PhD-student in Machine Elements - Machine learning and computational Tribology
Luleå University of Technology Department of Engineering Sciences and Mathematics
Sweden

JOB DETAILS
Title PhD-student in Machine Elements – Machine learning and computational tribology
Employer Luleå University of Technology
Job location Universitetsområdet, Porsön, 971 87 Luleå
Published August 22, 2019
Application deadline September 15, 2019
Job types PhD
Fields Materials Engineering, Computer Engineering, Applied Mathematics, Materials Physics, Computational Sciences, Computational Mathematics, Computational Engineering, Machine Learning, Systems Engineering

 

 

JOB DESCRIPTION

Ref 2885-2019

The Division of Machine Elements (ME) at the Department of Engineering Sciences and Mathematics (TVM) is a research group that enthusiastically works for a more sustainable world with a diminishing climate and environmental impact. We work to develop products and processes that are energy-efficient, preferably fossil-free and renewable. We are a large and growing group of about fifteen doctoral students and as many supervisors. In our postgraduate education (PhD@ME), we strive for the doctoral student to develop into an expert on the subject but also develop as a person and become a skilled researcher. A professional who after the doctoral degree her/himself can initiate and lead research projects within the university world or in the industry. Being a part of the research group means working in an international and creative environment with good resources and an extensive international contact network. The research group is one of the leading tribology research groups in Europe and there are good opportunities for exchanges with other groups in Europe and other parts of the world.

The activities at ME include many aspects of tribology, eg. materials tribology, lubrication and lubricants, condition monitoring, biotribology, computational tribology and tribology of machine components. The research is both of a basic research nature and of an applied character, and is often done in close cooperation with the Swedish and international industry. The group is responsible for some of the theory and project courses that are given within LTU's engineering programs. In particular, courses are given in the Mechanical Engineering program and in the international master's program in tribology (Tribos).

We are now looking for a doctoral student who will work on developing a completely new model, based on machine learning methodology, which will be able to help product developers choose the right material, surface treatment, lubricants etc. for the product to be as durable and energy efficient as possible.

Subject description
Machine Elements includes analysis and optimisation of components and systems based on performance, durability, energy efficiency, reliability and sustainability. Particular emphasis is placed on the field of tribology.

Project description
In the development of products and manufacturing processes, advanced simulation within structural and fluid mechanics is often used to optimize the function. Wear and friction are important parameters that affect the function. The wear makes service life shorter and friction makes the power losses to increase and the efficiency to decrease. Unfortunately, there are not as good simulation tools to predict wear and friction as, for example, strength. This is partly due to the complexity of the processes that arise between two surfaces in contact with each other but also that there are no good materials data to use in such simulations.

In this project we will develop better and more general models and materials data for wear and friction. We will develop models that can be used to calculate if wear occurs and, if so, how serious the damage will be. We will also calculate the magnitude of friction. There are many different things that affect wear and friction, eg. surface roughness, load, surface material and lubricants. It is therefore a large number of variables and it is impossible to cover all possible load cases, which are combinations of the input parameters. We will therefore build up a database where all calculated load cases are stored and we will use machine learning to train a model that is getting better the more load cases we handle. Hopefully, we will be able to automate the calculations and create thousands of load cases that can then be used in machine learning to build an artificial neural network (ANN) that easily and quickly tells about wear and friction.

In the project, you will work with other doctoral students and researchers within neighbouring research projects. Cooperation will also take place with leading experts in machine learning. You also have the opportunity to conduct your research for a few months at another university or at a company.

Qualifications
Applicants must have an MSc degree in Engineering or Natural Sciences, for example, Mechanical Engineering, Engineering Physics, Engineering Mathematics, Materials Science or equivalent. Very good skills in oral and written communication in English is also a requirement.

The following selection criteria apply:

Knowledge relevant to the project in question,
Personal qualities relevant to research
Quality of Master's Thesis
Predicted that the qualifications are equal, the underrepresented gender (currently female) will be given priority in this recruitment.

Information
For further information, you are welcome to contact the supervisor group, Chair Professor Roland Larsson, +46-920-491325, roland.larsson@ltu.se and Professor Andreas Almqvist, +46-920-492407, andreas.almqvist@ltu.se

Union representatives, SACO – Christer Gardelli, +46 920 49 1809, christer.gardelli@ltu.se OFR – Lars Frisk, +46 920 49 1792, lars.frisk@ltu.se

Luleå University of Technology is actively working for equality and diversity that contributes to a creative study- and work environment. The University's core values are based on respect, openness, cooperation, trust and responsibility.

In case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence.

Application
We prefer that you apply for this position by clicking on the apply button below. The application should include a CV, personal letter and copies of verified diplomas from high school and universities. Your application, including diplomas, must be written in English or Swedish. Mark your application with the reference number below.

Reference number: 2885-2019

Last day for application: 15 September 2019


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