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- From the directive to the machinery regulation: implications, challenges and impacts of digitalisation, ai and safety in European industryPublication . Pinho, Gonçalo; Pinto, Margarida Sofia de Sousa Pinto; Almeida Santos, Adriano Manuel; Pereira, Filipe; da Silva, A. Ferreira; Silva, Paulo; Felgueiras, CarlosThe current industrial paradigm, commonly referred to as Industry 4.0, is characterized by the interconnection of cyber-physical systems (CPS), artificial intelligence and connectivity on the shop floor. This technological evolution brings benefits in terms of efficiency and flexibility, but at the same time introduces security challenges that existing regulatory frameworks do not anticipate. The recent publication of Regulation from Europea Union (EU) 2023/1230, which replaces the current Directive 2006/42/EC, represents the legislative response to these new challenges by imposing new requirements in the areas of cybersecurity, safety-critical software and machine learning systems. The main aim of this paper is to carry out a comparative analysis between Directive 2006/42/EC and the new EU Regulation 2023/1230 given the recent shifts in the industrial paradigm, particularly due to the emergence of Industry 4.0 and 5.0, which are manifesting themselves in the areas of industrial automation, cyber-physical systems, digitalization and artificial intelligence, technical challenges have arisen that are reflected in the gaps in the current Machinery Directive (2006/427/EC). This article aims to analyze emerging technological challenges, with a particular focus on the risks associated with collaborative robotics, cybersecurity and machine learning systems, by systematizing the necessary knowledge and highlighting the gaps in the current directive, thereby demonstrating how the new regulation aims to address these new security challenges.
- A review of applied artificial intelligence in manufacturing: Emergent AI models in cyber–physical systems for manufacturingPublication . Rocha Varela, Maria Leonilde; Manupati, Vijaya Kumar; Pinheiro, Pedro; Putnik, Goran; Ferreira, Luís; Alves, Cátia; Ávila, Paulo; Castro, HelioThe 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.