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126 results for “weld”
Contour method and neutron diffraction dataset to determine the weld fusion zone shape on residual stress in submerged arc welding
<p>This is a dataset which formed the basis for "The effect of the weld fusion zone shape on residual stress in submerged arc welding" by A. Ishigami, M. J. Roy, J. N. Walsh and P. J. Withers appearing in the Journal of Advanced Manufacturing Technology.</p> <p>Two X-grade steel specimens with different high speed, submerged arc welds with very slight differences in fusion zone shape were compared with a novel contour method application as well as with neutron diffraction. Neutron diffraction was carried out with the SALSA instrument at the Institut Laue-Langevin in Grenoble, France with the assistance of T. Pirling. Data files with 441 in the descriptor refer to 'conventional' parameters (see publication), while 241 refers to 'new'.</p> <p>Provided in this dataset are four *.dat files, which contains 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> <p>Also included is an Excel worksheet, which contains the calculated residual stresses as found with LAMP (https://www.ill.eu/instruments-support/computing-for-science/cs-software/all-software/lamp/). Raw data is available here:</p> <p>P. J. Withers, A. Ishigami, T. Pirling, M. Roy, J. Walsh (2014). The effect of weld bead shape on residual stress in novel low heat input welding of steel [Data set]. ILL. http://doi.ill.fr/10.5291/ILL-DATA.1-02-145</p> <p>The authors would like to thank JFE Steel Corporation for both direct and in-direct support of this research. The authors would also like to thank the Institut Max von Laue-Paul Langevin for the allocation of beamtime at SALSA and gratefully acknowledge the help of Thilo Pirling for his assistance in performing the neutron diffraction experiments. A. Ishigami would like to thank Kenji Oi for his support of this research. M. J. Roy would like to thank Ian Winstanley for his assistance in performing the contour cuts. M. J. Roy acknowledges financial support from the EPSRC (EP/L01680X/1) through the Materials for Demanding Environments Centre for Doctoral Training.</p>
Photonics4All Bookmark Laser Welding (German)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> Do you know how the metal pieces of your car have been welded?<br> <br> Nails and rivets are long gone! Today we use lasers to join multiple pieces of metal with different types of surfaces - curved or straight. Using lasers to cut and weld is cheaper, safer and faster than older techniques and lasers can be easily controlled by robots for high accuracy and high throughput.<br> New powerful lasers can also be used to cut different materials such as plastics, ceramics and metals. The emergence of new types of lasers made it possible.<br> All thanks to the progress in Photonics!</p>
Photonics4All Bookmark Laser Welding (Swedish)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> Do you know how the metal pieces of your car have been welded?<br> <br> Nails and rivets are long gone! Today we use lasers to join multiple pieces of metal with different types of surfaces - curved or straight. Using lasers to cut and weld is cheaper, safer and faster than older techniques and lasers can be easily controlled by robots for high accuracy and high throughput. New powerful lasers can also be used to cut different materials such as plastics, ceramics and metals. The emergence of new types of lasers made it possible.<br> All thanks to the progress in Photonics!</p>
Photonics4All Bookmark Laser Welding (French)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> Do you know how the metal pieces of your car have been welded?<br> <br> Nails and rivets are long gone! Today we use lasers to join multiple pieces of metal with different types of surfaces - curved or straight. Using lasers to cut and weld is cheaper, safer and faster than older techniques and lasers can be easily controlled by robots for high accuracy and high throughput.<br> New powerful lasers can also be used to cut different materials such as plastics, ceramics and metals. The emergence of new types of lasers made it possible.<br> All thanks to the progress in Photonics!</p>
