An artificial intelligence planning approach to manufacturing feature recognition

Autores
Marchetta Fernandez, Martin Gonzalo; Forradellas, Raymundo Quilez
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.
Fil: Marchetta Fernandez, Martin Gonzalo. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Materia
Feature recognition
Process planning
AI-Planning
PLM
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/242322
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/242322
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network_name_str CONICET Digital (CONICET)
spelling An artificial intelligence planning approach to manufacturing feature recognitionMarchetta Fernandez, Martin GonzaloForradellas, Raymundo QuilezFeature recognitionProcess planningAI-PlanningPLMhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.Fil: Marchetta Fernandez, Martin Gonzalo. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaElsevier2010-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/242322Marchetta Fernandez, Martin Gonzalo; Forradellas, Raymundo Quilez; An artificial intelligence planning approach to manufacturing feature recognition; Elsevier; Computer-aided Design; 42; 3; 3-2010; 248-2560010-4485CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cad.2009.11.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:44Zoai:ri.conicet.gov.ar:11336/242322instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:44.337CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An artificial intelligence planning approach to manufacturing feature recognition
title An artificial intelligence planning approach to manufacturing feature recognition
spellingShingle An artificial intelligence planning approach to manufacturing feature recognition
Marchetta Fernandez, Martin Gonzalo
Feature recognition
Process planning
AI-Planning
PLM
title_short An artificial intelligence planning approach to manufacturing feature recognition
title_full An artificial intelligence planning approach to manufacturing feature recognition
title_fullStr An artificial intelligence planning approach to manufacturing feature recognition
title_full_unstemmed An artificial intelligence planning approach to manufacturing feature recognition
title_sort An artificial intelligence planning approach to manufacturing feature recognition
dc.creator.none.fl_str_mv Marchetta Fernandez, Martin Gonzalo
Forradellas, Raymundo Quilez
author Marchetta Fernandez, Martin Gonzalo
author_facet Marchetta Fernandez, Martin Gonzalo
Forradellas, Raymundo Quilez
author_role author
author2 Forradellas, Raymundo Quilez
author2_role author
dc.subject.none.fl_str_mv Feature recognition
Process planning
AI-Planning
PLM
topic Feature recognition
Process planning
AI-Planning
PLM
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.
Fil: Marchetta Fernandez, Martin Gonzalo. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
description Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.
publishDate 2010
dc.date.none.fl_str_mv 2010-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/242322
Marchetta Fernandez, Martin Gonzalo; Forradellas, Raymundo Quilez; An artificial intelligence planning approach to manufacturing feature recognition; Elsevier; Computer-aided Design; 42; 3; 3-2010; 248-256
0010-4485
CONICET Digital
CONICET
url http://hdl.handle.net/11336/242322
identifier_str_mv Marchetta Fernandez, Martin Gonzalo; Forradellas, Raymundo Quilez; An artificial intelligence planning approach to manufacturing feature recognition; Elsevier; Computer-aided Design; 42; 3; 3-2010; 248-256
0010-4485
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cad.2009.11.007
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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