant recording parameters at time of measurement. </li></ul></li><li><strong>.aup3 files </strong><ul><li>These files contain Audacity workspaces used to collect and process audio data during speaker characterization measurements. These files can be opened using the opensource Audacity software (<a href="https://www.audacityteam.org/">https://www.audacityteam.org/</a>).</li></ul></li><li><strong>.wav files </strong><ul><li>These files are audio files of the data exported from Audacity during speaker device characterization and can be opened with any audio processing software.</li></ul></li></ul><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>01 Characterization Device Design</strong></h3><p>This folder contains the design files associated with the fabrication of the basic printed devices used for characterization studies.</p><h4><strong>01 CapacitorsALL.svg</strong></h4><p>A vector file containing the whole-device design of the capacitor style devices used in primary characterization. Each layer of the file is a layer of the device, and was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><h4><strong>02 BottomElectrode.svg</strong></h4><p>A vector file containing the design of solely the bottom electrode layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><h4><strong>03 PiezoelectricLayer.svg</strong></h4><p>A vector file containing the design of solely the piezoelectric layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><h4><strong>04 TopElectrode.svg</strong></h4><p>A vector file containing the design of solely the top electrode layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>02 Physical Characteristics</strong></h3><p>This folder contains all the data associated with characterizing the physical properties of the piezoelectric devices in this manuscript.</p><h4><strong>01 Particle Size Analysis</strong></h4><p><strong>01 Images</strong>: A folder of SEM images as .png files. These images are cross-sectional SEM images of samples used to evaluate the particle size distribution for the KNbO3, Zinc, and carbon powders using in device fabrication. A second set of images with an appended filename ("traces") in the same folder portrays the sizing lines used to randomly sample particles for sizing.</p><p><strong>02 ParticleSizeDistribution_RAW.csv</strong>: A data file containing the raw measurements collected using the above images in tandem with ImageJ processing software. Data was used to produce histograms of particle size distributions for the KNbO3, Zinc, and Carbon particles.</p><p> </p><h4><strong>02 Profilometry</strong></h4><p><strong>01 Profilometry Data</strong>: A folder of raw data collected as profilometry measurements in the form of .dat files. Data files are labelled using the convention "Profile_[Electrode Material]_DeviceStack_Sample[#].dat" (Ex: "Profile_Carbon_DeviceStack_Sample2.dat"). All measurements begin with a measure of the paper substrate as reference before approaching the sample, where it crosses all 3 layers of the sample before returning to the paper substrate, creating a series of layer-cake-like steps from which layer thicknesses can be determined.</p><p><strong>02 ProfilometeryDataPlotter.py</strong>: a python script to batch import and plot the above collected raw profilometry data. </p><p> </p><h4><strong>03 Cross-section Optical Images</strong></h4><p>A folder containing optical microscopy images of the printed devices in cross-section when printed on paper substrates. The naming convention used is "Optical_[Electrode Material]_[microscope Magnification]_[Image number in that condition].tif" (Ex: "Optical_Carbon_x50_01.tif").</p><p> </p><h4><strong>04 Cross-section SEM Images</strong></h4><p>A folder containing scanning electron microscopy ("SEM") images of the printed devices in cross-section when printed on silicon substrates. The naming convention used is "SEM_[Electrode Material]_[microscope Magnification]_[Image number in that condition].tif" (Ex: "SEM_Carbon_1k_01.tif").</p><p> </p><h4><strong>05 Ink Rheology </strong></h4><p>Summary data collected during rheological measurements of the KNbO3, Zinc, and Carbon inks, as .txt files. The naming convention is "Viscosity_[Ink Primary Component]Ink.txt" (Ex: "Viscosity_ZincInk.txt")</p><p> </p><h4><strong>06 Degradation Study </strong></h4><p><strong>01 DegradationStudy_MassChange.csv</strong>: A data file containing the raw measurements collected during the degradation studies. Data includes the specific samples under test and their measured mass at specific dates, as measured after drying.</p><p><strong>02 Degradation Study Images.pdf</strong>: a .pdf file containing the raw degradation images used in this study, correlated to the dates of imaging and text conditions. </p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>03 Electrical Characteristics</strong></h3><p>This folder contains all the data associated with characterizing the dielectric and piezoelectric properties of the piezoelectric devices in this paper.</p><h4><strong>01 Impedance Data</strong></h4><p><strong>01</strong> <strong>ImpedanceDataAnalysis.py </strong></p><p>The python analysis script used to batch analyze and plot the raw impedance data collected for these samples. Takes the files in the associated folder as input and outputs arrays of measured capacitance and permittivity values for the data analyzed, as well as plots of the processed impedance data as a function of frequency.