Photonics4All Bookmark Laser Welding (English)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> Do you know how the metal pieces of your car have been welded?<br> <br> Nails and rivets are long gone! Today we use lasers to join multiple pieces of metal with different types of surfaces - curved or straight. Using lasers to cut and weld is cheaper, safer and faster than older techniques and lasers can be easily controlled by robots for high accuracy and high throughput.<br> New powerful lasers can also be used to cut different materials such as plastics, ceramics and metals. The emergence of new types of lasers made it possible.<br> All thanks to the progress in Photonics!</p> <p> </p>
EC 5th Framework ENPOWER austenitic edge welded beam contour cut metrology for assessing residual stress
<p>Data from contour method cut surfaces collected from an autogenously edge-welded AISI 316H stainless steel beam produced as part of ENPOWER. Surfaces were generated as part of a slitting experiment and then subsequently measured with a coordinate measurement machine. This dataset forms the basis for <a href="https://doi.org/10.1115/1.4004626">"<em>Slitting and Contour Method Residual Stress Measurements in an Edge Welded Beam</em>" Hosseinzadeh et al. (2012)</a>, and further information on the specimen background and diffraction based results can be found in <a href="https://doi.org/10.1115/PVP2008-61339">"<em>A statistical framework for analysing weld residual stresses for structural integrity assessment</em>" Nadri et al. (2008)</a>.</p> <p>Datasets are in the form of lists of x,y.z coordinates, with one point per line, whitespace delimited in millimeters. The *Perimeter1.txt file coincides with *Surface1.txt, with the former an outline identifying the cut surface periphery, and the latter points lying on the surface. The same format is employed for the other side of the cut.</p>
Supplementary Information and EBSD data for 'Intermetallic phase layers in cold metal transfer aluminium-steel welds with an Al-Si-Mn filler alloy'
<p>Supplementary information and electron backscatter diffraction (EBSD) data for the article entitled 'Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy'. There are three EBSD datasets, I-III, named "I_EBSD.dat" - "III_EBSD.dat", each with corresponding calibration and background patterns, as well as secondary electron scanning electron microscopy images showing the scanned area and text files containing the acquisition parameters. The data analysis workflow has been published on GitHub, see References.</p>
Dataset for 'Weld map tomography for determining local grain orientations from ultrasound'
<p>This dataset contains data files and Jupyter notebooks used to produce figures in the manuscript 'Weld map tomography for determining local grain orientations from ultrasound'.</p>
Metal Arc Welding
<h2>Predictive Quality Arc Welding Dataset</h2> <p>The dataset comprises various current and voltage time series. Both currents and voltages are synchronously sampled at a frequency 100 kHz, with a maximum permissible error of 0.5%.</p> <p> </p> <h3>Preprocessed Data</h3> <p>Column Name Description</p> <p>------------ -------------------------------------------------------------</p> <p>labels Quality label (0: bad weld quality | 1: good weld quality | -1: no label)</p> <p>exp_ids ID of the experiment run</p> <div> <div>welding_run_id : ID of the welding run</div> </div> <p>V_000 Voltage at the beginning of the cycle (t_0)</p> <p>... Voltage from (t_1) to (t_198)</p> <p>V_199 Voltage at the end of the cycle</p> <p>I_000 Current at the beginning of the cycle (t_0)</p> <p>... Current from (t_1) to (t_198)</p> <p>I_199 Current at the end of the cycle<br><br></p> <h3>Code Sample Reading the Data</h3> <pre><code>import numpy as np import pandas as pd def convert_to_np(data: pd.DataFrame) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ Convert DataFrame to numpy arrays, separating labels, experiment IDs, and features. Args: data (pd.DataFrame): Input DataFrame containing 'labels', 'exp_ids', and feature columns. Returns: tuple: A tuple containing: - labels (np.ndarray): Array of labels - exp_ids (np.ndarray): Array of experiment IDs - data (np.ndarray): Combined array of current and voltage features """ logging.info(f"Converting data to numpy array") labels, exp_ids, welding_run_ids = data["labels"].values, data["exp_ids"].values, df["welding_run_id"].values data = data.drop(columns=["labels", "exp_ids"]) cols_v = data.columns[data.columns.str.startswith("V")] cols_i = data.columns[data.columns.str.startswith("I")] current_data = data[cols_i].values voltage_data = data[cols_v].values data = np.stack([current_data, voltage_data], axis=2) return labels, exp_ids, welding_run_ids, data data_path = "" data = pd.read_csv(data_path) labels, exp_ids, welding_run_ids, data = convert_to_np(data)</code></pre> <p> </p>
