PhD position on data-driven, adaptive 3D printing

Updated: 2 months ago
Deadline: 15 Feb 2023

(ref. BAP-2023-18)

Laatst aangepast : 20/01/2023

This vacancy refers to a PhD position in the framework of an internal KU Leuven project on the topic of 3D printing (also called additive manufacturing) using Fused Filament Fabrication (FFF). The project is a close collaboration between the departments of Computer Science and Mechanical Engineering. The selected PhD candidate will therefore have promotors from the two departments, ensuring a true multi-disciplinary nature of the research. The objective is to research process monitoring and control via a combination of physical approaches and Artificial Intelligence (AI) methods towards adaptive 3D printing. Description of the organizational unit. Due to the nature of the research, the candidate will be active at KU Leuven on 2 different campuses (De Nayer and Bruges) and in 2 different departments (Mechanical Engineering and Computer Science). The promotors of the PhD will be Prof. dr. ir. Eleonora Ferraris (, +3215316944) from the AML group of the Mechanical Engineering department, campus De Nayer ( ), and Prof. dr. Mathias Verbeke, ( from the M-Group, which gathers interdisciplinary expertise from the departments of Computer Science, Electrical and Mechanical Engineering, at the Bruges campus ( ). Upon successful completion of the PhD trajectory, the candidate will obtain a PhD in Mechanical Engineering at the Faculty of Engineering Technology ( of KU Leuven (


Outline :

Fused Filament Fabrication (FFF) is one of the best known additive manufacturing techniques for the production of thermoplastics based components. It is based on material extrusion and it is thermal energy driven. In the Advanced Manufacturing Lab(AML) of KU Leuven, campus de Nayer, code enabling to predict and simulate the build temperature of FFF (Fused Filament Fabrication) printed parts has been developed [doi:10.1007/s40964-022-00271-0]. This code, which is named 'Temperature for Fused Filament Fabrication' (T4F3), has been successfully applied to predict critical reheating temperatures for high-quality and efficient printing of PLA (polylactic acid) [doi: 10.1016/j.cirp.2022.03.046] parts and to develop the first examples of adaptive printing strategies in FFF.

Content :

In this PhD project, we aim to develop a stand-alone tool “AF3 - Adaptive Fused Filament Fabrication”, which will generate adaptive tool paths/G-code for printing free form shapes efficiently, while maintaining consistent quality, regardless of the part design or the material. This will provide deep insights into the relations between thermal history and thermally driven failures. A combined physical and data-driven (machine learning-based)approach will be used to solve less evident physical relations. With AF3 we aim at a more efficient printing process and first-time-right outcomes, thus avoiding wasting material and energy. This will further improve the sustainability of the process.


 A Master's degree in Science or Engineering witha background in Mechanical Engineering, Material Engineering, Chemical Engineeringor Computer Science, or an equivalent Master’s degree.

●The candidate preferably has a background both in Manufacturing and AI, buteagerness to learn is certainly just as important. Understanding thermaltransfer mechanisms is considered an added value.

●Graduation with distinction is a requirement to start the PhD

●Proficiency with programming in Python or C++

●Expertise in additive manufacturing with focus on fused filament fabrication(FFF) is a plus.

●Being fluent in English (both speaking and writing) is a must

●You are creative and a team worker

●You are curious, and application driven with interest in science


Ph.D.fellowship for the duration of a maximum of 4 years at competitive salary.

●A challenging project with a very large industrial valorisation potential

●A multidisciplinary training and working environment

●A highly valued academic environment and multi-cultural working group


For more information please contact Prof. Mathias Verbeke, tel.: +32 50 66 47 76, mail:, Prof. Eleonora Ferraris, mail:, or Dr. Ann Witvrouw, tel.: +32 16 32 71 59, mail:

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