PhD Position: Digital Twin Framework Enabling Fleet-Level Feed Drive Systems Health Assessment and Maintenance Planning

Systems Health Assessment and Maintenance Planning

(ref. BAP-2023-129)

Last modification : Friday, March 17, 2023

The PhD candidate will be appointed in the Department of Mechanical Engineering and the Faculty of Engineering Technology of the Science, Engineering and Technology Group at KU Leuven, and will be located at Campus Bruges. The research activities will be embedded in the Mechatronics Research Group (M-Group, https: // iiw.kuleuven.be/brugge/m-group) located there. Research activities of the M-Group focus on the design, development and validation of dependable interconnected mechatronic systems. The M-Group consists of professors from the Departments of Mechanical Engineering, Electrical Engineering and Computer Science, and operates in a truly interdisciplinary environment. Within the Department of Mechanical Engineering, the research is linked to the activities of the Mecha(tro)nic System Dynamics (LMSD) research division (https: // www. mech.kuleuven.be/lmsd). With its activities, the LMSD division aims to create added value during each phase of the life cycle of mechanical and mechatronic systems, by understanding, monitoring and controlling their dynamic behaviour. Core concepts here are Model-Based System Engineering (MBSE) and Digital Twins. The M-group as well as the LMSD division build upon solid research infrastructure, an extensive regional, national and international network, and connections with companies and non-profit organisations in Belgium and abroad.

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The feed drive is one of the most critical systems used for the workpiece and tool's desired positioning and provides high rigidity by preloading. Its degradation exhibits significant machining errors (backlash) and vibrations which affect the product quality. The challenges, such as the unavailability of data for all failure modes, domain-specific data processing strategies and the black-box nature, and the high modelling uncertainty of machine learning models at the fleet level, restrict the implementation of existing health assessment and maintenance planning solutions for feed drive systems at the actual factory level. This PhD proposal aims to overcome these issues and develop a digital twin framework for feed drive systems health assessment and maintenance planning.

You share strong interest in design or manufacturing, and you have:

For more information please contact Prof. dr. ing. Pradeep Kundu, tel.: +32 50 66 47 54, mail: [email protected].

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