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A Case Study of Digital Twin for a Manufacturing Process Involving Human Interactions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current algorithms, computations, and solutions that predict how humans will engage in smart manufacturing are insufficient for real-time activities. In this paper, a digital-twin implementation of a manual, manufacturing process is presented. This work (1) combines simulation with data from the physical world and (2) uses reinforcement learning to improve decision making on the shop floor. An adaptive simulation-based, digital twin is developed for a real manufacturing case. The digital twin demonstrates the improvement in predicting overall production output and solutions to existing problems.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 Winter Simulation Conference, WSC 2020
EditorsK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2659-2670
Number of pages12
ISBN (Electronic)9781728194998
DOIs
StatePublished - Dec 14 2020
Externally publishedYes
Event2020 Winter Simulation Conference, WSC 2020 - Orlando, United States
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings - Winter Simulation Conference
Volume2020-December

Conference

Conference2020 Winter Simulation Conference, WSC 2020
Country/TerritoryUnited States
CityOrlando
Period12/14/2012/18/20

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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