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dc.contributor.authorLi, Weidong
dc.contributor.authorLiang, Yuchen
dc.contributor.authorLiu, Yiding
dc.date.accessioned2022-11-28T16:30:02Z
dc.date.available2022-11-28T16:30:02Z
dc.date.issued2022-11-24
dc.identifier.citationLi , W , Liang , Y & Liu , Y 2022 , ' Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation ' , Advanced Engineering Informatics , vol. 54 , no. 101817 , 101817 . https://doi.org/10.1016/j.aei.2022.101817
dc.identifier.issn1474-0346
dc.identifier.urihttp://hdl.handle.net/2299/25923
dc.description© 2022 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.aei.2022.101817
dc.description.abstractAdhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. Tostrengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samplesand re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporatedinto the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.en
dc.format.extent12
dc.format.extent1269788
dc.language.isoeng
dc.relation.ispartofAdvanced Engineering Informatics
dc.titleFailure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisationen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionMaterials and Structures
dc.description.statusPeer reviewed
dc.date.embargoedUntil2023-11-24
rioxxterms.versionofrecord10.1016/j.aei.2022.101817
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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