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    A fuzzy based model proposal on risk analysis for human-robot interactive systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bozkus E.; Kaya I.; Yakut M.
    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. © 2022 IEEE.
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    A Two-Dimensional Fuzzy Risk Assessment Model for Occupational Health and Safety Evaluations
    (Institute of Electrical and Electronics Engineers Inc., 2022) Yakut M.; Kaya I.; Bozkus E.
    In working life, the danger in the environment and the risks arising from it are of great importance for the health and safety of the employees. In order for these pre-determined damaging factors to be evaluated correctly, an evaluation method suitable for that field of activity should be selected. Since real case risk assessment problems include many uncertainties, the fuzzy set theory (FST) that has a huge ability to model uncertainty has found a wide application area thanks to its easy and convenient solution approach to the solution of difficult and complex problems today. FST can be used to eliminate the uncertainties in classical risk analyzes in workplaces and to introduce new methods by applying different combinations. Combinations prepared using various methods make positive contributions to occupational health and safety (OHS) risk assessment processes. These combinations prepared using various methods. In this paper, one of fuzzy set extensions named Z-number has been integrated with the proposed methodology to improve ability that is modelling uncertainties. It ensures that the inadequacies in the existing risk assessment methods are eliminated. For this aim a new framework has been suggested. In the proposed model that we will propose in our study, the risks arising from the danger will be analyzed by using Fine Kinney Method that is one of the most widely used methods in OHS risk analysis. Grading of risks in the Fine Kinney method is obtained by multiplying the probability of occurrence of the risks, the frequency of exposure to the hazard and the numerical values of the effect it creates. Additionally, the proposed methodology consists of three MCDM methods such as DEMATEL, AHP or ANP and TOPSIS methodology. The DEMATEL method will be used to establish causality between hazards. The method AHP or ANP will be used to determine risk weights and the TOPSIS method will be used to determine hazards in order of priority. The proposed framework is also being constructed on Z-numbers and thus a new risk assessment methodology based on MCDM, and Fine-Kinney Methods is suggested. It will be advantageous to use Z-numbers to clarify the uncertainties. to ensure that risk assessments are more objective and to make its applicability even more possible. The proposed framework can be applied in a real case risk assessment problem to analyze its results. © 2022 IEEE.