</p><p><strong>02 Raw Impedance Data</strong></p><p>The raw impedance data used to evaluate the dielectric properties of the devices, including two file types: </p><p><strong>".csv"</strong>: Impedance data collected for capacitor style devices of each electrode type, in the after exporting the relevant impedance, phase, and capacitance data from the raw collection file format. Naming convention is "Impedance_[electrode material]_ALL.csv" (Ex: "Impedance_Carbon_ALL.csv").</p><p><strong> ".dwf3work"</strong>: Raw Impedance data collected for capacitor style devices of each electrode type, in the original files as collected using Digilent Waveforms Software (open source). Data is split into two files based on the size of the capacitors being measured, (either 5 and 10 mm2 devices, or 20 and 30mm2 devices). The naming convention used is "Impedance_[Electrode Material]__[capacitor surface areas tested].dwf3work" (Ex: "Impedance_Zinc_5&10mm2.dwf3work").</p><p> </p><h4><strong>02 Berlincourt Data</strong></h4><p>This folder contains all the data associated with characterizing the piezoelectric properties of the devices in this manuscript. It includes 3 files, sorted by the electrode material of the devices under test, and reports the average d33,eff values (over 3 repetitions) measured for those devices based on the applied poling Voltage and field. The naming convention used is "BerlincourtData_[Electrode Material].csv" (Ex: "BerlincourtData_Carbon.csv").</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>04 Force Sensor Demonstrator</strong></h3><p>This folder contains all the data associated with force sensor demonstrator reported on in the associated manuscript.</p><h4><strong>01 TouchGrid.svg</strong></h4><p>A vector file containing the whole-device design of the force sensor device used in this sensing demonstration. Each layer of the file is a layer of the device, and was used for the ordering and fabrication of associated screen printing meshes.</p><p> </p><h4><strong>02 VoltageMeasurement.vi</strong></h4><p>A LabView script file used for recording the output voltage of the piezoelectric devices as a function of time. To be used in combination with an Agilent 34410A or 34411A Multimeter. Outputs Voltage response as a function of time from the initiation of the recorded measurement.</p><p> </p><h4><strong>03 Single Force Data</strong></h4><p>This folder contains data associated with characterizing the force sensor demonstrators reported on in this manuscript. It includes 3 sub-folders, sorted by the electrode material of the devices under test. The naming convention used for the sub-folders is "SingleForce_[Electrode Material]" (Ex: "SingleForce_Zinc"). Within each sub-folder is a series of data files (.csv) detailing the test and data conditions. Each sample was compressed with a set force in a series of pulses. The naming convention used for the files in the sub-folders is "[Electrode Material]_[Max Applied Force]_[MeasuredData].csv" (Ex: "Gold_12.5N_MM.csv"), where the measured data is either "_F" or "_MM" for Force data or Multimeter data respectively.</p><p><strong>"_F.csv": </strong>The files include data recorded by the Instron Pull tester of the force applied to the sample as a function of time. This data is associated with the measured voltage in the paired "_MM.csv" file. </p><p><strong>"_MM.csv": </strong>The files include data recorded by a LabVIEW script in tandem with an Agilent multimeter of the voltage produced by the sample associated with the incident force in the paired "_F.csv" file. </p><p> </p><h4><strong>04 Stepped Force Data</strong></h4><p>This folder contains data associated with characterizing the force sensor demonstrators reported on in this manuscript. It includes 3 sub-folders, sorted by the electrode material of the devices under test. The naming convention used for the sub-folders is "SteppedMeasurements_[Electrode Material]" (Ex: "SteppedMeasurements_Gold"). Within each sub-folder is a series of data files (.csv) detailing the test and data conditions. Each sample was compressed with a series of pulses successively increasing in applied force. The naming convention used for the files in the sub-folders is "[Electrode MaterialSteppedSweep[#]_[MeasuredData].csv" (Ex: "Carbon_SteppedSweep1_F.csv"), where the measured data is either "_F" or "_MM" for Force data or Multimeter data respectively, and the # indicates the repetition number of that specific measurement. Other details are the same as those described above for the subfolder data of 03 Single Force Data.</p><p>-------------------------------------------------------------------------------------------------------------------------------</p><h3><strong>05 Speaker Demonstrator</strong></h3><p>This folder contains all the data associated with speaker demonstrator reported on in the associated manuscript.</p><h4><strong>01 Speaker Design</strong></h4><p><strong>01</strong> <strong>SpeakersBuzzers.svg </strong></p><p>A vector file containing the design of the components used to fabricate the piezoelectric buzzer for the speaker demonstrator. This includes a baseplate onto which the piezoelectric devices were adhered, two rings as standoffs, and two long traces used for the "contact wires". All components were lasercut from cardboard or cardstock components.</p><p><strong>02</strong> <strong>3D Design Files </strong></p><p>A folder containing the design files (.stl) for 3D printing of the headphone chassis, including the headband, ear cans, and baffles. All designs were provided open source by Shannon Ley (https://pinshape.com/items/36134-3d-printed-3d-printed-headphones).</p><p> </p><h4><strong>02 Speaker Data</strong></h4><p><strong>01</strong> <strong>RawAudioRecordings </strong></p><p>A folder containing the raw audio recordings used in speaker characterization, in the form of .aup3 files, directly from the recording software (Audacity). File naming convention is "[Electrode Material]_[Device Size used]_[Recording sampling rate]_RAW.aup3" (Ex: "Zinc_5x30mm2_44.1khz_RAW.aup3").