Feature-level data fusion for energy consumption analytics in additive manufacturing

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dc.contributor.authorHu, Fu
dc.contributor.authorLiu, Ying
dc.contributor.authorQin, Jian
dc.contributor.authorSun, Xianfang
dc.contributor.authorWitherell, Paul
dc.date.accessioned2021-05-13T09:53:38Z
dc.date.available2021-05-13T09:53:38Z
dc.date.issued2020-10-08
dc.description.abstractThe issue of Additive Manufacturing (AM) energy consumption is attracting attention in both industry and academia, particularly with the trending adoption of AM technologies in the manufacturing industry. It is crucial to analyze, understand, and manage the energy consumption of AM for better efficiency and sustainability. The energy consumption of AM systems is related to various correlated attributes in different phases of an AM process. Existing studies focus mainly on analyzing the impacts of different processing and material attributes, while factors related to design and working environment have not received the same amount of attention. Such factors involve features with various dimensions and nested structures that are difficult to handle in the analysis. To tackle these issues, a feature-level data fusion approach is proposed to integrate heterogeneous data to build an AM energy consumption model to uncover energy-relevant information and knowledge. A case study using real-world data collected from a selective laser sintering (SLS) system is presented to validate the proposed approach, and the results indicate that the fusion strategy achieves better performances on energy consumption prediction than the individual ones. Based on the analysis of feature importance, the design-relevant features are found to have significant impacts on AM energy consumption.en_UK
dc.identifier.citationHu F, Liu Y, Qin J, et al., (2020) Feature-level data fusion for energy consumption analytics in additive manufacturing. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 20-24 August 2020, Hong Kong, Chinaen_UK
dc.identifier.isbn978-1-7281-6905-7
dc.identifier.issn2161-8089
dc.identifier.urihttps://doi.org/10.1109/CASE48305.2020.9216947
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16677
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectEnergy consumptionen_UK
dc.subjectFeature extractionen_UK
dc.subjectData integrationen_UK
dc.subjectData modelsen_UK
dc.subjectSensorsen_UK
dc.subjectManufacturingen_UK
dc.subjectData miningen_UK
dc.titleFeature-level data fusion for energy consumption analytics in additive manufacturingen_UK
dc.typeConference paperen_UK

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