AVANGARD | In Situ Monitoring of Welding Quality of GMAW Process
<p>In the framework of the AVANAGRD project, a series of collaborative sensors were used in a feasibility study to develop inline quality monitoring of welding processes, such as microphones, acoustic emission sensors, and thermal cameras, besides of course the arc parameters. In this video, some examples of thermal filming are shown, performed during extreme conditions of GMAW applied on T-joints.<br> Besides the art that each frame already is, it’s possible to provide key information about process stability that will be translated into welding defects.<br> Using a combination of those techniques with Artificial Intelligence, there is no limit on what we can reach related to productivity, quality, and zero-defect manufacturing.<br> The AVANGARD project (Advanced Manufacturing Solution Tightly Aligned with Business Need) is funded by the European Union within the frame of the Horizon 2020 research and development program under Grant Agreement No. 869986. <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnNoM1F3ZnBManZKUlVKT0h1WnhyMUVDY3BpUXxBQ3Jtc0ttR2FwOWVRbXY0am1NNGxWNk5JUndJdS05cnF2U2s2S1lJMEo5YzcycWdWdWRxMEdIM1hNVy1yelpYWW84M0V3anFsVGg0SDZTbFNZVHJIY2RiNnNZQ2pZY2huNE1Da2ZVY2NxZ3lkSVBvUVE0TkdkSQ&q=http%3A%2F%2Fwww.avangard-project.eu%2F&v=7HgNP1rdeHI">http://www.avangard-project.eu/</a></p>
Linked collectors and determiners for: A revision of Ganaspidium Weld, 1952 (Hymenoptera, Figitidae, Eucoilinae): new species, bionomics, and distribution.
Natural history specimen data linked to collectors and determiners held within, "A revision of Ganaspidium Weld, 1952 (Hymenoptera, Figitidae, Eucoilinae): new species, bionomics, and distribution". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/2737c0d7-97dc-4770-9ec1-d420a668bd4a">https://bionomia.net/dataset/2737c0d7-97dc-4770-9ec1-d420a668bd4a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/2737c0d7-97dc-4770-9ec1-d420a668bd4a">https://gbif.org/dataset/2737c0d7-97dc-4770-9ec1-d420a668bd4a</a>. Formatted as a Frictionless Data package.
FIGURE 233 in GEORGE MELIKA, JULI PUJADE-VILLAR, JAMES A. NICHOLLS, VICTOR CUESTA-PORTA, CRYSTAL COOKE-McEWEN & GRAHAM N. STONE (2021) 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. Zootaxa, 4993: 001-081.
FIGURE 233. Consensus tree showing the relationships among the three new Nearctic gall wasp genera Burnettweldia, Nichollsiella and Disholandricus, the re-established genus Paracraspis and other allied genera, based on a Bayesian analysis of a concatenation of three loci (cytochrome b, long-wavelength opsin, and the D2 region of the 28S rRNA gene). Numbers above nodes indicate posterior probability support; nodes with <70% support have been collapsed.
HYPERCOG_Process data_Welding log_2020-07-30
<p>The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three di_erent sectors: steel, cement and chemical.</p> <p>Participating entities: LORTEK. The data was gathered and used in the proof of concept of the architecture introduced in the paper in the way that is described in it.</p> <p><strong>Dataset 1: Welding process data (xlsx files)</strong></p> <p>Real time data of the process of a welding cell on Excel sheets. The data is obtained at a frequency of 100 Hz and variables of voltage, current, temperature, gas flux, etc. are registered in the Excel file by rows.</p> <p><strong>Dataset 2: Temperature and movement of the piece constructed (zip files)</strong></p> <p>Data of temperatures obtained by thermocouple sensors and distortion of the structure measured by a laser sensor. The zip files contain coma separated values of 8 thermocouples welded to the substrate of the piece constructed by the welding cell. The reading of a laser sensor is also recorded along the x coordinate of the movement of the robot for synchronization purposes.</p> <p>The article corresponding to these datasets are available in open access in </p> <pre><a href="https://doi.org/10.5281/zenodo.5533904">https://doi.org/10.5281/zenodo.5533904</a></pre>
Figure 2. Ganaspidium pusillae Weld. A habitus, female B in A revision of Ganaspidium Weld, 1952 (Hymenoptera, Figitidae, Eucoilinae): new species, bionomics, and distribution
Figure 2. Ganaspidium pusillae Weld. A habitus, female B head and mesosoma, lateral view, female.
