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We forge the researchers and developers of the future. Our PhD students have high academic ambitions and deliver high-quality results for both the private and the public sectors. Our primary focus is on applied research, and we have strong collaboration with industry, because we listen to the core questions from industry regarding mechanics and production engineering, and we develop solutions.

On this page, you can meet some of our PhD student and read about their projects.

New research identifies the strengths and weaknesses of super material

Scientists from Aarhus University and the University of Cambridge are the first to measure and set guidelines for bolted joints using the up-coming replacement for Kevlar: the ultra-strong material with the catchy name ultra-high molecular weight polyethylen.

Imagine a velvety, soft material that is extremely light, but also strong enough to stop a bullet. This is close to a description of ultra-high molecular weight polyethylene (UHMWPE), a super-plastic material commercially known as Dyneema or Spectra, which is already taking over from the para-aramid fibrous material, Kevlar, in e.g. bullet-proof jackets.

There is also much need for the super material in many other applications than body armour, and therefore researchers have now set up guidelines and failure maps for use of the material in joints with steel bolts. The research team is being led by Simon Skovsgård, PhD and MSc in engineering at the Department of Engineering, Aarhus University, and Professor Norman Fleck at the University of Cambridge.


Joint Dynamics and Impedance Control of Collaborative Robots for Smart Manufacturing under Industrial 4.0


Human-robot collaboration plays an important role in a smart factory that is the result of industry 4.0. Therefore, collaborative robots have been developed and rapidly accepted to conduct the human-robot collaboration while reducing cost, simplifying programming, increasing flexibility, etc. This PhD thesis project aims to address the challenging problems on the dynamics and control of collaborative robots. The major research efforts are made towards the threefold investigation: 1) Joint dynamics modeling with the consideration of the comprehensive geometries of structure, and the impacts of internal interaction and external stimulate of harmonic drive on joint dynamics, 2) Impedance control strategy considering the inherent flexibility of robot joints and accurate joint dynamic models, and the advanced learning algorithms for determining the optimal inertia, damping and stiffness of impedance control, and 3) Human-Robot interaction with the collaborating-scene-graph construction of factories, and collision risk assessment and reduction for safe human-robot cooperation. In addition, the real-time simulation and optimization technology, Digital Twins, will be employed to establish all the model of a collaborative robot system in the virtual world and be validated through experiments. The research achievements of this PhD thesis project will provide significant insights and guidance to the design and control of collaborative robots with applications to smart factories.


Project title: Joint Dynamics and Impedance Control of Collaborative Robots for Smart Manufacturing under Industrial 4.0 

PhD student: Xingyu Yang

Contact: xingyu_yang@mpe.au.dk

Project period: Dec 2020 to Nov 2023

Main supervisor: Xuping Zhang

Robust optimization of flow meters using computational fluid dynamics with quantified uncertainty

Kamstrup’s flow meters are highly accurate instruments that rely on the propagation of ultrasounds to measure the flow rate of water moving through a pipe. The project’s objective is two-fold. 

On one hand, to develop a robust optimisation tool, which further increases the quality of the meters by reducing the pressure loss in the pipe (a higher flow can be reached), increase the signal-to-noise ratio of the ultrasound transmission (more precise and repetitive measurements) and reduce the effect of inlet flow disturbances on the measurement (the meter is accurate even when mounted downstream of a bent pipe). 

On the other hand, the implementation of an optimisation tool in the design process of new meters and thereby drastically reduce the number of prototypes and associated testing time. These objectives are achieved by developing a framework that combines computational fluid dynamics (CFD), optimisation techniques and uncertainty quantification to obtain reliable, optimised flow meters design at a fast pace. To achieve the rapid development required in industrial applications the project focuses on the use of advanced mathematical and theoretical techniques rather than on huge computational resources.


