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126 results for “Welding”
Full field deformation data: Laser welded Eurofer-97
<p>Tensile deformation data at room temperature for an autogenous laser weld in Eurofer-97.</p> <p>Specimens are with and without a laser weld, with and without a post-weld heat treatment.</p> <p>Deformation is full field and takes the form of x, y, z coordinates and dx, dy, dz displacements.</p> <p>Virtual extensometry is extracted from the data.</p> <p>Example Jupyter notebook provided.</p> <p> </p>
Weld Quay, Penang, Malaysia
5 different building in Weld Quay, Penang, Malaysia Source: Objaverse 1.0 / Sketchfab
FIGURE 1. Amphibolips murata Weld, 1957 in Revision of the Amphibolips 'niger' group with the description of a new species (Hymenoptera: Cynipini)
FIGURE 1. Amphibolips murata Weld, 1957, representative of the 'nassa' species-group; lateral habitus, extracted from the Smithsonian Institution website (http://n2t.net/ark:/65665/m350fdd1f3-a353-4d6c-98ce-ee807cbe7576), picture taken by Matt Buffington.
5 Heritage Building (Weld Quay, Penang)
Source: Objaverse 1.0 / Sketchfab
Data for Thermo-Mechanical Analysis of Friction Stir Welding
<p><span>This study explores the development and application of machine learning (ML) metamodels for the thermo-mechanical analysis of Friction Stir Welding (FSW). The main objective is to address the challenge of accurately predicting the thermo-mechanical behaviour of materials in FSW processes. Using finite element models, a high-fidelity dataset consisting of 20 Hammersley design datapoints is generated which is then used to develop a low-fidelity dataset of 420 datapoints using KNN imputation. </span></p>
Flexible Automation and Intelligent Manufacturing 2024, Taiwan - Optimization of Thermal Induction Welding Parameters in LPBFM for Enhanced Adhesion Control
<p>Laser powder bed fusion of metals (LPBFM) is an additive manufacturing process in which powdered alloy is selectively fused together to form near net shape parts. A method for qualifying a model to optimize thermal conduction welding parameters to omit lack of fusion defects is proposed. The approach takes into account: material, geometry and previously experimentally derived, robust process parameters.</p> <p><br>It entails carrying out specific experiments to generate data for calibration and validation of the model for fine-tuning of process parameters to compensate for the changing boundary conditions throughout the build. The objective is to achieve defined adhesion criteria for the meltpools of each laser scan vector.</p> <p><br>The method transforms the build job preparation of LPBFM into an optimization problem by determining the required precision and defining requirements for the model approach to be applicable on the shopfloor. The work contributes to a model-based quality assurance of LPBFM technology.</p>
Dataset for article "Fatigue crack initiation and propagation relation at notched specimens with welded joint characteristics"
<p>The dataset presents is a collection of fatigue test data obtained from artificially notched specimens with weld characteristics. The data was used to investigate the relation between crack initiation and propagation in welded joints of different notch acuity (different radii and opening angle) by excluding the effect of geometrical variation along weld seams. The experiments show that the investigated relationship basically depends on the notch acuity, the load level and the stress ratio.</p> <p> </p> <p>For detailed information about the tests and the assessment please refer to the article:</p> <p>Braun M, Fischer C, Baumgartner J, Hecht M, Varfolomeev I. Fatigue Crack Initiation and Propagation Relation of Notched Specimens with Welded Joint Characteristics. <em>Metals</em>. 2022; 12(4):615. https://doi.org/10.3390/met12040615 </p>
Laser welding IR data for ML
<p> </p> <p>HDF5 datasets of laser welding processes using different laser power and robot speed configurations.</p> <p>- excel sheet protocol </p> <p>- 500Hz aquisition rate</p> <p>- coaxial view</p> <p>- wavelength: 1750 +-250</p> <p> </p>
Geometry scans - Experimental validation of an analytical stress magnification factor for thin plates with welding-induced curvature
Open the record for dataset details and reuse information.
2D image dataset for the presence or absence of aesthetical defects on EV battery welds.
