Energy-Aware Resources in Digital Twin: The Case of Injection Moulding Machines

Abstract

Many initiatives aim at describing the objectives and functionalities of the so-called digital twin of manufacturing systems. Considering the assets, the twin is meant to be able to both exhibit the actual and current states of the resources, and provide some estimates about the future behavior of these resources. To target the sustainability pillar of future industrial systems, the energy monitoring and management are critical issues. Consequently, the integration of multiphysics models helping to model the resources inside the twin is a major issue to deal with. This article intends to introduce a framework integrating the models inside the twin of the ARTI architecture, propose a methodology to implement the twin on a resource and illustrate these on a case study on injection molding machines.

MES-integrated digital twin frameworks

“…The single DT modules may be hosted by the single physical resources and may be used for various functionalities, mainly related to a local decision making (e.g. monitoring and forecasting of the behaviour of the physical twin [61] or may be used as a means to populate data repositories to be used on higher levels [70]). When integrating the various DT modules into a system architecture, this could be used for a global decision making and integrated into a higher-level control system, such as the MES [71,72].…”

“…The intelligence layer, and its role with respect to the DT model, are in line with the proposed reference architecture by Valckenaers [61] [62].…”

Utilising Web-based Digital Twin to Promote Assembly Line Sustainability

“…Digital twin is "an integrated multi-physics, multi-scale, and probabilistic simulation of a complex product and uses the best available physical models, sensor updates, etc., to mirror the life of its corresponding twin" [18]. Two types of DT can be exhibited [13]: DT of products and DT of the enterprise (including manufacturing systems). For the latter, depending on its model sophistication, connectivity to the data from the physical twin and availability of Artificial Intelligence (AI) The current work exhibits a digital twin with existing connectivity to the physical counterpart but without an advanced artificial intelligence capability for the time being but a statistical regression model (machine learning).…”

“…This could help to reduce the energy and manufacturing resources' consumption by 14.4%. A framework of an energy-aware asset DT is introduced in [13]. In this framework, each DT can have multiphysical models, and its function is to monitor and report the behaviour of the physical counterpart.…”

Digital Twins: Review and Challenges

“…Cardin [20] in his work states that: The concept of DT is starting to be used in smart manufacturing specifically in management frameworks. And it has become an essential component to take decisions, resources management, with the capacity to check the status, modify the behavior and realize predictions of the real object.…”