On optimizing the build orientation problem using genetic algorithm
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2019 |
| Outros Autores: | , |
| 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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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 instacron:RCAAP |
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FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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RCAAP |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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info@rcaap.pt |
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