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

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dc.contributor.authorYin, Yi
dc.contributor.authorTian, Yingtao
dc.contributor.authorDing, Jialuo
dc.contributor.authorMitchell, Tim
dc.contributor.authorQin, Jian
dc.date.accessioned2023-10-27T11:24:20Z
dc.date.available2023-10-27T11:24:20Z
dc.date.issued2023-10-24
dc.description.abstractThe necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.en_UK
dc.description.sponsorshipLloyds Register Foundation; Joining 4.0 Innovation Centre (J4IC); Cranfield Universityen_UK
dc.identifier.citationYin Y, Tian Y, Ding J, et al., (2023) Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling, Sensors, Volume 23, Issue 21, October 2023, Article Number 8687en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s23218687
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20465
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectelectron beam weldingen_UK
dc.subjectcomputational fluid dynamics modellingen_UK
dc.subjectmachine learningen_UK
dc.subjectartificial neural networksen_UK
dc.subjectpenetration depth predictionen_UK
dc.subjectbeam characterisationen_UK
dc.titlePrediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modellingen_UK
dc.typeArticleen_UK

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