Data Requirements for A Digital Twin of A Robot Workcell

Abstract

The applications of digital twins continue to grow with the volume and variety of data collected. These data support the modeling of function, behavior, and structure of a physical element. However, successfully building a digital twin requires data identification, data fusion, and data management. Thus, despite the increase in data availability, there are still challenges of data usage, especially data scoping and scaling to implement a digital twin for a specific purpose. The objective of this paper is to identify data requirements for various types of digital twins for a robot workcell. The identification includes data description, source, method of collection, and data formats. The digital twin types include descriptive digital twins, diagnostics and prognostics digital twins, prescriptive digital twins, and intelligent digital twins. The outcome of this data requirements identification can be used as a guide for developing and validating digital twins for a robot workcell through its lifecycle.