Semantic Enrichment of Spatio-Temporal Production Data to Determine Lead Times for Manufacturing Simulation

Abstract

Data from real-time indoor localization systems (RTILS) based on ultra-wideband (UWB) technology provide spatio-temporal information on the material flows of production orders on the shop floor. This paper investigates how historical position data can be used for the determination of lead times and respective time shares. We propose three different approaches for the enrichment of spatio-temporal trajectories with process information. Two of them are online algorithms for the automated posting of process times using either points or areas of interest. The third is an offline classification problem that minimizes the error that occurs during the assignment of measurements to processes when generating semantic trajectories. Furthermore, a sensor fusion concept is presented, which is necessary to split up the lead times of the operations in smaller time shares for simulation input modeling.