Digital Twin for Automated Industrial Optimization: Intelligent Machine Selection via Process Modelling

Abstract

Digital Twin (DT) is an emerging paradigm that enables a virtual model to effectively represent a physical process. In this paper, we present the adoption of the DT scheme by an offset printing company towards industrial optimization. The considered DT model is a virtual representation that serves as the digital copy of the physical printing process within an industrial unit. A virtual model for selecting the optimal machine line was developed to ensure cost-efficient printing. The machine line selection process was modeled as a decision process and then analyzed through simulations in a safe and cost-efficient digital environment, provided by the DT. Moreover, Machine Learning (ML) models were exploited to extract knowledge for the machine selection task, taking full advantage of the DT experiment. Based on real data and selection policies of a printing enterprise, the results revealed an improvement during the selection process, followed by a 5% cost reduction on the examined dataset.