The use of conventional manufacturing methods is mainly limited by the size of the production run and the geometrical complexity of the component, and as a result we are occasionally forced to use processes and tools that increase the final cost of the element being produced. Additive manufacturing techniques provide major competitive advantages due to the fact that they adapt to the geometrical complexity and customised design of the part to be manufactured. The following may also be achieved according to field of application: lighter weight products, multimaterial products, ergonomic products, efficient short production runs, fewer assembly errors and, therefore, lower associated costs, lower tool investment costs, a combination of different manufacturing processes, an optimised use of materials, and a more sustainable manufacturing process. Additive manufacturing is seen as being one of the major revolutionary industrial processes of the next few years. Additive manufacturing has several alternatives ranging from simple RepRap machines to complex fused metal deposition systems. This paper will expand upon the structural design of the machines, their history, classification, the alternatives existing today, materials used and their characteristics, the technology limitations, and also the prospects that are opening up for different technologies both in the professional field of innovation and the academic field of research. It is important to say that the choice of technology is directly dependent on the particular application being planned: first the application and then the technology.

Would it be possible for an additively manufactured impeller of fused deposition modeling (FDM) technology to have a functional behavior and a similar performance to that of the original impeller of a close-coupled centrifugal pump? In this research paper, different tests are conducted to answer this question and to evaluate the manufacturing process of FDM functional parts. Three performance experiments with the same centrifugal pump, using an open test rig, are carried out and compared, first using the original impeller provided by the manufacturer of the pump, second using an FDM replication of the original one without post-treatment, and third using a chemically dimethyl ketone post-treated FDM replication. The results obtained in the tests demonstrate the functional behavior of both additive FDM impellers in comparison with the one fabricated by means of conventional technology (subtractive manufacturing). Additionally, analogous head-flow curves (and also with an improved performance to the one of the original impeller) are obtained. This research paper introduces significant information concerning a low-cost and low-time manufacturing process of additive functional parts. Moreover, new results are presented regarding the performance of chemically post-treated FDM parts working in functional applications. FDM impellers of high complexity and quality, which meet performance criteria, can be achieved.

This paper proposes an expansion of the Lean 5S methodology, which includes the concept of Safety–Security as 6S. Implementation was done by a standardized process tested in a pilot area that is part of the Integrated Industrial Manufacturing System Laboratory at the Higher Technical School of Engineering (ICAI). The additional 6S phase (Safety-Security) thoroughly reviews all areas of an industrial plant by analyzing the risks at each workstation, which let employees be fitted out with protection resources depending on each of their personal characteristics and to guarantee the safety of the workstation by strictly complying with occupational safety and health and machinery use standards, which must hold a CE certificate of compliance. The main objective was to increase the scope of 5S methodology to respond to the occupational safety and health needs for machines required in optimizing production processes. It is important to remember that companies must guarantee that their employees use personal protection equipment (PPE) at their work posts or stations that protect them properly from risks to their health and safety and that cannot be prevented or sufficiently limited by using collective means of protection or by adopting work organization measures. The industrial resources employed in the pilot area chosen for 6S implementation were a sheet metal deformation and cutting line comprised of a punch press, feeder, and winder, as well as a conventional machine tool with minimum safety specifications and without CE certification. Until now, there had been no plans to implement a 6S tool in this laboratory. However, given that the existing safety risk to operators is high, the implementation has led to obtaining optimal results, which justify the success of the 6S methodology proposal. In short, it lets us advance towards the zero accident target.

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence.

This study investigates how AI-driven innovations are reshaping manufacturing value chains through the transition from Industry 4.0 to Industry 6.0, particularly in resource-intensive sectors such as ceramics. Addressing a gap in the literature, the research situates the evolution of manufacturing within the broader context of digital transformation, sustainability, and regulatory demands. A mixed-methods approach was employed, combining semi-structured interviews with key industry stakeholders and an extensive review of secondary data, to develop an Industry 6.0 model tailored to the ceramics industry. The findings demonstrate that artificial intelligence, digital twins, and cognitive automation significantly enhance predictive maintenance, real-time supply chain optimization, and regulatory compliance, notably with the Corporate Sustainability Reporting Directive (CSRD). These technological advancements also facilitate circular economy practices and cognitive logistics, thereby fostering greater transparency and sustainability in B2B manufacturing networks. The study concludes that integrating AI-driven automation and cognitive logistics into digital ecosystems and supply chain management serves as a strategic enabler of operational resilience, regulatory alignment, and long-term competitiveness. While the industry-specific focus may limit generalizability, the study underscores the need for further research in diverse manufacturing sectors and longitudinal analyses to fully assess the long-term impact of AI-enabled Industry 6.0 frameworks.