Motion Parameters Identification for the Authoring of Manual Tasks in Digital Human Simulations: An Approach Using Semantic Modelling

Abstract

The use of digital simulation tools for the planning and verification of manufacturing processes has been identified as a key enabler technology. Through these tools, the need for physical prototypes is reduced, thus enabling the early assessment of decisions, regarding the efficiency of processes. The same stands for manual assembly planning. However, in industrial current practices, the digital simulation tools are scarcely used since the times for the generation of human simulations are still high. Furthermore, the current tools do not support the generation of motions that correspond to real life worker behaviors. This paper presents a methodology for the recognition and reuse of motions and motion parameters during a manual assembly execution. The methodology is based on a motion recognition algorithm using low cost sensors. This algorithm employs a rule based approach in order to identify motions that are translated into semantic individuals. A semantic model is also presented, accompanied by the relevant semantic rules for the organization and reuse of recorded motion parameters, during the production planning and more specifically, during the Digital Human Simulation. The methodology is applied to an industrial case study around the assembly of a car differential.