The optimization and ergonomics approaches for the manufacturing systems with human robot collaboration

Human-Robot Collaboration empower manufacturing workstations by enabling ergonomic and safe working conditions for humans. To design successful human-robot collaboration, operational and ergonomics aspects must be considered. In this study, a new methodology is developed to identify factors that can impact physical workload of workers' performance in a human-robot collaboration environment. The proposed approach contributes to the simulation-based optimization literature by integrating Digital Human Modeling and Factorial Experiments tools. In this study, a workstation design including cobots is simulated in JACK software. A set of controllable design parameters (input factors) that may influence the physical ergonomic risks are defined as number of cobots, task complexity, human's anthropometric characteristics (height and weight) and product features (weight and dimensions). Each factor has two levels as high and low. Fractional and full factorial designs were applied for elimination purposes and then to examine all interactions at high resolution, and 64 different experiments were modelled for different configurations of factor levels. Multi-objective optimization technique applied for five different performance measures that are characterized as cumulative compression, cumulative low back moment exposure on L4/L5, energy expenditure and cycle time. Main and interaction effects of input factors on performance measures are discussed and profit/cost analysis included. Lastly, a simplified model focusing on the highest impacts and risks is proposed. Factor levels that optimizes the cumulative low back moment and cycle time simultaneously was found when the weight is low, the part weight is low and the number of cobots is two. The proposed methodology shows the applicability and advantages of combining digital human modelling tools, and statistical design approaches.