Deep fusion for energy consumption prediction in additive manufacturing

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dc.contributor.authorHu, Fu
dc.contributor.authorQin, Jian
dc.contributor.authorLi, Yixin
dc.contributor.authorLiu, Ying
dc.contributor.authorSun, Xianfang
dc.date.accessioned2021-12-22T14:09:38Z
dc.date.available2021-12-22T14:09:38Z
dc.date.issued2021-11-26
dc.description.abstractOwing to the increasing trend of additive manufacturing (AM) technologies being employed in the manufacturing industry, the issue of AM energy consumption attracts attention in both industry and academia. The energy consumption of AM systems is affected by various factors. These factors involve features with different dimensions and structures which are hard to tackle in the analysis. In this work, a data fusion approach is proposed for energy consumption prediction based on CNN-LSTM (convolutional neural network and long short-term memory) model. A case study was conducted on an SLS system by using the proposed methodology, achieving the RMSE of 8.143 Wh/g in prediction.en_UK
dc.identifier.citationHu F, Qin J, Li Y, et al., (2021) Deep fusion for energy consumption prediction in additive manufacturing. Procedia CIRP, Volume 104, pp. 1878-1883. 54th CIRP Conference on Manufacturing Systems 2021 (CIRP CMS 2021), 22-24 September 2021, Virtual Eventen_UK
dc.identifier.issn2212-8271
dc.identifier.urihttps://doi.org/10.1016/j.procir.2021.11.317
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17351
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAdditive Manufacturingen_UK
dc.subjectData Fusionen_UK
dc.subjectEnergy Consumptionen_UK
dc.subjectMachine Learningen_UK
dc.subjectConvolutional Neural Networken_UK
dc.titleDeep fusion for energy consumption prediction in additive manufacturingen_UK
dc.typeConference paperen_UK

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