摘要：AbstractEvaluation of manufacturing systems requires large amounts of accurate data from the factory floor. This data is then processed to calculate Key Performance Indicators (KPIs), evaluation metrics used within the manufacturing industry by engineers and managers in order to make data-driven decisions. Mechanisms to capture large scales of usable data, which is both reliable and scalable is, more often than not, scarce. In this paper, we provide an approach to capture data from robot actions, which can be applied to both legacy and current state-of-the-art manufacturing systems. By exploiting the robot code structure, robot actions are converted to event streams that are transformed into a higher usable abstraction of data. Applicability of this data is demonstrated, primarily, by visualizations. The described approach is developed in Sequence Planner - a tool for modeling and analyzing production systems - and is currently implemented at an automotive company as a pilot project to visualize and examine what goes on on the factory floor.
