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A review of applied artificial intelligence in manufacturing: Emergent AI models in cyber–physical systems for manufacturing
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Orientador(es)
Resumo(s)
The 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.
Descrição
Palavras-chave
Cyber-physical systems Manufacturing systems Artificial intelligence Machine learning Emergent AI models Systematic literature review
Contexto Educativo
Citação
Varela, 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
Editora
MDPI