Data: Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling
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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.
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'Computational Fluid Dynamics Modelling', 'machine learning-based', 'Artificial neural networks'
DOI
10.17862/cranfield.rd.24427033
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CC BY 4.0