Laser welding multispectral coaxial monitoring
<p>This is the first dataset containing thermopgraphy data of some single laser welding trials as they were observed by coaxial integrated NIR and S/MWIR cameras and different IR filters.</p>
Data Augmentation for learning mechanical digital twins of voids in welding joints
<p>In Source-2_Data_Augmentation:</p> <p>Exercice1_augmentation.ipynb Jupyter Notebook for data warpping of defect images.</p> <p>Exercice2_augmentation_multimodale.ipynb Jupyter Notebook for multimodal data augmentaion (defect images and mechanical fields) via oversampling</p> <p>Exercice3_clustering.ipynb Data clustering using the k-medoids algorithm applied to mechanical dissimilarity of the defects.</p> <p>k_medoids.py is a python code of a kmedoids algorithm.</p> <p>in Data:</p> <p>All_images.npy (numpy file) contains the defect images.</p> <p>All_Stresses.npy (numpy) contains mechanical fields, All_Stresses[k,i,j,ic,it] is the instance number k of the component ic of the Cauchy stress tensor at time it. The mechanical problem is decribed in <a href="https://dx.doi.org/10.5802/crmeca.51">⟨10.5802/crmeca.51⟩</a>. <a href="https://hal.archives-ouvertes.fr/hal-03113503">⟨hal-03113503⟩.</a></p> <p>New_images_1.npy and New_Stresses_1.npy are augmented data for k=1.</p> <p>New_images_87.npy and New_Stresses_87.npy are augmented data for k=87.</p> <p>Dissimilarity_Stress.npy is the Frobenius norm of the distances between stress tensors (All_Stresses.npy).</p> <p> </p>
Videographic Data for Pore Formation and Melt Pool Analysis of Laser Welded Al-Cu Joints using Synchrotron Radiation
<p>The published data include video recordings of synchrotron radiation during a laser beam welding process in aluminium-copper joints. The recordings show the phase boundaries of the materials and are suitable for an analysis with regard to material mixing and pore formation. The experiments were conducted with the high energy beamline P07 (EH4) of Petra 3 at Deutsches Elektronen Synchrotron DESY in Hamburg, Germany.</p> <p>General parameters:</p> <p>Photon energy of synchrotron beam: 37,7 keV<br> Scintillator material: CdWO4<br> Frame rate: 1000 Hz</p> <p>Specific parameters used for videos:</p> <p>HV185: Cu-ETP (top) to Al99.5 (bottom); wavelengths of laser beam source: 1030 nm; laser beam diameter: 117 µm; laser power: 1000 W; feed rate: 50 mm/s<br> HV186: Cu-ETP (top) to Al99.5 (bottom); wavelengths of laser beam source: 1030 nm; laser beam diameter: 117 µm; laser power: 1500 W; feed rate: 100 mm/s<br> HV192: Al99.5 (top) to CuSn6 (bottom); wavelengths of laser beam source: 1070 nm; laser beam diameter: 34 µm; laser power: 750 W; feed rate: 50 mm/s<br> HV196: Cu-ETP (top) to Al99.5 (bottom); wavelengths of laser beam source: 1070 nm; laser beam diameter: 34 µm; laser power: 750 W; feed rate: 50 mm/s</p>
Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steel battery tabs
<p>In this folder, excel files are stored with the results of signal processing that supported findings in the following paper:</p> <p>"Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steell battery tabs".</p> <p>Matlab scripts and orginal signals will be uploaded soon with more detailed description.</p> <p> </p>
Role of laser wobbling welding parameters in dissimilar welding of aluminum and copper alloys: general preliminary process window.
