SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION

Titanium alloys are attractive materials due to their unique high strength, excellent performance at elevated temperatures and exceptional corrosion resistance. The aerospace and military industries are the main users of this material. Titanium alloys are classified as difficult machining materials....

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Detalles Bibliográficos
Autor: Pedro Pérez Villanueva
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2008
País:México
Institución:Corporación Mexicana de Investigación en Materiales
Repositorio:Repositorio COMIMSA
Idioma:inglés
OAI Identifier:oai:comimsa.repositorioinstitucional.mx:1022/394
Acceso en línea:http://comimsa.repositorioinstitucional.mx/jspui/handle/1022/394
Access Level:acceso abierto
Palabra clave:info:eu-repo/classification/ARTÍCULO/NEURAL NETWORK
info:eu-repo/classification/cti/7
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spelling SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSIONPedro Pérez Villanuevainfo:eu-repo/classification/ARTÍCULO/NEURAL NETWORKinfo:eu-repo/classification/cti/7info:eu-repo/classification/cti/7Titanium alloys are attractive materials due to their unique high strength, excellent performance at elevated temperatures and exceptional corrosion resistance. The aerospace and military industries are the main users of this material. Titanium alloys are classified as difficult machining materials. The correct parameters of machining are a hard setting, actually researches are looking to develop new models to predict and optimize these parameters. The surface roughness (Ra) in turning of a titanium alloy machining Ti 6Al 4V was predicted using neural network and linear regression is shown. The machining tests were carried out using PVD (TiAIN) coated carbide inserts under different cutting conditions. Confidence intervals were estimated in the model to get correct results. There are various machining parameters and they have an effect on the surface roughness. A set of initial parameters in finished turning of Ti 6Al 4V obtained from literature have been used. These parameters are cutting speed, feed rate and depth of cut. The results showed the advantages of use a Neural –Statistical approach to analyze the variables and to model the machining process.2008-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://comimsa.repositorioinstitucional.mx/jspui/handle/1022/394reponame:Repositorio COMIMSAinstname:Corporación Mexicana de Investigación en Materialesinstacron:COMIMSAengcitation:SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION I. Escamilla, L. Torres, P. Perez, and P. Zambrano. Proceedings of the 13th Annual International Conference on Industrial Engineering Theory, Applications and Practice Las Vegas, Nevada September 7-10, 2008info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0oai:comimsa.repositorioinstitucional.mx:1022/3942024-09-19T02:59:03Z
dc.title.none.fl_str_mv SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
title SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
spellingShingle SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
Pedro Pérez Villanueva
info:eu-repo/classification/ARTÍCULO/NEURAL NETWORK
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/7
title_short SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
title_full SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
title_fullStr SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
title_full_unstemmed SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
title_sort SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
dc.creator.none.fl_str_mv Pedro Pérez Villanueva
author Pedro Pérez Villanueva
author_facet Pedro Pérez Villanueva
author_role author
dc.subject.none.fl_str_mv info:eu-repo/classification/ARTÍCULO/NEURAL NETWORK
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/7
topic info:eu-repo/classification/ARTÍCULO/NEURAL NETWORK
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/7
description Titanium alloys are attractive materials due to their unique high strength, excellent performance at elevated temperatures and exceptional corrosion resistance. The aerospace and military industries are the main users of this material. Titanium alloys are classified as difficult machining materials. The correct parameters of machining are a hard setting, actually researches are looking to develop new models to predict and optimize these parameters. The surface roughness (Ra) in turning of a titanium alloy machining Ti 6Al 4V was predicted using neural network and linear regression is shown. The machining tests were carried out using PVD (TiAIN) coated carbide inserts under different cutting conditions. Confidence intervals were estimated in the model to get correct results. There are various machining parameters and they have an effect on the surface roughness. A set of initial parameters in finished turning of Ti 6Al 4V obtained from literature have been used. These parameters are cutting speed, feed rate and depth of cut. The results showed the advantages of use a Neural –Statistical approach to analyze the variables and to model the machining process.
publishDate 2008
dc.date.none.fl_str_mv 2008-09
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://comimsa.repositorioinstitucional.mx/jspui/handle/1022/394
url http://comimsa.repositorioinstitucional.mx/jspui/handle/1022/394
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv citation:SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION I. Escamilla, L. Torres, P. Perez, and P. Zambrano. Proceedings of the 13th Annual International Conference on Industrial Engineering Theory, Applications and Practice Las Vegas, Nevada September 7-10, 2008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositorio COMIMSA
instname:Corporación Mexicana de Investigación en Materiales
instacron:COMIMSA
instname_str Corporación Mexicana de Investigación en Materiales
instacron_str COMIMSA
institution COMIMSA
reponame_str Repositorio COMIMSA
collection Repositorio COMIMSA
repository.name.fl_str_mv
repository.mail.fl_str_mv
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