Local Decision Making based on Distributed Digital Twin Framework

Abstract

Abstract In recent years, digitalization has taken an important role in the manufacturing industry. Digital twins (DT) are one of the key enabling technologies that are leading the digital transformation. Integrating DT with IoT and artificial intelligence enables the development of more accurate models to improve scheduling tasks, production performance indices, optimization and decision-making. This work proposes a distributed DT framework to improve decision making at local level in manufacturing processes. A decision-making module supported on an adaptive threshold procedure is designed and implemented. Finally, the proposed framework is evaluated on a pilot line, highlighting the behavior of the decision-making module for detecting possible faults, alerting the operator and notifying the manufacturing execution system to trigger actions of reconfiguration and scheduling.