Logo do repositório
 
Publicação

A review of applied artificial intelligence in manufacturing: Emergent AI models in cyber–physical systems for manufacturing

2026Artigo científico Acesso aberto
Ver registo simples
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia Mecânica
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorRocha Varela, Maria Leonilde
dc.contributor.authorManupati, Vijaya Kumar
dc.contributor.authorPinheiro, Pedro
dc.contributor.authorPutnik, Goran
dc.contributor.authorFerreira, Luís
dc.contributor.authorAlves, Cátia
dc.contributor.authorÁvila, Paulo
dc.contributor.authorCastro, Helio
dc.date.accessioned2026-07-03T13:26:42Z
dc.date.available2026-07-03T13:26:42Z
dc.date.issued2026
dc.description.abstractThe integration of artificial intelligence (AI) is a cornerstone of Industry 4.0, driving significant gains in automation, efficiency, and adaptability. In parallel, manufacturing environments are evolving into cyber–physical systems (CPS), where physical processes are deeply integrated with computational intelligence. While machine learning and deep learning techniques have become standard practice in manufacturing CPS, the emergence of advanced and foundation AI models—such as reinforcement learning, agent-based AI systems, large language models, and neuro-symbolic approaches—brings fresh opportunities and challenges that are not fully understandable. This paper offers a comprehensive systematic literature review (SLR) on AI applications in manufacturing cyber–physical systems, with a particular focus on the role, maturity, and industrial readiness of emergent AI models. Following the PRISMA 2020 guidelines, a structured search was carried out in Scopus andWeb of Science, producing over 4200 publications, out of which a final set of 172 publications were retained following a rigorous multi-stage screening and eligibility process. We analysed the selected literature through complementary descriptive, longitudinal, and mapping syntheses to identify publication trends, paradigm evolution, and relationships between AI paradigms and manufacturing functions. Our findings show a clear transition from rule-based and conventional machine learning approaches toward more adaptive, decentralized, and learning-driven AI paradigms. However, despite their conceptual suitability for complex and dynamic manufacturing environments, emergent AI models are mostly limited to experimental, hybrid, or decision-support contexts, with limited integration into core manufacturing operations. Critical research gaps regarding the industrial readiness of these models—specifically concerning integration frameworks, empirical validation, safety, and trust—are identified. Furthermore, the study outlines future research directions for advancing the next generation of intelligent and autonomous manufacturing CPS. Overall, this review underscores the rapid growth and current fragmentation of the field, highlighting the need for more integrative and production-ready AI frameworks in the evolution of manufacturing CPS.eng
dc.description.sponsorshipThis work has been supported by FCT—Foundation for Science and Technology—within the R&D Unit Project Scope UID/00319/2025—Centro ALGORITMI (ALGORITMI/UM https://doi.org/10.54499/UID/00319/2025). This paper was also funded by national funds and FCT/MCTES (PIDDAC), through the FCT under the scope of the project UID/05549/2025 (https://doi.org/10.54499/UID/05549/2025).
dc.identifier.citationVarela, L., Putnik, G. D., Ferreira, L., Kumar Manupati, V., Pinheiro, P., Alves, C., Avila, P., & Castro, H. (2026). A Review of Applied Artificial Intelligence in Manufacturing: Emergent AI Models in Cyber–Physical Systems for Manufacturing. Future Internet, 18(5), 253. https://doi.org/10.3390/fi18050253
dc.identifier.doi10.3390/fi18050253
dc.identifier.issn1999-5903
dc.identifier.urihttp://hdl.handle.net/10400.22/32542
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/1999-5903/18/5/253
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCyber-physical systems
dc.subjectManufacturing systems
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectEmergent AI models
dc.subjectSystematic literature review
dc.titleA review of applied artificial intelligence in manufacturing: Emergent AI models in cyber–physical systems for manufacturingeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue5
oaire.citation.titleFuture Internet
oaire.citation.volume18
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRocha Varela
person.familyNamePutnik
person.familyNameFerreira
person.familyNameAlves
person.familyNameÁvila
person.familyNameCastro
person.givenNameMaria Leonilde
person.givenNameGoran
person.givenNameLuís
person.givenNameCátia
person.givenNamePaulo
person.givenNameHelio
person.identifier1518795
person.identifier143491
person.identifier631747
person.identifier.ciencia-id161F-2986-0DA3
person.identifier.ciencia-idC21F-57E6-B727
person.identifier.ciencia-idAD10-8EAE-8EE2
person.identifier.ciencia-id9C1E-47E5-2D8A
person.identifier.ciencia-id5F17-10EE-C0CE
person.identifier.orcid0000-0002-2299-1859
person.identifier.orcid0000-0003-3378-6866
person.identifier.orcid0000-0001-9635-5372
person.identifier.orcid0000-0001-6445-5475
person.identifier.orcid0000-0001-8420-0875
person.identifier.orcid0000-0001-5712-9954
person.identifier.ridM-7580-2013
person.identifier.ridB-1428-2014
person.identifier.ridAAC-7395-2020
person.identifier.ridE-2348-2019
person.identifier.scopus-author-id7101656080
person.identifier.scopus-author-id8609137800
person.identifier.scopus-author-id57193618060
person.identifier.scopus-author-id55909979000
person.identifier.scopus-author-id8609138000
person.identifier.scopus-author-id55175839700
relation.isAuthorOfPublicationbf958898-a11a-42cf-a50a-e4b84707fa7b
relation.isAuthorOfPublication51158e6e-c167-4c5b-9331-3274afd15544
relation.isAuthorOfPublication0e38d198-7cf1-4d4f-bf3f-fb2a02ef8bc2
relation.isAuthorOfPublication1d3550ed-5cee-431c-9651-c8905ff3890d
relation.isAuthorOfPublication444efa0f-ca28-4743-8e78-7d31b0abde47
relation.isAuthorOfPublication21b8fcc5-040e-4238-91e5-499ec0cc0bff
relation.isAuthorOfPublication.latestForDiscoverybf958898-a11a-42cf-a50a-e4b84707fa7b

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
ART_ISEP_futureinternet-18-00253.pdf
Tamanho:
1.14 MB
Formato:
Adobe Portable Document Format
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
4.03 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

ISEP – DEM – Artigos