Machine Learning Agents Augmented by Digital Twinning for Smart Production Scheduling
Kosmas Alexopoulos, Nikolaos Nikolakis, Emmanouil Bakopoulos, Vasilis Siatras, Panagiotis Mavrothalassitis
Abstract
Digital manufacturing tools aim to provide intelligent solutions that can support manufacturing industry to adapt to the volatile operational environment. The successful implementation of such tools highly depends on the capabilities of the digital frameworks or platforms they are deployed upon as well as the quality of their intelligence. The objective of this work is to develop and discuss a framework for training and deploying Machine Learning (ML) agents for production scheduling with the augmentation of Digital Twin (DT) technologies. Two types of ML production scheduling agents have been developed and integrated with the DT framework: a Deep Learning agent and a Deep Reinforcement Learning agent. In order to increase interoperability, Asset Administration Shell Industry4.0 standard has been utilized for the integration and deployment of the proposed DT framework into industrial practice. The proposed framework is tested and validated upon an industrial case study from the bicycles’ production industry.