On optimizing the build orientation problem using genetic algorithm

Detalhes bibliográficos
Autor(a) principal: Matos, Marina A.
Data de Publicação: 2019
Outros Autores: Rocha, Ana Maria A. C., Pereira, Ana I.
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: https://hdl.handle.net/1822/62905
Resumo: Build orientation is a critical issue in Additive manufacturing (AM), where three-dimensional objects are created layer-by-layer directly from a 3D CAD model, since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduce, significantly, the building costs and will improve the object accuracy. This work presents the solutions obtained by the Genetic Algorithm (GA) in solving the part build orientation optimization problem, considering the staircase effect, support area characteristics and the building time of four models. Preliminary experiments show that GA gives competitive results in solving the build orientation problem when compared with other metaheuristics.
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spelling On optimizing the build orientation problem using genetic algorithmScience & TechnologyBuild orientation is a critical issue in Additive manufacturing (AM), where three-dimensional objects are created layer-by-layer directly from a 3D CAD model, since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduce, significantly, the building costs and will improve the object accuracy. This work presents the solutions obtained by the Genetic Algorithm (GA) in solving the part build orientation optimization problem, considering the staircase effect, support area characteristics and the building time of four models. Preliminary experiments show that GA gives competitive results in solving the build orientation problem when compared with other metaheuristics.This work has been supported and developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ao para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.American Institute of PhysicsUniversidade do MinhoMatos, Marina A.Rocha, Ana Maria A. C.Pereira, Ana I.2019-07-242019-07-24T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/1822/62905eng97807354185470094-243X10.1063/1.5114224info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T04:42:46Zoai:repositorium.sdum.uminho.pt:1822/62905Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:56:14.469698Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv On optimizing the build orientation problem using genetic algorithm
title On optimizing the build orientation problem using genetic algorithm
spellingShingle On optimizing the build orientation problem using genetic algorithm
Matos, Marina A.
Science & Technology
title_short On optimizing the build orientation problem using genetic algorithm
title_full On optimizing the build orientation problem using genetic algorithm
title_fullStr On optimizing the build orientation problem using genetic algorithm
title_full_unstemmed On optimizing the build orientation problem using genetic algorithm
title_sort On optimizing the build orientation problem using genetic algorithm
author Matos, Marina A.
author_facet Matos, Marina A.
Rocha, Ana Maria A. C.
Pereira, Ana I.
author_role author
author2 Rocha, Ana Maria A. C.
Pereira, Ana I.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Matos, Marina A.
Rocha, Ana Maria A. C.
Pereira, Ana I.
dc.subject.por.fl_str_mv Science & Technology
topic Science & Technology
description Build orientation is a critical issue in Additive manufacturing (AM), where three-dimensional objects are created layer-by-layer directly from a 3D CAD model, since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduce, significantly, the building costs and will improve the object accuracy. This work presents the solutions obtained by the Genetic Algorithm (GA) in solving the part build orientation optimization problem, considering the staircase effect, support area characteristics and the building time of four models. Preliminary experiments show that GA gives competitive results in solving the build orientation problem when compared with other metaheuristics.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-24
2019-07-24T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/62905
url https://hdl.handle.net/1822/62905
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9780735418547
0094-243X
10.1063/1.5114224
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Institute of Physics
publisher.none.fl_str_mv American Institute of Physics
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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