Modelling fatigue in manual and robot-assisted work for operator 5.0

Occupational Applications: Fatigue, and many other human performance factors, impact worker wellbeing, and thus production quality and efficiency. Adopting the Industry 5.0 perspective, we propose that integrating human performance models into wider industrial system models can improve modeling accuracy and lead to superior outcomes. Integrating our Worker Fatigue Model as part of their industrial system architect model allowed Airbus, a leading aircraft manufacturer, to more accurately predict system performance as a function of the workforce makeup, which could be a combination of human workers and robots, or a combination of highly experienced and less experienced workers. Our approach demonstrates the importance and value of including human performance models in trade studies for introducing robots on the shop floor, and can be used to include various aspects of human performance in industrial system models to address specific task requirements or different levels of automation. Technical Abstract: Rationale: The advent of Industry 5.0 places a heightened focus on enhancing worker wellbeing during the digital transformation of factories. System models that ignore human workers yield suboptimal results in product design and system improvement.Purpose: In the aircraft industry, worker workload is of primary concern as most tasks are performed manually, leading to general fatigue and musculoskeletal disorders. Robot assistance could improve quality, efficiency and relieve workers from fatigue. To demonstrate the feasibility and value of integrating human performance models in system design at Airbus, a Worker Fatigue Model was developed, focusing on the effects of (1) automation (manual vs semi-automated), and (2) workforce makeup (various ratios of high-skilled to low-skilled workers). Our ultimate goal was to inform the development of effective policies and strategies for human-technology integration in Industry 5.0.Methods: We developed the Worker Fatigue Model by adapting existing fatigue models for workers in industrial environments and by considering worker characteristics, tasks, and the presence or absence of robot-assistance. Two different scenarios were simulated (fully manual and semi-automated), with input variables such as worker skill, age, and motivation, and output variables including overall fatigue and error probabilities were evaluated. The Worker Fatigue Model was integrated into the Airbus system model to conduct trade studies based on workforce characteristics.Results: Our findings revealed that the composition of the workforce (i.e., various ratios of high-skilled to low-skilled workers), alongside specific manufacturing technologies, significantly reduced worker fatigue, especially with higher ratios of high-skilled workers, and improved overall industrial system performance.Conclusions: Although applying our Worker Fatigue Model effectively demonstrated the feasibility and value of integrating human factors into early industrial system design, it remains a work in progress. Future work will aim to accurately represent the workload of human workers, including operational costs, when implementing robot assistance

The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration

This paper presents the architectural design and implementation of DIGICOR — a collaborative Industry 4.0 (I4.0) platform aimed at enabling SMEs to dynamically form supply-chain collaborations so as to pool production capacities and capabilities and jointly address complex supply chain requests. The DIGICOR architecture builds on the event-driven service-oriented architecture (EDSOA) model to support the collaboration between SMEs, dynamic modelling of their systems and services, and their integration in the supply chains of large OEMs, enforcing digital platform governance rules for knowledge protection and security. In contrast to the extant platforms assessed through our systematic review, the proposed architecture supports the entire lifecycle of I4.0 collaborations, from creation of viable teams to deployment and operation. The architecture provides an open and extensible solution for (i) creating a marketplace for the collaboration partners, (ii) providing services for planning and controlling the collaborative production, logistics, and risk management, while supporting APIs for third parties to provide complementary services such as advanced analytics, simulation, and optimization; and (iii) seamless connectivity to automation solutions, smart objects and real-time data sources. We report on the design of the architecture and its innovative artefacts such as the component model description and the semantic model constructs created for meaningful event exchanges between architectural end-points. We also describe a running use case demonstrating implementation scenarios

The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration

This paper presents the architectural design and implementation of DIGICOR — a collaborative Industry 4.0 (I4.0) platform aimed at enabling SMEs to dynamically form supply-chain collaborations so as to pool production capacities and capabilities and jointly address complex supply chain requests. The DIGICOR architecture builds on the event-driven service-oriented architecture (EDSOA) model to support the collaboration between SMEs, dynamic modelling of their systems and services, and their integration in the supply chains of large OEMs, enforcing digital platform governance rules for knowledge protection and security. In contrast to the extant platforms assessed through our systematic review, the proposed architecture supports the entire lifecycle of I4.0 collaborations, from creation of viable teams to deployment and operation. The architecture provides an open and extensible solution for (i) creating a marketplace for the collaboration partners, (ii) providing services for planning and controlling the collaborative production, logistics, and risk management, while supporting APIs for third parties to provide complementary services such as advanced analytics, simulation, and optimization; and (iii) seamless connectivity to automation solutions, smart objects and real-time data sources. We report on the design of the architecture and its innovative artefacts such as the component model description and the semantic model constructs created for meaningful event exchanges between architectural end-points. We also describe a running use case demonstrating implementation scenarios

Human Model For Industrial System And Product Design In Industry 5.0: A Case Study

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Human Model For Industrial System And Product Design In Industry 5.0: A Case Study

International audienceHuman performance models can be included in industrial system models to improve the design of the industrialsystem, manufacturing processes, and product design. In our use case, a critical process in the production of a newairplane was being considered for automation. This process requires the highest quality assurance and is normallyperformed manually. Robot assistance could improve quality and efficiency. A human performance model focused onworker fatigue was developed, taking into account characteristics of the workers, robots, and tasks. Two differentautomation scenarios (fully manual, semi-automated), with different worker characteristics such as skill, age,motivation, etc. were studied. Using historical production line data in the fully manual scenario, and simulated datafor the semi-automated scenario, global fatigue scores and graphical visualization were generated by the model foreach scenario, allowing the system architects to understand the effects of the future production system on workers,including errors, time lost, costs and overall resilience of the system