Turbulent times await SMEs and major corporates in the manufacturing industry. Not only do they face the task of making maintenance more efficient, but they will also have to make their production processes more energy efficient with a view to the energy transition. Beyond that, they will have to maximise waste and resource reuse in order to join the circular transition.

Deploying Digital Twins

Digital Twin technology enables targeted improvement of processes, products and systems through data analysis and simulation, from testing prototypes to predicting and scheduling maintenance. These insights can help save companies a lot of time, money and energy, which is why the Industrial Digital Twins research group is investigating how they can best use this novel technology.

A Digital Twin is a digital representation or virtual copy of any object, system or process in the 'real world', ranging from a cooling system, to a laser cutting plant and even a business park. Digital Twins are connected to their physical counterparts in real time and continuously communicate with sensors that mimic their behaviour. Algorithms turn the incoming data into actionable insights and actions, effectively serving as the brains of the Digital Twin. A key feature of Digital Twin technology is the feedback loop from the virtual environment back to the physical world, which is used to control machines or manage people.

Three lines of research

The Industrial Digital Twins research group focuses on three lines of research, which together form a logical whole. Companies can join the line of research that best suits their capabilities and the challenges they face.

1. From physical to digital: developing physical test rigs and sensor systems for digital simulations within companies to make up for the lack of high-quality, real-time sensor data in machines and machine components. A good example of work in this line is the simulation of failure mechanisms, e.g. how a cooling system fails or a gearbox jams.
2. Virtual modelling and simulation: developing algorithms and generalising solutions to specific physical Twins, e.g. in the context of predictive maintenance and energy optimisation.
3. Feedback optimisation: the interaction between machines, and that between humans and machines, as a basis for informed decision-making. After all, model output cannot improve human and machine decisions until the effects on the company have been calculated.

Jurjen Helmus, professor of Industrial Digital Twins

Jurjen Helmus is a professor of Industrial Digital Twins at Amsterdam University of Applied Sciences. He received his PhD from the University of Amsterdam on simulation models for electric vehicle charging, having first studied Technical Business Administration (Innovation Management) and Industrial Engineering (Chemical Engineering) at the University of Groningen.

Connecting education and research

The research group involves various degree programmes in its research. Engineering students, for example, work on research into the physical component of Digital Twins, while students taking the Data Science minor or master's students in Applied AI busy themselves with the digital component. The research group also works with other AUAS degree programmes to develop course materials, including Mechanical Engineering, Technical Business Administration and Applied Mathematics.

Final-year university or university of applied sciences students from various fields - such as Aviation, Engineering Physics and Electrical Engineering - can also do a graduation project with the research group. In addition to this, there is an ongoing NRO Senior Fellows project aimed at developing Digital Twins for teaching at the Faculty of Engineering.

Partners of the Industrial Digital Twins research group

The L.INT Industrial Digital Twins research group is a partnership between AUAS, the Centre for Mathematics and Computer Science (CWI) and several industrial parties. The research group contributes applied knowledge to ongoing and new CWI projects and collaborations, building on CWI's state-of-the-art fundamental knowledge and applying it in the context of a university of applied sciences. Within AUAS, the research group has close ties with the Aviation Engineering research group and the Centre of Expertise for Applied AI.