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Optimisation of ultrasonically welded joints through machine learning

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dc.contributor.author Mongan, P.G.
dc.contributor.author Hinchy, Eoin
dc.contributor.author O'Dowd, Noel P.
dc.contributor.author McCarthy, Conor T.
dc.date.accessioned 2020-11-30T14:03:44Z
dc.date.available 2020-11-30T14:03:44Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/9493
dc.description peer-reviewed en_US
dc.description.abstract The quality of joint achievable through ultrasonic welding is highly dependent on the process input parameters. In this study an artificial neural network (ANN) is combined with a genetic algorithm (GA) to develop a high-fidelity model for predicting the strength of ultrasonically welded joints. Initial weights of the ANN were optimized using the GA. The model was then trained by the Levenberg-Marquardt algorithm on 27 training experiments and validated on 10 experiments. The model demonstrated a high level of accuracy with a mean relative error of 6.79% on validation data and a correlation coefficient of 0.9827 for all 37 experiments. en_US
dc.language.iso eng en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Procedia CIRP;pp.527-531
dc.relation.uri http://dx.doi.org/10.1016/j.procir.2020.04.060
dc.subject ultrasonic wedling en_US
dc.subject machine learning en_US
dc.subject artificial neural networks en_US
dc.title Optimisation of ultrasonically welded joints through machine learning en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1016/j.procir.2020.04.060
dc.contributor.sponsor SFI en_US
dc.relation.projectid 16/RC/3918 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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