A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature

Detalhes bibliográficos
Autor(a) principal: Silva, João Miguel Carvalho Lopes
Data de Publicação: 2023
Outros Autores: Macedo, Rafael, Morais, António Francisco Nogueira, Ribeiro, João Paulo Silva, Mould, Sacha Trevelyan, Cruz, Paulo J. S., Figueiredo, Bruno
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/88101
Resumo: CEES 2023 | 2nd International Conference on Construction, Energy, Environment & Sustainability
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spelling A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature3DCPAdditive manufacturingRobotic fabricationMachine learningComputer visionEngenharia e Tecnologia::Engenharia CivilProdução e consumo sustentáveisCEES 2023 | 2nd International Conference on Construction, Energy, Environment & Sustainability3D Concrete Printing (3DCP) is a fast-paced process that requires multiple parameters to be accurately tuned in order to guarantee a high-quality print. Despite the technological advances in 3DCP, the control of this process still requires frequent manual intervention, which can lead to error, inconsistency under different executions and the reliance on human expertise to accommodate changes in the characteristics of the printing environment. In order to bypass these issues and approximate an autonomous self-correcting process, machine learning vision models have been applied, particularly in polymer extrusion, to extract and analyse information from captured images or video - colour and texture, geometric deviation, defect recognition, amongst others - and consequently introduce corrections to the print settings - motion path, speed, acceleration, material flow or temperature - to improve the print quality. In this paper, we first review related techniques, which include both real-time and offline correction approaches. We then present a comprehensive computer vision system for real-time control suited to the characteristics of robotic 3DCP. Within this scope, we focus on a particular ML component of this system - Speed Control - that manages layer width through direct access to robot motion speed or material flow rate. The proposed framework has three main components: (1) a data acquisition and processing pipeline for extracting printing parameters and build a synthetic training dataset, (2) a machine learning model for tuning parameters in real time, and (3) a depth camera mounted on a custom 3D-printed rotary mechanism for close-range monitoring of the printed layer shape.This work was also supported under the base funding project of the DTx CoLAB - Collaborative Laboratory, under the Missão Interface of the Recovery and Resilience Plan (PRR), integrated in the notice 01/C05-i02/2022, which aims to deepen the effort to expand and consolidate the network of interface institutions between the academic, scientific and technological system and the Portuguese business fabric.Institute for Research and Technological Development in Construction Sciences (ITECONS)Universidade do MinhoSilva, João Miguel Carvalho LopesMacedo, RafaelMorais, António Francisco NogueiraRibeiro, João Paulo SilvaMould, Sacha TrevelyanCruz, Paulo J. S.Figueiredo, Bruno20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/88101engSilva, J.; Macedo, R.; Morais, A.; Ribeiro, J.; Mould, S.; Cruz, P.J.S.; Figueiredo, B. (2023). “A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature”. In: CEES 2023 | 2nd International Conference on Construction, Energy, Environment & Sustainability, Itecons, Coimbra.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-20T01:20:51Zoai:repositorium.sdum.uminho.pt:1822/88101Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:52:16.842415Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
title A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
spellingShingle A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
Silva, João Miguel Carvalho Lopes
3DCP
Additive manufacturing
Robotic fabrication
Machine learning
Computer vision
Engenharia e Tecnologia::Engenharia Civil
Produção e consumo sustentáveis
title_short A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
title_full A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
title_fullStr A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
title_full_unstemmed A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
title_sort A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature
author Silva, João Miguel Carvalho Lopes
author_facet Silva, João Miguel Carvalho Lopes
Macedo, Rafael
Morais, António Francisco Nogueira
Ribeiro, João Paulo Silva
Mould, Sacha Trevelyan
Cruz, Paulo J. S.
Figueiredo, Bruno
author_role author
author2 Macedo, Rafael
Morais, António Francisco Nogueira
Ribeiro, João Paulo Silva
Mould, Sacha Trevelyan
Cruz, Paulo J. S.
Figueiredo, Bruno
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Silva, João Miguel Carvalho Lopes
Macedo, Rafael
Morais, António Francisco Nogueira
Ribeiro, João Paulo Silva
Mould, Sacha Trevelyan
Cruz, Paulo J. S.
Figueiredo, Bruno
dc.subject.por.fl_str_mv 3DCP
Additive manufacturing
Robotic fabrication
Machine learning
Computer vision
Engenharia e Tecnologia::Engenharia Civil
Produção e consumo sustentáveis
topic 3DCP
Additive manufacturing
Robotic fabrication
Machine learning
Computer vision
Engenharia e Tecnologia::Engenharia Civil
Produção e consumo sustentáveis
description CEES 2023 | 2nd International Conference on Construction, Energy, Environment & Sustainability
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/88101
url https://hdl.handle.net/1822/88101
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Silva, J.; Macedo, R.; Morais, A.; Ribeiro, J.; Mould, S.; Cruz, P.J.S.; Figueiredo, B. (2023). “A Machine Learning and computer-vision framework for real-time control in 3DCP: layer morphology as a design feature”. In: CEES 2023 | 2nd International Conference on Construction, Energy, Environment & Sustainability, Itecons, Coimbra.
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 Institute for Research and Technological Development in Construction Sciences (ITECONS)
publisher.none.fl_str_mv Institute for Research and Technological Development in Construction Sciences (ITECONS)
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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