Project title: Robust optimization of flow meters using computational fluid dynamics with quantified uncertainty 

PhD student: Mario Javier Rincón

Contact: mjr@mpe.au.dk

Project period: Nov 2020 to Oct 2023

Main supervisor: Guoqiang Zhang

Dynamics and Control of Lightweight Industrial Mobile Robot Manipulators for Smart Manufacturing under Industry 4.0

This project intends to research the dynamics and control of lightweight industrial mobile robot manipulators under Industry 4.0. Because of light weight, the component flexibility of the robot needs to be taken into account in the dynamics and control. It is also more important to analyse its dynamic problem by considering the influence of the dynamic interactions based on a more precise mathematical model. So, a new control scheme based on the model will be developed to make the mobile robot manipulator more efficient so they work safely alongside human co-workers for smart manufacturing under Industry 4.0. The control scheme can be optimised by considering dynamic compensation.

Finally, to validate and optimise the proposed kinematic and dynamic model and control scheme, extensive experiments will be conducted on a lightweight mobile robot manipulator.


Project title: Dynamics and control of lightweight industrial mobile robot manipulators for smart manufacturing under Industry 4.0

PhD student: Zhengxue Zhou

Contact: zhouzx@mpe.au.dk

Project period: Sep 2019 to Aug 2022

Main supervisor: Xuping Zhang

Development of robotic brace integrated with 3D-ultrasound for AIS treatment

The biggest problem in the orthopaedic profession is the medical condition known as adolescent idiopathic scoliosis (AIS). In this condition, a person’s spinal axis has a three-dimensional deviation from normal, with no cure currently available. The aetiology of adolescent idiopathic scoliosis (AIS) is unknown, but the extensive research results show that the problem can be reduced by decreasing the imbalance of forces along the spine. This motivates us to develop a robotic and wearable exoskeleton (brace) to restore the balance of forces along the spine with the aim of making a major breakthrough in the complementary treatment of AIS patients, which affects 1-3% of the population.

Preliminary work has been done: mechanical design of one steward platform, controller design and control system for one steward platform. The project will extend the preliminary work, and the objectives include:

  • develop the designed exoskeleton to three Stewart platform,
  • integrate the ultrasound sensors to the robotic system and design a control system,
  • validate the proposed control scheme and the mechanism, experiments will be conducted on the designed exoskeleton.


Project title: Development of robotic brace integrated with 3D-ultrasound for AIS treatment

PhD student: Farhad Farhadiyadkuri

Contact: ffyadkuri65@mpe.au.dk

Project period: Aug 2019 to July 2022

Main supervisor: Xuping Zhang

Fundamental socio-techno-economic modelling of the energy system transformation and the derivation of long-term sustainable strategies for Denmark in a European context

The Danish government has announced plans to make the country fossil-fuel free by 2050. Likewise, the European neighbours have ambitious targets. This transformation is one of the biggest challenges of our society. Techno-economical modelling and optimisation approaches are able to deliver first guidance principles for such pathways, but more than this needs to be taken into account. The dynamics of investor decision making, the formation and design of new market rules, the influence of social acceptance and the impact of climate change are going to strongly influence the development of a future highly efficient energy technology portfolio. More fundamental knowledge of these interactions is required to supply policy and commercial decision makers with a quantitative description of how to implement the transformation of the energy system in the best possible, most cost-efficient and robust manner.

We will use our competencies in the modelling of networked renewable energy systems: the description of the Danish energy system will include the internal coupling between the energy sectors electricity, heating and transportation, and the external coupling to the energy systems of the neighbouring countries.

Optimal techno-economic systems will be derived, for instance by invoking an increase of the CO2 emission constraint, subsidising specific technologies, etc. The scenarios are transformed to pathways by modelling them as emerging from the current energy system. The transition dynamics of the individual nodes are analysed and compared. With the energy systems becoming more distributed - decentralised renewable capacity replaces centralised conventional generation capacity - the generation capacity does not only become more spatially distributed but also opens up the possibility for an increased number of smaller actors in the market. We will seek the right incentives and regulations which are necessary to guide a large amount of decentral investments to overall follow an optimal transformation of the energy system.


Project title: Fundamental socio-techno-economic modelling of the energy system transformation and the derivation of long-term sustainable strategies for Denmark in a European context

PhD student: Leon Joachim Schwenk-Nebbe

Contact: leonsn@mpe.au.dk

Project period: March 2019 to Feb 2022

Main supervisor: Martin Greiner

Co-supervisors: Gorm Bruun Andresen and Marianne Zandersen