PhD on wear prediction using machine learning

Katholieke Universiteit Leuven

Belgium

July 11, 2021

Description

PhD on wear prediction using machine learning

(ref. BAP-2021-395)

Last modification : Thursday, June 3, 2021

The M-Group (Mechatronics Group) research group of the KU Leuven conducts research on reliable interconnected mechatronic systems with applications in industry 4.0 and healthcare. The M-Group has several labs equipped with state- of-the-art infrastructure to build, test and research mechatronic systems and self-organising production systems. The research group is composed of a multidisciplinary team of professors and researchers skilled in hardware design, software engineering and mechanical engineering. The research is focused on reliable and sustainable performance of systems taking into account mechanical aspects, electrical energy, automation, electronics, signal processing and ICT. The research group consists of 5 professors and 20 research assistants or PhD students and is located at the brand new Bruges campus. Here, the research team can make use of state-of-the-art research equipment and infrastructure to design, test and validate mechatronic systems. Moreover, the available infrastructure enables the PhD students and research assistants to design and develop robust and reliable design techniques for hardware or software. The research group has been successful in obtaining prestigious funding such as H2020, Marie Slodowska-Curie Actions and national funding (FWO, imec.ICON, ...). The research group maintains a close collaboration with other research groups at KU Leuven within the departments of Computer Science (imec.DistriNet), Mechanical Engineering (LMSD, RAM) and Electrical Engineering (ESAT-WaveCore).

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Project

Cutting machines, such as lathes or milling machines, are very susceptible to wear and tear. A good prediction of this wear and tear makes it possible to replace the machine parts at the correct moment in time so that the production process is not compromised. However, this prediction is closely related to the data measured during the operational functioning of the machine. With the advent of machine learning, many opportunities are created to take data prediction a step further. In the WearAI project, we want to predict the wear of machine parts through machine learning. This project is in collaboration with VIVES university college.

Profile
  • You have a master's degree in electromechanics and an interest in artificial intelligence
  • You show a proactive attitude in communicating with other research partners and industry
  • You have good communication skills, especially with a non-expert audience
  • For educational tasks, proficiency in the Dutch language is a plus
  • You are not unfamiliar with designing proof-of-concepts using state-of- the-art components
  • You have organisational skills to organise workshops and network events for demonstrations or you want to learn those skills
  • You can work autonomously and independently, regularly reporting on project progress
  • Offer
  • You work on a state-of-the-art subject at a brand-new campus in Bruges with state-of-the-art laboratories
  • You work in a young and dynamic working environment where human contact and professionalism are very important. KU Leuven is one of the top 10 most innovative universities in the world.
  • You will build a network of industrial partners and consultancies in the sector.
  • A full-time position for 1 year.
  • Other employment conditions can be found at https: // www. kuleuven.be/personeel/jobsite/academisch-personeel/academic- staff-research-education-informatie#arbeidsvoorwaarden
  • Interested?

    For more information please contact Prof. dr. ir. Hans Hallez, tel.: +32 50 66 48 38, mail: hans.hallez@kuleuven.be or Prof. dr. ir. Jeroen Boydens, tel.: +32 50 66 48 03, mail: jeroen.boydens@kuleuven.be.

    KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.