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PhD position – Improving stability predictions in milling through machine learning in Zürich

Studierende finden an der ETH Zürich ein Umfeld, das eigenständiges Denken fördert, Forschende ein Klima, das zu Spitzenleistungen inspiriert.

Jobbeschreibung

100%, Zurich, temporary

The Institute of Machine Tools and Manufacturing (IWF) performs international leading research on machine tools and in the field of production engineering. For a new research project with close industrial contact we are looking for a new PhD candidate.

Project background

Process instability, resulting in chatter vibrations, remains the main limiting factor for modern CNC milling processes. These vibrations can lead to low surface quality, tool breakage and, in extreme cases, even to machine failure. Typically, unstable process conditions are avoided by creating stability lobe diagrams, which indicate stable cutting depths as a function of the spindle speed. However, existing models provide inaccurate results, making their usage unattractive for real industrial application cases. One alternative solution is to build experience-based stability lobe diagrams from measured stable and unstable cutting conditions. While it is straight-forward to autonomously and continuously monitor the milling process, making stability predictions from experimental observations requires an enormous amount of data, which is not practicable for industrial use. It is hence targeted to develop a hybrid approach, which combines the existing knowledge about the milling process and the capabilities of machine learning approaches. With the continuously growing database, it is expected that the prediction accuracy improves significantly over time.

Job description

The PhD student participating in this research project will work at the interface of mechanical engineering and machine learning to improve prediction quality of stability models. The work will be performed in close collaboration with leading Swiss industry partners and involves a mix of programming, modelling and experimental validation. The candidate can expect a full-time position in a highly motivated, young research group that offers an excellent research infrastructure. The work place is located in the heart of Zurich.

Your profile

We are looking for a candidate with a Master’s degree (or close to completion) in Mechanical Engineering or a similar field from a recognized university with an excellent GPA, strong analytical skills and some experience in machine learning. A background in machine tool vibrations is beneficial. Good programming skills in MATLAB or Python are required. Furthermore, proficient oral and written English skills are expected.

ETH Zurich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
Interested?

We look forward to receiving your online application including motivation letter, a full CV and transcripts of all degrees obtained (in English). Please note that we only accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Please do not hesitate to contact Mr. Martin Postel at postel@iwf.mavt.ethz.ch for any inquiries about the position (no applications).

Veröffentlicht am

30-10-2020

Extra Informationen

Status
Inaktiv
Standort
Zürich
Jobart
Werkstudentenstelle
Tätigkeitsbereich
Technik / Elektronik
Führerschein erforderlich?
Nein
Auto erforderlich?
Nein
Motivationsschreiben erforderlich?
Nein

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