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3 results for “arc 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>
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>
EBSD datasets for low-alloy steel weld metals - Arc, Laser, and Laser-hybrid welding
<p>Example open access datasets for low-alloy structural steel weld metals, previously published in ref. [1]. The datasets are also related to the International Institute of Welding Commission C-IX, document nr. IX-L-1239-2021.</p> <p>Files included:</p> <ul> <li>The nomenclature of the files corresponds to what is used in ref. [1].</li> <li>CV_3.crc/cpr: Conventional arc weld, 3/5 mm t-joint</li> <li>HY_1.crc/cpr: Laser-hybrid weld, 3mm butt-joint</li> <li>LA_1.crc/cpr: Laser weld, 3mm butt-joint</li> </ul> <p>The methodology for analysing grain size and dislocation sub-structures is found at: <a href="../record/5053377">https://zenodo.org/record/5053377</a> and <a href="https://doi.org/10.5281/zenodo.4430623">https://doi.org/10.5281/zenodo.4430623</a></p> <p>For further information visit: Aalto University Wiki - <a href="https://wiki.aalto.fi/display/GSMUM">https://wiki.aalto.fi/display/GSMUM</a> and <a href="https://wiki.aalto.fi/display/EMDIDS">https://wiki.aalto.fi/display/EMDIDS</a></p> <p><strong>Refererences:</strong></p> <ul> <li>[1] Materials Science and Engineering: A, 2014; 592: 28-39, <a href="http://dx.doi.org/10.1016/j.msea.2013.10.094">http://dx.doi.org/10.1016/j.msea.2013.10.094</a></li> <li>[2] Welding in the World. 2016; 60: 673-678. <a href="http://dx.doi.org/10.1007/s40194-016-0318-8">http://dx.doi.org/10.1007/s40194-016-0318-8</a></li> <li>[3] Ultramicroscopy 2021, Volume 222, <a href="https://doi.org/10.1016/j.ultramic.2021.113203">https://doi.org/10.1016/j.ultramic.2021.113203</a></li> </ul>
ScienceDex guides
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