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Data Requirements for a Digital Twin of a Robot Workcell

Published

Author(s)

Deogratias Kibira, Guodong Shao

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 typical 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 lifecycle.
Proceedings Title
Proceedings of the 2023 Winter Simulation Conference
Conference Dates
December 10-13, 2023
Conference Location
San Antonio, TX, US
Conference Title
Winter Simulation Conference

Keywords

Digital twin types, Data categories, Robot workcell, Simulation, MTConnect, Standards

Citation

Kibira, D. and Shao, G. (2024), Data Requirements for a Digital Twin of a Robot Workcell, Proceedings of the 2023 Winter Simulation Conference, San Antonio, TX, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936902 (Accessed April 15, 2024)
Created February 1, 2024, Updated February 26, 2024