Modelling laser milling of microcavities for the manufacturing of DES with ensembles

A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of t...

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Autores: Santos, Pedro Rosa, Teixidor Ezpeleta, Daniel, Maudes, Jesús M., Ciurana, Quim de
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/10185
Acceso en línea:http://hdl.handle.net/10256/10185
Access Level:acceso abierto
Palabra clave:Làsers -- Aplicacions industrials
Lasers -- Industrial applications
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spelling Modelling laser milling of microcavities for the manufacturing of DES with ensemblesSantos, Pedro RosaTeixidor Ezpeleta, DanielMaudes, Jesús M.Ciurana, Quim deLàsers -- Aplicacions industrialsLasers -- Industrial applicationsA set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial taskThe authors would like to express their gratitude to the GREP research group at the University of Girona and the Tecnologico de Monterrey for access to their facilities during the experiments. This work was partially funded through Grants from the IREBID Project (FP7-PEOPLE-2009-IRSES-247476) of the European Commission and Projects TIN2011-24046 and TECNIPLAD (DPI2009-09852) of the Spanish Ministry of Economy and CompetitivenessHindawi Publishing CorporationMinisterio de Ciencia e Innovación (Espanya)2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10256/10185http://hdl.handle.net/10256/10185© Journal of Applied Mathematics, 2014, vol. 2014, p. 439091Articles publicats (D-EMCI)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1155/2014/439091info:eu-repo/semantics/altIdentifier/issn/1110-757Xinfo:eu-repo/semantics/altIdentifier/eissn/1687-0042info:eu-repo/grantAgreement/MICINN//DPI2009-09852info:eu-repo/grantAgreement/EC/FP7/247476Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/101852026-05-29T05:05:01Z
dc.title.none.fl_str_mv Modelling laser milling of microcavities for the manufacturing of DES with ensembles
title Modelling laser milling of microcavities for the manufacturing of DES with ensembles
spellingShingle Modelling laser milling of microcavities for the manufacturing of DES with ensembles
Santos, Pedro Rosa
Làsers -- Aplicacions industrials
Lasers -- Industrial applications
title_short Modelling laser milling of microcavities for the manufacturing of DES with ensembles
title_full Modelling laser milling of microcavities for the manufacturing of DES with ensembles
title_fullStr Modelling laser milling of microcavities for the manufacturing of DES with ensembles
title_full_unstemmed Modelling laser milling of microcavities for the manufacturing of DES with ensembles
title_sort Modelling laser milling of microcavities for the manufacturing of DES with ensembles
dc.creator.none.fl_str_mv Santos, Pedro Rosa
Teixidor Ezpeleta, Daniel
Maudes, Jesús M.
Ciurana, Quim de
author Santos, Pedro Rosa
author_facet Santos, Pedro Rosa
Teixidor Ezpeleta, Daniel
Maudes, Jesús M.
Ciurana, Quim de
author_role author
author2 Teixidor Ezpeleta, Daniel
Maudes, Jesús M.
Ciurana, Quim de
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (Espanya)
dc.subject.none.fl_str_mv Làsers -- Aplicacions industrials
Lasers -- Industrial applications
topic Làsers -- Aplicacions industrials
Lasers -- Industrial applications
description A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/10185
http://hdl.handle.net/10256/10185
url http://hdl.handle.net/10256/10185
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1155/2014/439091
info:eu-repo/semantics/altIdentifier/issn/1110-757X
info:eu-repo/semantics/altIdentifier/eissn/1687-0042
info:eu-repo/grantAgreement/MICINN//DPI2009-09852
info:eu-repo/grantAgreement/EC/FP7/247476
dc.rights.none.fl_str_mv Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
dc.source.none.fl_str_mv © Journal of Applied Mathematics, 2014, vol. 2014, p. 439091
Articles publicats (D-EMCI)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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