A reference architecture for cloud-edge meta-operating systems enabling cross-domain, data-intensive, ML-assisted applications: architectural overview and key concepts

Future data-intensive intelligent applications are required to traverse across the cloudto-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.For UPC authors: this research was funded by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant number PID2021-124463OB-100.Peer ReviewedPostprint (published version

Towards Artificial Intelligence in Production: A Competence Profile for Shop Floor Managers

Artificial intelligence (AI) technologies experience an ever-growing interest both in research and industry. Though they offer high potential for manufacturing, recent studies among practitioners reveal that there is a lack of knowledge for implementing AI in production environments and especially for leading an implementation successfully. Therefore, in this paper, a competence profile for shop floor managers has been developed. Shop floor managers are seen as suitable levers in companies for implementing AI technologies. This profile focuses on their practical requirements and encompasses relevant productionoriented use cases, social factors for engaging employees and deeper understanding of the models to interpret the results. The profile has been put into practice by means of learning content and has been tested by shop floor managers. The feedback is promising: About 78% of the testers stated that the content is helpful for them in understanding the benefits, challenges, tasks, and risks when implementing AI based projects. The results serve as a baseline for future development of learning materials with corresponding exercises to be taught in learning factories targeting the hands-on AI implementation

Assessing ergonomics on cobot for an optimized integrated solution in early phase of product and process design

The design goal for Human-Robot collaboration is combining the repeatability and productivity of automated systems with the flexibility of the operators [1]. One main interest is for cobots to take over complex and physically demanding assembly tasks, reducing the biomechanical workload on workers and increasing product quality. However, as reported by several authors [1-3], the introduction of cobots is not straightforward and should be thoroughly investigated and planned to avoid higher mental stress on workers and a decrease in efficiency. In this respect, Digital Human Modelling can support the integration of robots in design or evaluation of hybrid cells, anticipating process and interaction criticalities.One key aspect of hybrid cells is task allocation between worker and cobot. Recently, Authors in [4] proposed an optimization procedure that looks into productivity and ergonomics. However, the proposed approach is rather complex to apply and does not allow for evaluations of the what-if type, which are particularly useful in the development of new hybrid cells.The paper proposes a new methodology, which has the advantage of being simple and allowing for a visualization of shared operations through a simulator, and for a heuristic evaluation during the design phase of the hybrid workstation, and, finally, permitting a “what-if” analysis. The work is part of the research project D-HUMMER (Digital HUMan Model for ERgonomic workplace) funded by EIT Manufacturing (project number 22294).The simulation tools are the IPS IMMA and IPS Robotics that integrate in the same scenario the evaluation of the biomechanical load on different anthropometries of workers with the performance of the robot working cycle. The advantage of a full digital approach allows frontloading in Concept Phase the workstations’ layout and sequence, even considering different variants, before any production line is established.In the presented methodology, each working job scenario is represented by a state-machine (fig. 1), where each state defines a single atomic task (i.e. a single state) that may be fulfil by either the robot or the human operator, depending on the choice of the workplace designer. Each atomic task is characterized by several parameters (e.g. human strains, time of execution, etc.). In each of the simulator runs, a performance index I_k is computed:I_k = sum_(i=1, to n) alpha_i,k * t_i,k + beta_i,k * s_i,kwhere:k : is the index of each scenario, each represented by a state machine.i : is the index of each state in the state machine.t_i,k : is the time required for each atomic tasks_i,k : is the human strain required for each atomic taskalpha and beta are appropriate weights for each parameter.The paper presents the methodology through a real industrial use case of the Whirlpool microwave assembly. In the current workstation (fig.2) the operator assembles a heavy component (transformer), given by a robot that places it on a table. The worker has therefore the full weight of the component in hands. The optimized solution (fig.3) improves the robot usage, resetting the weight for the worker, and achieving better safety and ergonomics conditions.The methodology has been applied to an already-existing industrial solution. However, its application in early stage may mark a paradigm shift in the workstation design layout for manufacturing companies