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dc.rights.licenseAttribution 4.0 International
dc.contributor.authorMcCloskey, Alex
dc.contributor.authorLlavori, Inigo
dc.contributor.otherViswanathan, V.
dc.contributor.otherMathur, Ruchir
dc.contributor.otherNguyen, Dinh T.
dc.contributor.otherHaque Faisal, Nadimul
dc.contributor.otherPrathuru, Anil
dc.contributor.otherMurphy, Adrian
dc.contributor.otherTiwari, Ashutosh
dc.contributor.otherMatthews, Allan
dc.contributor.otherAgrawal, Anupam
dc.contributor.otherGoel, Saurav
dc.date.accessioned2024-02-02T08:53:02Z
dc.date.available2024-02-02T08:53:02Z
dc.date.issued2024
dc.identifier.issn1096-1216
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=175901
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6196
dc.description.abstractThermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in digitalisation of thermal spraying process lies in instrumenting the manufacturing platform as the process includes harsh conditions, including UV Rays, high-plasma temperature, dusty chemical environment, and spray booth inaccessibility. This paper introduces a novel application of machine learning to the acoustic emission spectra of thermal spraying. By transitioning from the amplitude-time domain to a Fourier-transformed frequency-time domain, it is possible to predict anomalies in real-time, a crucial step towards sustainable material and manufacturing digitalization. Our experimental results also indicate that this method is suitable for industrial applications by generating useful data that can be used to develop Visual Geometry Group (VGG) transfer learning models to overcome the traditional limitations of convoluted neural networks (CNN).
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2024 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectThermal spray
dc.subjectAcoustic
dc.subjectDigitalisation
dc.titleMachine learning model of acoustic signatures: Towards digitalised thermal spray manufacturing
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2
dcterms.sourceMechanical Systems and Signal Processing
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.1016/j.ymssp.2023.111030
local.contributor.otherinstitutionhttps://ror.org/02vwnat91
local.contributor.otherinstitutionhttps://ror.org/04f0qj703
local.contributor.otherinstitutionhttps://ror.org/026zzn846
local.contributor.otherinstitutionhttps://ror.org/05krs5044
local.contributor.otherinstitutionhttps://ror.org/027m9bs27
local.contributor.otherinstitutionhttps://ror.org/01f5ytq51
local.contributor.otherinstitutionhttps://ror.org/04q2jes40
local.source.detailsVol. 208. Artículo 111030, 2024
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International