Integrating smart glasses and smart gloves in hybrid assembly/disassembly systems: an STPA-driven semi-automated risk management tool

With the rise of Industry 5.0, wearables have become increasingly common in manufacturing, making effective risk management more critical than ever. Despite this trend, there remains a significant gap in research regarding the risks associated with the simultaneous use of multiple wearables, particularly in complex hybrid systems involving human operators. This study addresses this gap by using an improved Systems-Theoretic Process Analysis combined with Particle Swarm Optimization (STPA-PSO) methodology. Moreover, it introduces a circular, semi-automated methodology (incorporating mitigation measures) that can systematically identify, analyze, quantify, and mitigate risks, including those arising from human error, in the integration of multiple wearables. Three case studies, two assembly lines and one disassembly line, were tested to check the effectiveness of this method. The findings indicate that increased interactions among system components can lead to elevated risk levels. It demonstrates that highlighting the hazardous areas, calibration regulations, and training of workers are high-risk control action scenarios that need to be reduced. This methodology can provide a safer and more efficient integration of wearable technologies in human-centered manufacturing environments.