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NAERINGSPH-Nærings-phd

Heterogeneous environment of digital twins for industrial applications

Alternative title: Heterogent miljø av digitale tvillinger for industrielle applikasjoner

Awarded: NOK 1.8 mill.

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Project Number:

337501

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Project Period:

2022 - 2025

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Over the last few decades, there have been rapid advancements within automation and digitalization due to the developments made within the fields of internet of things (IoT), computing power and software developments. These, together with emerging technologies like big data, cloud computing, artificial intelligence (AI) and digital twins (DT) lays the ground for the transition to industry 4.0. This PhD project aims at investigating how digital twins combined with big data and AI can optimize cost, reduce carbon footprint and increase reliability and safety for manufacturing. The project is divided into three main topics: 1. Safe and efficient human-machine collaboration 2. Predictive maintenance 3. Smart production planning In the first topic, the research fellow will use sensors (2D- or 3D-cameras, lidars or similar) to detect humans in hazardous environments. Further on, the motion of the detected humans will be predicted using machine learning. This enables for safe control of machines, which is the next step. By defining different safety levels of areas on the shop floor in a digital twin, hazardous machines can be slowed down and stopped when a human is approaching them. The motivation for this topic is increased safety and a higher degree of space efficiency since it enables the possibility of removing the cages that surround hazardous machinery today. On the second topic, the research fellow will look at how big data can be used to predict time of maintenance to avoid machine failures as well as creating a schedule for maintainers. Big data will come from sensors measuring machine parameters, i.e. oil temperature or bearing vibrations, to name a few. Lastly, methods for smart production planning will be proposed by developing algorithms that optimize the production planning and the path of production from raw material to finished product. In this way, lead time, idle time, energy consumption and waste may be reduced.

Over the last few decades, there have been rapid advancements within automation and digitalization due to the advancements made within the fields of internet of things (IoT) and computing power. These, together with emerging technologies like big data, cloud computing, artificial intelligence (AI) and digital twins (DT), makes smart manufacturing and the transition to industry 4.0 possible. The transitions demand high competences within the field of mechatronics combining information and communications technology (ICT) with technical understanding of manufacturing processes. Many small and medium-sized businesses (SMBs) are struggling to keep up with the pace of these rapidly evolving technologies. Tvillingfabrikken is positioned in the market of developing DTs for industrial SMBs and intends to be a catalyst for smart manufacturing systems towards industry 4.0. DTs are used to some extent today, mainly for visualization purposes and product development. However, the full potential is far from being fully exploited. This project aims at combining DTs with big data and AI for smart production planning, predictive maintenance as well as virtual commissioning and testing of new automated systems.

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NAERINGSPH-Nærings-phd