FASTory digital twin data

Wael M Mohammed; Jose L Martinez Lastra

doi:10.1016/j.dib.2021.106912

FASTory digital twin data

Data Brief. 2021 Feb 26:35:106912. doi: 10.1016/j.dib.2021.106912. eCollection 2021 Apr.

Authors

Wael M Mohammed¹, Jose L Martinez Lastra¹

Affiliation

¹ FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 600, Tampere FI-33014, Finland.

Abstract

The vast adoption of machine learning techniques in developing smart solutions increases the need of training and testing data. This data can be either collected from physical systems or created using simulation tools. In this regard, this paper presents a set of data collected using a digital twin known as the FASTory Simulator. The data contains more than 100 K events which are collected during a simulated assembly process. The FASTory simulator is a replica of a real assembly line with web-based industrial controllers. The data have been collected using specific-developed orchestrator. During the simulated process, the orchestrator was able to record all the events that occurred in the system. The provided data contains raw JavaScript Object Notation (JSON) formatted data and filtered Comma Separated Values (CSV) formatted data. This data can be exploited in machine learning for modelling the behaviour of the production systems or as testing data for optimization solution for the production system. Finally, this data has been utilized in a research for comparing different data analysis approaches including Knowledge-based systems and data-based systems.

Keywords: Assembly process; Data engineering; Digital twin; Discrete manufacturing process; Linked data.