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 dig...Show MoreMetadata
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.
Published in: 2020 Winter Simulation Conference (WSC)
Date of Conference: 14-18 December 2020
Date Added to IEEE Xplore: 29 March 2021
ISBN Information: