Data: Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling
Date
2023-10-26 15:38
Supervisor/s
Journal Title
Journal ISSN
Volume Title
Publisher
Cranfield University
Department
Type
Dataset
ISSN
Format
Free to read from
Citation
Yin, Yi; Tian, Yingtao; Ding, Jialuo; Mitchell, Tim; Qin, Jian (2023). Data: Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.24427033
Abstract
Electron beam probing data: beam characteristics of raidii at welding direction and cross-section direction. Experiments setup: 40–60 kV for the accelerating voltage, 25–45 mA for the beam current, and a welding speed of 500–700 mm/min.
Description
Software Description
Software Language
Github
Keywords
'Computational Fluid Dynamics Modelling', 'machine learning-based', 'Artificial neural networks'
DOI
10.17862/cranfield.rd.24427033
Rights
CC BY 4.0