<p>A 2D image dataset of EV car battery welds is used to train AI/ML models on weld classification for quality inspection applications or other purposes.</p> <p> </p> <p>Includes 1 zip file with 61,670 greyscale images of 409x40 resolution split in</p> <ul> <li>training set [ 43,169 images]</li> <li>validation set [ 9,250 images]</li> <li>testing set [ 9,251 images]</li> </ul> <p> </p> <p>and the following 2 classes:</p> <p>1] OK --> indicating the absence of aesthetical defects</p> <p>2] KO --> indicating the presense of aesthetical defects</p>
Synthetic Multimodal Dataset using ABB Studio: Single Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Löppenberg et al. (2024), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p>Frame '02655_frame' is corrupted. That means the corresponding values of the other modality needs to be removed as well.</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024). Dynamic robot routing optimization: State-space decomposition for operations research-informed reinforcement learning. Robotics and Computer-Integrated Manufacturing, 90, 102812.</p>
Synthetic Multimodal Dataset using ABB Studio: Dual Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Diprasetya et al. (2023), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters for each robot:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Diprasetya, M. R., Yuwono, S., Löppenberg, M., & Schwung, A. (2023, July). Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation. In 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1-7). IEEE.</p>
FIGURES 224–232. 224–225 in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 224–232. 224–225, Dros perlentum Kinsey, 1937: 224, mesopleuron, lateral view, 225, mesoscutum, dorsal view. 226, Amphibolips quercuscinerea (Ashmead, 1881), asexual female, mesoscutum and mesoscutellum, dorsal view. 227, Femuros integrum Kinsey, 1937, ind leg (arrows indicate darker lines on hind tibia). 228–230, Kinseyella lapiei (Kieffer, 1911), asexual female, 228–229, metasoma: 228, lateral view, 229, dorsal view; 230, propodeum, dorsoposterior view. 231, Striatoandricus georgei (Pujade-Villar, 2011), asexual female, metasoma, lateral view. 232, Disholcaspis pedunculoides Weld, 1926, asexual female, mesoscutum and mesoscutellum, dorsal view.
FIGURES 208–209. Paracraspis asexual galls. 208, P in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 208–209. Paracraspis asexual galls. 208, P. guadaloupensis, comb. rev.. 209, P. insolens, comb. rev.. Images courtesy of J. Gross.
FIGURES 196–201 in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 196–201. Paracraspis insolens, comb. rev., asexual female. 196–199, head: 196, frontal view, 197, dorsal view, 198, lateral view, 199, posterior view. 200, antenna. 201, mesosoma and propleura, frontal view.
FIGURES 202–205 in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 202–205. Paracraspis insolens, comb. rev., asexual female. 202, mesosoma, lateral view. 203, mesoscutum, dorsal view. 204, mesoscutellum, dorsal view. 205, metascutellum and propodeum, posterodorsal view.
FIGURES 193–195. Disholandricus asexual galls. 193, D in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 193–195. Disholandricus asexual galls. 193, D. reniformis, comb. nov.. 194–195, D. truckeensis, comb. nov.:194, lateral view, 195, ventral view. Fig 193 courtesy of J. Gross.
FIGURES 189–192 in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 189–192. Disholandricus truckeensis, comb. nov., asexual female. 189, mesosoma, lateral view. 190, mesoscutum, dorsal view. 191, mesoscutellum, dorsal view. 192, metasoma, lateral view.
FIGURES 183–188 in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 183–188. Disholandricus truckeensis, comb. nov., asexual female. 183–186, head: 183, frontal view, 184, dorsal view, 185, lateral view, 186, posterior view. 187, antenna. 188, mesosoma and propleura, frontal view.
FIGURES 176–178 in Three new Nearctic genera of oak cynipid gall wasps (Hymenoptera: Cynipidae: Cynipini): Burnettweldia Pujade-Villar, Melika & Nicholls, Nichollsiella Melika, Pujade-Villar & Stone, Disholandricus Melika, Pujade-Villar & Nicholls; and re-establishment of the genus Paracraspis Weld
FIGURES 176–178. Disholandricus reniformis, comb. nov., asexual female. 176–178, metasoma: 176, lateral view, 177, dorsal view, 178, ventral view.
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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.