The hybrid analysis as a disseminator in the field of motion economics studies through machine learning methods and rule-based knowledge

Abstract

Manufacturing companies are increasingly confronted with the challenges of market globalisation, a shortening of product life cycles and a growing diversity of variants. New and flexible approaches to optimizing production processes and their planning ability are therefore needed to secure competitiveness in a sustainable way. Manual assembly in particular is a cost factor in the manufacturing industry and takes up a high proportion of the total production time. In addition to the efficient design of assembly processes, the ergonomic assessment and optimisation of work systems to avoid health hazards is also becoming increasingly important, also in consideration of demographic change. Currently, high personnel costs for the analysis of the workplace as well as special technical requirements for the employees in industrial engineering are identified as problematic. Especially for small and medium-sized companies with limited capacities in planning and existing competence levels of the employees, this aspect represents a hurdle that should not be underestimated. The following paper discusses the hypothesis that a combined approach of machine learning and rule-based knowledge as a hybrid analysis is suitable for transferring motion data captured by motion capturing into rule-conforming analyses in a semi-automated way. For this purpose, the new process building block system MTM-Human Work Design is used, which documents the required influencing factors chronologically and makes them variably evaluable in order to create time measurements and ergonomic execution analyses.