dc.contributor.author	Yin, Yi
dc.contributor.author	Tian, Yingtao
dc.contributor.author	Ding, Jialuo
dc.contributor.author	Mitchell, Tim
dc.contributor.author	Qin, Jian
dc.date.accessioned	2024-06-07T03:31:01Z
dc.date.available	2024-06-07T03:31:01Z
dc.date.issued	2023-10-26 15:38
dc.description.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.
dc.identifier.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
dc.identifier.doi	10.17862/cranfield.rd.24427033
dc.identifier.uri	https://dspace.lib.cranfield.ac.uk/handle/1826/22031
dc.publisher	Cranfield University
dc.relation.isreferencedby	https://doi.org/10.3390/s23218687'
dc.rights	CC BY 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	'Computational Fluid Dynamics Modelling'
dc.subject	'machine learning-based'
dc.subject	'Artificial neural networks'
dc.title	Data: Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling
dc.type	Dataset

Data: Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling

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