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717 results for “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>
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</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>
TCM: Benchmark Datasets for Predictive Maintenance in Steel Manufacturing
<h1>Anomaly-TCM</h1> <p>Predictive Maintenance (PdM) is a strategy that uses advanced data analytics to predict equipment failures and maintain industrial machinery in good condition. Its goals are to minimize downtime, reduce operational costs, and ensure product quality. PdM methods are applicable across various industries, including steel manufacturing.</p> <p>In steel production, cold rolling is a critical process that reduces the thickness of hot-rolled steel. Developing PdM methods for tandem cold mills (TCM) can significantly improve production efficiency. However, researchers often rely on real manufacturing data, which is typically unavailable, unlabeled, and noisy, making it difficult to validate and compare methods.</p> <p>To overcome this, we created synthetic datasets for the cold rolling process to identify anomalies based on physical principles. These datasets were generated using a mathematical model of a 5-stand TCM, calculating key process parameters like rolling force, torque, speed, tension, gap, thickness reduction, and motor power. We introduced anomalies related to specific failures in the process.</p> <p>We produced six diverse datasets, each with varying complexity, to enable benchmarking of machine learning-based PdM methods for the cold rolling process. Four different types of anomalies were introduced, which are related to a physics-based deviations in the process:</p> <ol> <li>Anomaly in reduction scheme</li> <li>Anomaly in work roll (increased work roll friction)</li> <li>Anomaly in bearing (increased motor torque)</li> <li>Anomaly in electric motor (decrease efficiency)</li> </ol> <p> The details of the datasets are provided below.</p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Observations</strong></td> <td><strong>Anomalies</strong></td> <td><strong>Share of Anomalies</strong></td> <td><strong>Features</strong></td> <td><strong>Anomaly Types</strong></td> <td><strong>Products</strong></td> <td><strong>Data Drift</strong></td> </tr> <tr> <td>tcm5_dataset_1</td> <td>20009</td> <td>1045</td> <td>5.2%</td> <td>51</td> <td>1</td> <td>4</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_2</td> <td>20001</td> <td>1035</td> <td>5.2%</td> <td>51</td> <td>1</td> <td>20</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_3</td> <td>20003</td> <td>981</td> <td>4.9%</td> <td>51</td> <td>4 (16)</td> <td>4</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_4</td> <td>20001</td> <td>925</td> <td>4.6%</td> <td>51</td> <td>4 (16)</td> <td>20</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_5</td> <td>20005</td> <td>1031</td> <td>5.2%</td> <td>51</td> <td>4 (16)</td> <td>5</td> <td>TRUE</td> </tr> <tr> <td>tcm5_dataset_6</td> <td>20008</td> <td>954</td> <td>4.8%</td> <td>51</td> <td>4 (16)</td> <td>25</td> <td>TRUE</td> </tr> </tbody> </table> <p> </p> <p>Each dataset is generated as a data stream, meaning the observations follow a chronological order, represented by increasing work roll mileage (which is reset after a predefined threshold). The table below provides details about the features and labels present in the datasets. Several features are recorded for each rolling stand, totaling 51 features. Apart from the anomaly related to reduction, the other anomalies are specific to individual stands, resulting in 16 anomaly labels in total.</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Suffixes</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>thickness_entry</td> <td>-</td> <td>mm</td> <td>steel entry thickness</td> </tr> <tr> <td>thickness_exit</td> <td>-</td> <td>mm</td> <td>steel exit thickness</td> </tr> <tr> <td>width</td> <td>-</td> <td>mm</td> <td>steel width</td> </tr> <tr> <td>ys_entry</td> <td>-</td> <td>MPa</td> <td>steel entry yield strength</td> </tr> <tr> <td>ys_exit</td> <td>-</td> <td>MPa</td> <td>steel exit yield strength</td> </tr> <tr> <td>work_roll_diam</td> <td>1 to 5</td> <td>mm</td> <td>work roll diamaeter (stands 1 to 5)</td> </tr> <tr> <td>work_roll_mileage</td> <td>1 to 5</td> <td>km</td> <td>work roll mileage (stands 1 to 5)</td> </tr> <tr> <td>reduction</td> <td>1 to 5</td> <td>-</td> <td>thickness reduction (stands 1 to 5)</td> </tr> <tr> <td>tension</td> <td>0 to 5</td> <td>N</td> <td>interstand tension (0 is tension before stand 1, 1-5 refer to tension after stands 1-5)</td> </tr> <tr> <td>roll_speed</td> <td>1 to 5</td> <td>NaN</td> <td>linear work roll speed (stands 1 to 5)</td> </tr> <tr> <td>force</td> <td>1 to 5</td> <td>N</td> <td>rolling force (stands 1 to 5)</td> </tr> <tr> <td>torque</td> <td>1 to 5</td> <td>Nm</td> <td>rolling torque (stands 1 to 5)</td> </tr> <tr> <td>gap</td> <td>1 to 5</td> <td>mm</td> <td>stand gap (stands 1 to 5)</td> </tr> <tr> <td>motor_power</td> <td>1 to 5</td> <td>kW</td> <td>electric motor power (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_Reduction</td> <td>-</td> <td>-</td> <td>(label) anomaly in reduction scheme</td> </tr> <tr> <td>Anomaly_Electric</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in electric motor (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_Bearing</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in stand bearing (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_WorkRoll</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in work roll friction (stands 1 to 5)</td> </tr> </tbody> </table>
ncrncornell/ced2ar-nber-ces-codebook: Codebook for NBER-CES Manufacturing Industry Database
<p>Codebook for NBER-CES Manufacturing Industry Database (2009) [NAICS and SIC], by Randy A. Becker , Wayne B. Gray , Jordan Marvakov , and Eric J. Bartelsman</p> <p>Main website: <a href="https://www.nber.org/data/nberces5809.html">https://www.nber.org/data/nberces5809.html</a> (note: a newer version is available at <a href="http://www.nber.org/data/nberces.html">http://www.nber.org/data/nberces.html</a> - this codebook does not necessarily reflect the more recent version.)</p> <p>Live version of the DDI codebook at <a href="https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nber-ces/">https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nber-ces/</a></p>
Extensive Checklist to cGMP Inspections in Pharmaceutical Manufacturing Plants
<p><span>cGMP inspections are an essential part of ensuring that pharmaceutical companies produce high-quality, safe, and effective drugs. These inspections help maintain the integrity of the pharmaceutical supply chain and protect public health. Pharmaceutical companies must prioritize continuous cGMP compliance, prepare thoroughly for inspections, and respond promptly to any observations made by inspectors to remain in good standing with regulatory authorities.</span></p> <p><span>Here's a comprehensive cGMP inspection checklist for a pharmaceutical company, incorporating requirements from USFDA, EMA, WHO, UKMHRA, TGA, and ANVISA. This checklist includes a rating system to evaluate compliance with each requirement.</span></p>
Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset
<p>This dataset was published with the dataset descriptor "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset".</p> <p>ACKNOWLEDGEMENTS</p> <p>The project ”ZUKIPRO” is funded as part of the ”Future Centers” program by the Federal<br>Ministry of Labour and Social Affairs and the European Union through the European Social<br>Fund Plus (ESF Plus).Roles and Contributions.</p>
Liquid Resin Infusion (LRI) manufacturing and Spring_In monitoring by FBGs, DCs and 3D CMM meassurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion (LRI) and oven cured pre-preg</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data from the FBGs, DCs and 3D CMM meassurements for the LRI manufacturing process and spring_in distortions monitoring is included.</strong></p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
PnP module: multi-material components manufacturing by Automated Tape Laying process
<p><strong>Introduction</strong></p> <p>The Automated Tape Laying (ATL) process is an automated technique used for composites manufacturing based on fiber placement processes. This module is part of AIMEN Technology Centre Open Pilot Line focusing on manufacturing of multi-material components. This module is composed by a movement system (robot) and heating system (ATL head), which can be composed by IR system or laser source. </p> <p><strong>Asset Administration Shell</strong></p> <p>The Asset Administration Shell (AAS) modelling follows the <em>Product</em>, <em>Process</em> and <em>Resources</em> (PPR) model. The relation between the assets allows the traceability of the Product by demonstrating a Digital Thread based on AAS and how the active AAS modelling allows the Plug and Produce capabilities in a modular production scheme.</p> <p>In this repository some examples of AAS modeling (.aasx files) for a subset of assets in the shop floor (Resources), Product and Process can be found, as well as the architecture of the whole module.</p> <p><strong>Architecture</strong></p> <p>The information gathered by the central unit/industrial PC (Operational Technology) will be available in DIMOFAC platform (Information Technology) as well as the Product information related to the design and/or simulation (Engineering Technology). In the central unit the software in charge of taking the decision and allowing Plung and Produce capabilities is named “Orchestrator”, and in the product side, the software in charge of register all the information related with a specific software “Digital Thread”.</p> <p> </p> <p><strong>AAS Demonstration</strong></p> <p>A demonstration video is available: <a href="https://www.youtube.com/watch?v=aOP6QWiF5FE&t=7s">PnP module: multi-material components manufacturing by Automated Tape Laying process - YouTube</a></p>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Dataset of experiment of wire-harness manufacturing framework validation
<p>Dataset created during the validation experiments of the wire harness manufacturing framework developed within the REMODEL European project.</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>
Lithium tantalate photonic integrated circuits for volume manufacturing
<p>Dataset for the manuscript "Lithium tantalate photonic integrated circuits for volume manufacturing". </p> <p>DOI: 10.1038/s41586-024-07369-1</p> <p>Contains all raw data and code used to produce the Figures and Extended Data Figures in the manuscript. </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>
Manufacturing of screw rotors via 5-axis double-flank CNC machining
<p>Each folder contains the mesh files of the target geometry (screw rotor) and of a corresponding custom-shaped tool. The motions of the tool are described as the CL files, where each line in these files contains the information about the tool tip (first 3 coordinates) and the unit vector of the tool's axis (last 3 coordinates).</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>
Pre-Preg (PP) Manufacturing and Spring-in monitoring through FBGs, DCs and 3D CMM measurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion and oven cured Pre-Preg (PP)</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data obtained from the FBGs, DCs and 3D CMM meassurements during a PP manufacturing process and spring-in distortions monitoring can be found.</strong></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.