Modeling Fatigue in Manual and Robot-Assisted Work for Operator 5.0

Abstract

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.