Concept of Digital Twins for Autonomous Manufacturing through Virtual Learning and Commissioning

Abstract

Autonomous manufacturing represents a paradigm shift in industrial operations, akin to autonomous driving. This paper explores the role of digital twins in enabling autonomous decision-making within discrete manufacturing environments operated by massive fleets of robotic agents. By integrating artificial intelligence (AI), particularly reinforcement learning, digital twins facilitate the management of complex automated material handling systems (AMHS), driving the transition towards software-defined factories (SDFs). In this paper, we demonstrate how digital twins support virtual learning, training the parameters for reinforcement learning, as well as virtual commissioning, optimizing system validation and testing through virtual and physical integrations.