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11 results for “multi-material”
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>
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>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Datasets and images of publication: Self-healing and high interfacial strength in multi-material soft pneumatic robots via reversible Diels-Alder bonds
<p>Data and Figures of the publication:</p> <p>Terryn, S.; Roels, E.; Brancart, J.; Assche, G.V.; Vanderborght, B. Self-Healing and High Interfacial Strength in Multi-Material Soft Pneumatic Robots via Reversible Diels–Alder Bonds. <em>Actuators</em> <strong>2020</strong>, <em>9</em>, 34.</p>
Heterolayered carbon allotrope architectonics via multi-material 3D printing for advanced electrochemical devices
<p>3D printing has become a powerful technique in electrochemistry for fabricating electrodes, thanks to readily available conductive nanocomposite filaments, such as those based on carbon fillers (i.e., carbon nanotubes (CNTs) or carbon black (CB)) within an insulating polymeric matrix like polylactic acid (PLA). Inspired by inorganic heterostructures that enhance the functional characteristics of nanomaterials, we fabricated hetero-layered 3D printed devices based on carbon allotropes using a layer-by-layer assembly approach. The heterolayers were customised through the alternate integration of different carbon allotrope filaments via a multi-material 3D printing technique, allowing for a time-effective method to enhance electrochemical performance. As a first demonstration of applicability, CNT/PLA and CB/PLA filaments were utilised to construct ordered hetero-layered carbon-based electrodes. This contrasts with conventional methods where various carbon species are mixed in the same composite-based filament used for building electrochemical devices. Multi-material 3D-printed carbon electrodes exhibit improved electrochemical performance in energy conversion (e.g., hydrogen evolution reaction or HER) and sensing applications (e.g., ascorbic acid detection) compared to single-material electrodes. This work paves the way for manufacturing advanced 3D-printed heterolayered electrodes with enhanced electrochemical activity through multi-material 3D printing technology.</p>
Supplementary materials for "Operando phase mapping in multi-material laser powder bed fusion"
<p>This dataset includes the experimental data for the paper: "Operando phase mapping in multi-material laser powder bed fusion" by S. Sumarli, F. Malamud, S. Van Petegem, S. Gaudez, A. Baganis, M. Busi, E. Polatidis, C. Leinenbach, R. Logé, and M. Strobl. For more information, see the "Open data structure.pdf" file.</p>
Experimental data linked to publication "Process optimization and study of the co-sintering behaviour of Cu-Ni multi-material 3D structures fabricated by spark plasma sintering (SPS)"
<p>Those are all the experimental data used to produce the plots in the article</p>
Dataset for article "Prediction of thermal shock induced cracking in multi-material ceramics using a stress-energy criterion" published in Engineering Fracture Mechanics
<p>Dataset contains graphical outputs of the numerical analysis performed using Finite element method in finite elements software Ansys Mechanical. It contains also photoes of the tested specimens. Further, material data used for the numerical analysis and measured by authors are included in the csv file and all necessary input codes for the FE system Ansys creating data for graphs in the publication are provided in the subfolder "Models-Ansys" within "Data" directory.</p> <p> </p>
Fabrication of chemofluidic integrated circuits by multi-material printing
<p>Supplementary Information</p>
Code and Data for Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy
<p>The data consists of atomic force microscopy (AFM) images of metal organic chemical deposition (MOCVD) grown transition metal dichalcogenides (TMDs), MoS2, WS2, WSe2, MoSe2, and Mo-WSe2, used for the results reported in the manuscript "Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy". The TMDs are grown at the Penn State's 2D crystal consortium (2DCC). The raw data is also available on the LiST (https://data.<br>2dccmip.org/Rut1mMC8u25M). The file names have the format: imageSNo_TMD_sampleLabel_sampleId_set.tif, where SNo, TMD, sampleLabel, sampleId, and set are serial numbers (1, 2, 3, ...), class of TMD (e.g. MoS2, WS2, ...), sample label, sample id, and train or test set, as used in the manuscript. There could be multiple images from the same samples (taken from the center, edges, etc, of wafer). Images from the same sample have the same sample label and sample id. In using the data, it is recommended that the same sample is not present in more than one data set to avoid data leakage. Additionally, github_static consists of the codes used to generate the results reported in the manuscript.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.