A fuzzy based model proposal on risk analysis for human-robot interactive systems

Abstract

The role and job descriptions of the new generation of industrial robots that will operate in smart factories are being shaped by the industry 4.0 (I4.0) process, which has evolved with digital transformation and advanced production procedures. Human-robot interaction is a new industry trend and a key component of the I4.0 strategy. The main objective of this new solution is to improve the safety, ergonomics, productivity, and quality of the process. This solution aims to bridge the gap between manual production and fully automated production. In this way, the employee integrates the advantage of both humans and robots by sharing the workspace with the robot in non-ergonomic, repetitive, uncomfortable, and dangerous operations. This also means that the inclusion of robots in manufacturing processes does not devalue the human component; on the contrary, it shows that the increase in productivity is due to human-robot cooperation. As the level of human-robot cooperation increases, production capacity must be waived as a result of the slowdown of robots by nature, and risk assessment becomes more important according to certain standards. It is also clear that risk analysis of human and robot interaction systems contains a mixture of quantitative and qualitative data based on human evaluations and hesitancy and process uncertainties. In general, risk assessment approaches rely on the expertise and experience of specialists. So, the fuzzy set theory (FST) is more suitable to evaluate the risk assessment of this system. This study aims to contribute to improving human-robot collaboration and safety in an industrial setting for risk assessment based on FST. Additionally, the z-number, which is a fuzzy number of pairs is integrated into the proposed methodology to reflect the uncertainties of the risk assessment stage. Within the scope of the study, a new fuzzy-based risk assessment methodology is proposed to provide a safe workplace where humans and robots collaborate on a typical task. The proposed methodology consists of DELPHI, DEMATEL, ANP, and VIKOR which are multi-criteria decisions making (MCDM) methods based on the z-numbers that can take into account the uncertainty of the data and the hesitancies of the experts.