Development of data-driven methodologies for the prediction of part performance (ref. BAP-2025-154)

The proposed research track runs at the KU Leuven Mecha(tro)nic System Dynamics (LMSD) division which currently counts more than 100 researchers and is part of the Department of Mechanical Engineering, a vibrant environment of more than 300 researchers (www.mech.kuleuven.be). Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd). LMSD has a longstanding history and internationally highly recognized expertise in the fields of condition monitoring, numerical modeling, engineering dynamics, automotive engineering, vibro-acoustic analysis, identification and robust optimal control of (non-) linear systems, active control and lightweight structure design and analysis. It is also recognized for its yearly Modal Analysis (ISMA) and Acoustics (ISAAC) courses and for organizing the biennial ISMA Noise and Vibration Engineering Conference (www.isma-isaac.be). The research group has a long track record of combining excellent fundamental academic research with industrially relevant applications, leading to dissemination in both highly ranked academic journals as well as on industrial fora. Furthermore, the group contributes to the Flanders Make@KU Leuven Motion Products University Core Lab of Flanders Make. Flanders Make (https://www.flandersmake.be/en) is the strategic research centre for the manufacturing industry in Flanders, stimulating open innovation through excellent research. The research group's international research flavour is illustrated amongst others by the large portfolio of research projects (https://www.mech.kuleuven.be/e...) which includes regional, national and international funded activities through which the group cooperates with leading mechatronic and machine & vehicle-building companies in Flanders and throughout Europe. More information on the research group can be found on the website: https://www.mech.kuleuven.be/e... and our Linked.In page: https://www.linkedin.com/showc.... The PhD will be supervised by Prof. Konstantinos Gryllias.

https://www.mech.kuleuven.be/e...

Project

Designing and producing high performant injection moulded parts for automotive and biomedical applications is a tedious task, requiring expert knowledge and multiple iterations. One of the key issues is that during the design most often the effect of the production process on the performance of the manufactured part is not accounted for. The injection moulding process may lead to undesired warpage, internal stresses due to e.g., fiber orientations, and density and mass distributions within the part, which are all of key importance for the resulting static and dynamic mechanical performance of the part. As a consequence, modifications have to be made to the mould associated to the part and/or to the process settings, to iteratively ensure the part is within specifications.

Therefore the goal of the Flanders Make SBO project is to reduce or even eliminate these iterations by linking the outcome of high-fidelity injection moulding simulations with high fidelity part performance simulations. By comparing actual measurements with model-based predicted values, changes in the production process can be captured and adjusted process parameter settings can be proposed in real-time.

The focus of this PhD track will be the development of a data-driven methodology to predict the part performance (in terms of strength, stiffness, and dynamic properties) starting from the injection moulding process parameters and, when available, the material characteristics. Methodologies for the combination of simulation data with real data will be investigated as well transfer learning strategies for efficiently model (re)training on different real-world process conditions, material properties and/or parts. Finally a methodology which will combine in-process data with simulated data, material properties and machine setpoints in order to estimate improved setpoints if the product performance is off due to variations in the process or input material will be considered to close the loop.

Profile

If you recognize yourself in the story below, then you have the profile that fits the project and the research group.

• I have a master degree in engineering, physics, computer science or mathematics and performed above average in comparison to my peers.
• I am proficient in written and spoken English.
• I have a genuine interest in combining sensing techniques, machine learning, first principle models and measurement approaches into an innovative toolchain and in injection model and I have experience with (at least) some of these topics.
• I have good programming skills in Matlab and/or in Python (in particular Tensorflow or Pytorch).
• As a PhD researcher of the KU Leuven Mecha(tro)nic System Dynamics (LMSD) division I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
• Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
• In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
• I value being part of a large research group which is well connected to the machine and transportation industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
• During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.

Offer

• A remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
• An opportunity to pursue a PhD in Mechanical Engineering, typically a 4 year trajectory, in a stimulating and ambitious research environment.
• Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context. More information on the training opportunities can be found on the following link: https://set.kuleuven.be/phd/do...;
• A stay in a vibrant environment in the hearth of Europe. The university is located in Leuven, a town of approximately 100000 inhabitants, located close to Brussels (25km), and 20 minutes by train from Brussels International Airport. This strategic positioning and the strong presence of the university, international research centers, and industry, lead to a safe town with high quality of life, welcome to non-Dutch speaking people and with ample opportunities for social and sport activities. The mixture of cultures and research fields are some of the ingredients making the university of Leuven the most innovative university in Europe (KU Leuven is the Most Innovative University of Europe – Faculty of Arts). Further information can be found on the website of the university: https://www.kuleuven.be/englis...