The transition from Industry 4.0 to Industry 5.0 reflects a shift from technology-driven automation toward human-centred, resilient, and sustainable industrial systems. Although advanced digital technologies enable human–cyber–physical systems (HCPS), empirical evidence on their socio-technical implementation remains limited. This paper develops a Human-Centred Technology Framework (HCTF) to assess the operationalization of Industry 5.0 principles across four industrial pilot cases. Adopting a qualitative exploratory approach, 4 manufacturing described how human, organizational, and technical subsystems are aligned during technology deployment. The findings show that Industry 5.0 maturity depends less on technological sophistication and more on early socio-technical integration. Companies embedding human-centred considerations at the design stage achieved stronger coherence in workflow redesign, skills adaptation, and digital tool integration, whereas technology-led implementations required later organizational adjustments. The study contributes by empirically operationalizing Industry 5.0 through a structured socio-technical lens and offering managerial guidance for aligning digital transformation with human augmentation and organizational readiness

The emergence of Industry 6.0 represents the next phase of industrial transformation, driven primarily by advances in artificial intelligence, digital technologies, and data-driven innovation. While previous industrial paradigms such as Industry 4.0 and Industry 5.0 have focused respectively on digitalization and human-centric production systems, the conceptual foundations of Industry 6.0 remain fragmented in the current literature. This study aims to provide a structured overview of the emerging Industry 6.0 paradigm by identifying its key categories, concepts, and constructs. To address this objective, a systematic literature review was conducted using the Scopus database, covering publications from 2021 to 2026. The selected studies were analyzed using Grounded Theory Methodology, applying open, axial, and selective coding procedures to systematically categorize the literature. The analysis identifies five core categories that characterize Industry 6.0: Technology & Systems; Organizational & Human Capital Dimension; Strategic & Business Management; Ethics, Trust & Responsible Governance; and Societal, Environmental & Sustainability Impact. The findings highlight that Industry 6.0 extends previous industrial paradigms by integrating advanced digital technologies with human-centered, ethical, and sustainability-oriented perspectives. The study contributes to the literature by offering a structured conceptual framework that clarifies the multidimensional nature of Industry 6.0 and highlights key areas for future research and managerial practice.

Manufacturing is one of the key pillars of today’s society, representing an integral, vast component of the economy. In this field, process efficiency is of critical importance, time being one of the most important resources to optimize. Minimizing the time of operations results in a reduction of costs, through increasing the number of operations per unit of time and optimizing the use of equipment. This paper explores the optimization of the previous CPU based open-source, low-cost solution for 3D scanning, the process of capturing the features of the object and storing them in a digital format, ubiquitous in manufacturing. The optimization consisted in moving the matrix operations to the GPU and resulted in an approximately 30 times faster processing time. A linear dependency has been observed between the scanning speed and the image size.

Manufacturing as a Service (MaaS) offers significant advantages by enabling on-demand access to distributed manufacturing capabilities, improving resource utilization, and supporting scalable and flexible production through digital platforms. As such, it is emerging as a key paradigm within Industry 4.0, enabling distributed production through cyber-physical coordination mechanisms. Despite its increasing relevance, MaaS remains conceptually fragmented and lacks a clear theoretical positioning within the discipline of Collaborative Networks (CN). This paper proposes a structural framing of MaaS as a Cyber-Physical Service-Oriented Collaborative Network (CPSO-CN). Drawing upon CN theoretical foundations and documented MaaS architectural components, the study develops a conceptual mapping framework aligning core CN constructs, such as virtual organizations, service composition, governance structures, and evolutionary mechanisms, with MaaS ecosystem elements and agentic AI. The paper contributes by (i) theoretically positioning MaaS within CN theory, (ii) introducing the CPSO-CN framework, and (iii) identifying governance and innovation implications for digital industrial ecosystems. The proposed framework provides a structured basis for future empirical investigation of distributed manufacturing platforms, with illustrative applications demonstrated in the MaaSAI project.

This paper presents a digital twin–based modeling approach for predicting tool center point (TCP) trajectory deviations in a five-axis machine tool. A kinematic digital twin of the machine is developed and extended by incorporating thermal errors through a newly proposed thermal error integration strategy, enabling the prediction of thermally induced trajectory deviations during multi-axis motion. In parallel, TCP deviations are analytically derived using screw theory, which models trajectory errors primarily from geometric and kinematic error sources. The predicted TCP trajectories obtained from the digital twin and the screw-theory-based model are quantitatively compared with experimental measurements under representative machining conditions. The results demonstrate that both approaches provide accurate trajectory deviation predictions, with the digital twin effectively capturing thermal drift and time-dependent error behavior, while screw theory offers an interpretable geometric baseline. The strong agreement between simulations and experiments confirms the effectiveness of the proposed digital twin framework and highlights the complementary roles of digital twin–based thermal modeling and screw-theory-based geometric analysis for accurate five-axis machine tool trajectory prediction.