<p>This database explores a wide range of process parameters concerning laser welding of aluminum and copper 0.3 mm thick foils in lap configuration. In particular, this application involves wobbling technique as a dynamic beam shaping for enhancing the formation of a correct interface width in this kind of welding. Given different values of laser power, wobbling tangential speed and wobbling diameter, the results are expressed in terms of weld bead width at the face of the weld bead, weld bead width at the interface between the two sheets and bead penetration depth in the lower sheet. Some trials gave an excessive penetration so that the specimen was cut in two separate parts after the process. This specific type of dissimilar welding is useful for enhancing "hybrid" characteristics of components, where a trade-off between mechanical and electrical or thermal characteristics is needed.</p>
Enhancing the Agricultural Efficiency of Welded Mesh Solutions
<p>Modern agriculture is critically dependent on productivity and efficiency. Farmers are perpetually in pursuit of innovative methods to optimize operations and augment productivity. One solution that is gaining popularity is welded mesh, a flexible material that offers numerous benefits in a variety of agricultural applications.</p> <h1>Agricultural Landscapes Welded mesh undergoes transformation.</h1> <p>Contemporary farming methods are revolutionized by the adaptability and efficacy of <a href="https://www.dukesmetal.com/meshes/" target="_blank" rel="noopener"><strong>welded mesh</strong></a> solutions in a variety of applications. Welded mesh offers unparalleled durability and adaptability, making it suitable for a wide range of applications, including perimeter fencing, animal enclosures, and agricultural protection. It is an excellent choice for ensuring sustainable farming practices and enhancing operational efficiency due to its adaptability to satisfy a variety of agricultural needs.</p> <p>The utilization of welded mesh in agriculture has significantly altered the methods of infrastructure development employed by farmers. The modular design of the farm enables it to rapidly adjust to evolving requirements due to its scalability and ease of installation. Whether used as trellises for vertical farming or to establish secure boundaries for cattle, welded mesh solutions provide a robust foundation that supports agricultural productivity and encourages optimal land use.</p> <h2>The Advantages of Utilizing Welded Mesh Systems</h2> <p>Farmers can derive numerous advantages from the implementation of welded mesh systems in order to optimize agricultural productivity. In addition to its structural strength and endurance, welded mesh enhances farm security, reduces labour expenses, and promotes environmentally friendly agricultural practices. These systems promote sustainable agriculture by optimizing agricultural yield through efficient land management and protection, while simultaneously reducing resource waste.</p> <p>Welded mesh systems provide benefits that extend beyond structural integrity for the purpose of enhancing farm management methods. By establishing secure boundaries and confinement areas, farmers mitigate the risks of wildlife encroachment and illicit entry. This proactive approach not only safeguards invaluable assets but also cultivates an environment that is conducive to sustainable agricultural practices. Moreover, the modular design of welded mesh systems enables the flexible application of these systems to a variety of agricultural applications, including modest family farms and substantial commercial operations.</p> <h2>An Innovative Producer of Perforated Metal That is Revolutionizing Agricultural Methods</h2> <p>Perforated metal, which is manufactured by industry leaders, is a critical factor in the evolution of agricultural techniques. It is a critical instrument for the construction of efficient fencing and enclosures due to its versatility and durability. Farmers may be guaranteed of low maintenance costs and long-term dependability due to its resistance to external factors.</p> <p>Perforated metal panels are meticulously crafted by specialized producers to withstand the severe conditions of agricultural settings. They provide robust barriers that effectively protect cattle and produce from external threats. The high-strength composition of these materials may provide farmers with the assurance of longevity and significant cost reductions in comparison to traditional fencing materials.</p> <h2>Benefits of Selecting a Reliable Steel Wire Supplier</h2> <p><a href="https://www.dukesmetal.com/product/wire-rope-and-cable/" target="_blank" rel="noopener"><strong>Steel wire supplier</strong></a> play a substantial role in the agricultural industry by providing the premium materials necessary for the production of welded mesh. Farmers can be confident that their products meet the highest quality standards due to their expertise in procurement and processing. These vendors enable the improvement of agricultural efficacy by offering a wide range of products, including intricate mesh designs for crop protection and durable fencing wires.</p> <p>It is imperative that producers seeking welded mesh solutions choose a reliable supplier of steel wire. These sources offer a wide range of wire varieties, including those that are flexible, corrosion-resistant, and strong, to accommodate specific agricultural requirements. Farmers are able to install durable fencing and enclosure systems that can withstand the test of time and environmental challenges due to their collaboration with perforated metal producers.</p>
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