An uncertainty-based evaluation approach for human-robot-cooperation within production systems

摘要

The rising global trend towards mass customization embodies a major challenge for future production systems in terms of reconciling productivity with flexibility. Moreover, external factors such as varying lifecycles and shifting boundary conditions due to the demographic change intensify the severity on determining a suitable level of automation and changeability. Within this context, human-robot-cooperation offers a promising solution in order to cope with future demands towards flexible production systems. While most assembly design methods focus on interrelations between functionality and performance, the dependency of a varying economical feasibility due to an increased changeability potential within human-robot-cooperation is not considered so far and furthermore cannot be represented using conventional methodologies. Hence, the consistent comparability between system alternatives with variable degrees of automation is aggravated, representing the primary problem. This work presents a methodology for an uncertainty-based economical evaluation of flexible human-robotcooperation workplaces in assembly systems that catches the intricate nature and interrelations of internal and external factors and hence, comprising varying flexibility demands. The comprehensive cost model design takes into account uncertainties and thus, incorporating a more robust monetary evaluation within the scope of turbulent environment. Consistent reference scenarios are generated using a scenario-driven approach for quantitative uncertainties comprising both descriptive as well as prescriptive methods within the definition stage of prospective influencing parameters. Based on simulations a comprehensive analysis and interpretation of inherent risks and chances can be derived and consolidated enabling the overall quantitative assessment of system alternatives. The application of the uncertainty-based evaluation model is exemplarily shown using an automotive case study. The potential for the methodology supporting the planning and design phase is demonstrated via the assessment of the internal flexibility provided by alternative systems and matched with probability-based scenario attributes in order to obtain an optimized operation point.