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FASTory digital twin data.

Wael M Mohammed1, Jose L Martinez Lastra1

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

Data in Brief
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a dataset from the FASTory Simulator, a digital twin of an assembly line. The data aids machine learning for smart manufacturing and production system analysis.

Area of Science:

  • Industrial Engineering
  • Computer Science
  • Data Science

Background:

  • The increasing use of machine learning in smart solutions necessitates extensive training and testing data.
  • Data for these applications can be sourced from physical systems or generated via simulation tools.

Purpose of the Study:

  • To present a comprehensive dataset collected from a digital twin, the FASTory Simulator.
  • To provide data suitable for machine learning applications in production system modeling and optimization.
  • To facilitate research comparing data analysis approaches for industrial systems.

Main Methods:

  • Utilized the FASTory Simulator, a digital twin replicating a real assembly line with web-based controllers.
  • Collected over 100,000 events during a simulated assembly process using a custom-developed orchestrator.
Keywords:
Assembly processData engineeringDigital twinDiscrete manufacturing processLinked data

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  • Formatted the collected data into both raw JavaScript Object Notation (JSON) and filtered Comma Separated Values (CSV).

    • Main Results:

    • Generated a large-scale dataset (100K+ events) from a simulated industrial assembly line.
    • The dataset includes raw JSON and filtered CSV formats, offering flexibility for various analyses.
    • The data was successfully applied in research comparing knowledge-based and data-based analysis methods.

    Conclusions:

    • The FASTory Simulator provides a valuable, large-scale dataset for machine learning in smart manufacturing.
    • This data can enhance the development and testing of predictive models and optimization solutions for production systems.
    • The dataset supports comparative studies of different industrial data analysis techniques.