</p><p><strong>02</strong> <strong>Exported Audio </strong></p><p>A folder containing the exported audio recordings used in speaker characterization after trimming to a consistent length of 15s, and exporting from the recording software (Audacity) in the form of .wav files. Each sub-folder has naming convention of "[Electrode Material]_[Device Size used]_[Recording sampling rate]_export" (Ex: "Zinc_5x30mm2_44.1khz_export"), and contains a number of files. Each file within these folders is labelled with the frequency at which the speaker was actuated for that recording data. These files were then imported into Origin, where an FFT was used to extract the amplitude of the recorded data at that specific actuating frequency.</p><p> </p><h4><strong>03 Speaker LDV</strong></h4><p>A folder containing the laser doppler vibrometry data collected for speakers with either Zinc or Carbon-electroded piezoelectric actuators. The data comes in two formats: as-produced from Digilent Waveforms (.dwf3work), or exported (.csv) files. The naming convention for the raw data is "[Electrode Material]_[Speaker active area]_LDV_Raw.dwf3work" (Ex: "Zinc_5x30mm2_LDV_Raw.dwf3work"). The naming convention for the exported data is "[Electrode Material]_[Speaker active area]_LDV_Export.csv" (Ex: "Zinc_5x30mm2_LDV_Export.csv").</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ni-based alloy Inconel IN718
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ti-6Al-4V
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of austenitic stainless steel AISI 316L
<p><span>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF</span><span>‑</span><span>LB/M) and from a conventional process route (hot rolled sheet). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</span></p>
Dataset for paper entitled, 'Confirmation of rapid-heating β recrystallization in wire-arc additively manufactured Ti-6Al-4V'.
<p>Dataset for paper entitled, 'Confirmation of rapid-heating β recrystallization in wire-arc additively manufactured Ti-6Al-4V'. doi: https://doi.org/10.1016/j.mtla.2020.100857</p>
A Sensorized Soft Pneumatic Actuator Fabricated with Extrusion-Based Additive Manufacturing
<p>Soft pneumatic actuators with a channel network (pneu-net) based on thermoplastic elastomers are compatible with fused deposition modeling (FDM). However, conventional filament-based fused deposition modeling (FDM) printers are not well suited for thermoplastic elastomers with a shore hardness (Sh < 70A). Therefore, in this study, a pellet-based FDM printer was used to print pneumatic actuators with a shore hardness of Sh18A. Additionally, the method allowed the in situ integration of soft piezoresistive sensing elements during the fabrication. The integrated piezoresistive elements were based on conductive composites made of three different styrene-ethylene-butylene-styrene (SEBS) thermoplastic elastomers, each with a carbon black (CB) filler with a ratio of 1:1. The best sensor behavior was achieved by the SEBS material with a shore hardness of Sh50A. The dynamic and quasi-static sensor behavior were investigated on SEBS strips with integrated piezoresistive sensor composite material, and the results were compared with TPU strips from a previous study. Finally, the piezoresistive composite was used for the FDM printing of soft pneumatic actuators with a shore hardness of 18 A. It is worth mentioning that 3 h were needed for the fabrication of the soft pneumatic actuator with an integrated strain sensing element. In comparison to classical mold casting method, this is faster, since curing post-processing is not required and will help the industrialization of pneumatic actuator-based soft robotics</p>
Porosity distribution in sub-skin boundary area of the powderbed additively manufactured parts
<p>Repository contains measurement results of the experimental investigationn of the sub-skin porosity in additively manufactured parts. Specimens were manufactured using machine manufacturer's suggested process parameters. Four different machines (EOS M400, TRUMPF TruPrint 1000, SLM 280 and DMG MORI LASERTEC 30 2nd gen.) and four different powder materials (mararging steel 1.2709, aluminum alloy AlSi10Mg, Titanium grade 5 and stainless steel 1.4404) are covered. Influences of the relative orientation of the hatch and boundary scanning tracks was investigated. Efficiency of the mitigation strategy againts sub-skin porosity issues through distance variation between hatch and boundary tracks was evaluated.</p> <p>Please refer to README.MD (or .PDF) for more detailed information about this dataset.</p>
Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing
<p>This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".</p>
Additively Manufactured Titanium 'Alloy-Alloy Composites' and Site-Specific Property Design
<p>Paper links: Journal of Materials Characterization: https://doi.org/10.1016/j.matchar.2021.111577</p> <p>Research Gate (free preprint): https://bit.ly/3xy1Ldm</p> <p>Video written and produced by Alec Davis and Jacob Kennedy. Research was conducted at the University of Manchester and Cranfield University, UK, by Jacob Kennedy, Alec Davis, Armando Caballero, Michael White, Jon Fellowes, Ed Pickering, and Phil Prangnell. This work was supported by grants: NEWAM (EPSRC EP/R027218/1), Lightform (EPSRC EP/R001715/1), and Henry Royce Institute for Advanced Materials (EPSRC EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1).</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.