A framework for data-driven digital twins of smart manufacturing systems

TLDR

Digital twins in smart factories enhance productivity, cut costs and energy use, but rapid customer demand shifts and short product life cycles expose the inadequacy of traditional modeling and simulation approaches. The authors propose a generic data‑driven framework that automates the creation of simulation models for digital twins, aiming to eliminate the need for expert knowledge. The framework leverages machine learning and process mining techniques to continuously improve and validate the generated models. A case study demonstrates the feasibility of the framework.

Abstract

Adoption of digital twins in smart factories, that model real statuses of manufacturing systems through simulation with real time actualization, are manifested in the form of increased productivity, as well as reduction in costs and energy consumption. The sharp increase in changing customer demands has resulted in factories transitioning rapidly and yielding shorter product life cycles. Traditional modeling and simulation approaches are not suited to handle such scenarios. As a possible solution, we propose a generic data-driven framework for automated generation of simulation models as basis for digital twins for smart factories. The novelty of our proposed framework is in the data-driven approach that exploits advancements in machine learning and process mining techniques, as well as continuous model improvement and validation. The goal of the framework is to minimize and fully define, or even eliminate, the need for expert knowledge in the extraction of the corresponding simulation models. We illustrate our framework through a case study.