Modeling 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.

Keywords:

• Industry 5.0
• production and manufacturing
• simulation model
• worker fatigue
• robot assistance

Acknowledgments

We thank the production line workers and managers at Airbus Hamburg for their expertise, the Region Bretagne, FEDER and the University of South Australia for funding the Chair for Industry of the Future (Prof. Cao), the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the AI@IMT program of the ANR (French National Research Agency) for supporting Arnaud Allemang--Trivalle.

Conflict of Interest

The authors declare no conflict of interest. Contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of Airbus.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was performed under Airbus Contract SP2104350 DISM.