Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

Detalhes bibliográficos
Autor(a) principal: Peres, Ricardo Silva
Data de Publicação: 2020
Outros Autores: Jia, Xiaodong, Lee, Jay, Sun, Keyi, Colombo, Armando Walter, Barata, José
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/115087
Resumo: UIDB/00066/2020
id RCAP_68f5a6a8a6d04fe02b6eb5b5a349d3a2
oai_identifier_str oai:run.unl.pt:10362/115087
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and OutlookArtificial IntelligenceDigital TransformationFrameworkGuidelinesIndustry 4.0ManufacturingSystematic ReviewComputer Science(all)Materials Science(all)Engineering(all)UIDB/00066/2020The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus two-fold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven company-wide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.CTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasDEE2010-C2 Robótica e Manufactura Integrada por ComputadorDEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNPeres, Ricardo SilvaJia, XiaodongLee, JaySun, KeyiColombo, Armando WalterBarata, José2021-04-06T22:17:04Z2020-12-072020-12-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/115087engPURE: 29075933https://doi.org/10.1109/ACCESS.2020.3042874info: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:RCAAP2024-03-11T04:57:44Zoai:run.unl.pt:10362/115087Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:40.886397Repositório 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 Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
title Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
spellingShingle Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
Peres, Ricardo Silva
Artificial Intelligence
Digital Transformation
Framework
Guidelines
Industry 4.0
Manufacturing
Systematic Review
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
title_full Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
title_fullStr Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
title_full_unstemmed Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
title_sort Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
author Peres, Ricardo Silva
author_facet Peres, Ricardo Silva
Jia, Xiaodong
Lee, Jay
Sun, Keyi
Colombo, Armando Walter
Barata, José
author_role author
author2 Jia, Xiaodong
Lee, Jay
Sun, Keyi
Colombo, Armando Walter
Barata, José
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv CTS - Centro de Tecnologia e Sistemas
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
DEE2010-C2 Robótica e Manufactura Integrada por Computador
DEE - Departamento de Engenharia Electrotécnica e de Computadores
RUN
dc.contributor.author.fl_str_mv Peres, Ricardo Silva
Jia, Xiaodong
Lee, Jay
Sun, Keyi
Colombo, Armando Walter
Barata, José
dc.subject.por.fl_str_mv Artificial Intelligence
Digital Transformation
Framework
Guidelines
Industry 4.0
Manufacturing
Systematic Review
Computer Science(all)
Materials Science(all)
Engineering(all)
topic Artificial Intelligence
Digital Transformation
Framework
Guidelines
Industry 4.0
Manufacturing
Systematic Review
Computer Science(all)
Materials Science(all)
Engineering(all)
description UIDB/00066/2020
publishDate 2020
dc.date.none.fl_str_mv 2020-12-07
2020-12-07T00:00:00Z
2021-04-06T22:17:04Z
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 http://hdl.handle.net/10362/115087
url http://hdl.handle.net/10362/115087
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 29075933
https://doi.org/10.1109/ACCESS.2020.3042874
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.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
_version_ 1799138037874032640

Registros relacionados