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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorARRAZOLA, PEDRO JOSE
dc.contributor.otherKugalur-Palanisamy, Nithyaraaj
dc.contributor.otherRivière-Lorphèvre, Edouard
dc.contributor.otherGobert, Maxime
dc.contributor.otherBriffoteaux, Guillaume
dc.contributor.otherTuyttens, Daniel
dc.contributor.otherDucobu, François
dc.date.accessioned2022-07-07T10:41:38Z
dc.date.available2022-07-07T10:41:38Z
dc.date.issued2022
dc.identifier.issn2075-4701en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168078en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5633
dc.description.abstractThe application of artificial intelligence and increasing high-speed computational performance is still not fully explored in the field of numerical modeling and simulation of machining processes. The efficiency of the numerical model to predict the observables depends on various inputs. The most important and challenging inputs are the material behavior of the work material and the friction conditions during the cutting operation. The parameters of the material model and the friction model have a decisive impact on the simulated results. To reduce the expensive experimentation cost that gives limited data for the parameters, an inverse methodology to identify the parameter values of those inputs is suggested to potentially have data of better quality. This paper introduces a novel approach for the inverse identification of model parameters by implementing the Efficient Global Optimization algorithm. In this work, a method relying on a complete automated Finite Element simulation-based optimization algorithm is implemented to inversely identify the value of the Johnson–Cook (JC) parameters and Coulomb’s friction coefficient correlatively, where the objective function is defined as minimizing the error difference between experimental and numerical results. The Ti6Al4V Grade 5 alloy material is considered as a work material, and the identified parameters sets are validated by comparing the simulated results with experimental results. The developed automation process reduces the computation time and eliminating human errors. The identified model parameters value predicts the cutting force as 169 N/mm (2% deviation from experiments), feed force as 55 N/mm (7% deviation from experiments), and chip thickness as 0.150 mm (11% deviation from experiments). Overall, the identified model parameters set improves the prediction accuracy of the finite element model by 32% compared with the best-identified parameters set in the literature.en
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2022 by the authors. Licensee MDPIen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectOrthogonal cuttingen
dc.subjectconstitutive modelsen
dc.subjectparameters seten
dc.subjectFinite element modellingen
dc.subjectAutomationen
dc.subjectartificial intelligenceen
dc.subjectBayesian optimizationen
dc.subjectTi6Al4Ven
dc.subjectmachine learningen
dc.subjectsurrogate modelen
dc.titleIdentification of the Parameter Values of the Constitutive and Friction Models in Machining Using EGO Algorithm: Application to Ti6Al4Ven
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceMetalsen
local.contributor.groupMecanizado de alto rendimientoes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/met12060976en
local.contributor.otherinstitutionhttps://ror.org/02qnnz951es
local.source.detailsVol. 12. N. 6. N. artículo 976, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution 4.0 International
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