A CASE STUDY OF DIGITAL TWIN FOR A MANUFACTURING PROCESS INVOLVING HUMAN INTERACTIONS
Hasan Latif, Guodong Shao, Binil Starly
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.
, Shao, G.
and Starly, B.
A CASE STUDY OF DIGITAL TWIN FOR A MANUFACTURING PROCESS INVOLVING HUMAN INTERACTIONS, Proceedings of 2020 Winter Simulation Conference, Orlando, FL, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930232
(Accessed December 1, 2023)