Towards Manufacturing Digital Twins: A Case Study in the Injection Moulding Industry

Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandling

Abstract

The Industry 4.0 concept aims to revolutionise the way manufacturing companies operate. The integration of digital technologies enables the collection of data throughout the manufacturing lifecycle and the automation of manufacturing activities. However, the efficient use of digitalisation and connectivity in manufacturing remains one of the biggest challenges of the manufacturing industry for the foreseeable future. Digital Twins have emerged as a promising tool for using the increasing amounts of data and connectivity by creating a virtual replica of physical manufacturing processes. The claim is that Digital Twins can be used to simulate manufacturing processes, identify inefficiencies, and optimise production. By using Digital Twins, manufacturerscanimproveproductivity, reducecosts, andboostcompetitivenessbyimprovingoverall operational efficiency. However, to develop and use Digital Twins, manufacturing companies need a clear understanding of the requirements and opportunities that a Digital Twin can offer. They should also have the right tools and technologies in place to support the creation and management of Digital Twins. The research conducted throughout this Ph.D. project focusses on bridging the gap between the academic state-of-the-art research and the industrial application of Digital Twins. In this thesis, we studied and proposed Proof of Concept Digital Twins in close collaboration with our industrial partner, which is active in the injection moulding industry. These Proof of Concepts explore frameworks, data utilisation, and modelling approaches towards the development of manufacturing Digital Twins. We focus on two phases of the mould lifecycle which are mould manufacturing and operation. The thesis is a collection of five scientific articles and two unpublished research studies. One of the main contributions of this thesis is related to scoping research providing fundamental insights on Digital Twins in manufacturing. Within the selected focus areas, we further introduced methods for the qualification of geometric accuracy of mould inserts in manufacturing using data- and physics-based approaches, and we explored data-driven methods for the identification of mould wear and performance in operation. In summary, this thesis provides insights into the field of manufacturing Digital Twins, specifically for the injection tooling and moulding industry. We presented Proof of Concept approaches for the development of Digital Twins. The thesis lays a solid foundation for further research on the challenges and opportunities to enable the industrial adoption of Digital Twins.

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