Multi-sensor monitoring of laser powder bed fusion additive manufacturing

(ref. BAP-2022-550)

Laatst aangepast : 18/07/2022

This PhD position is hosted by the Additive Manufacturing (AM) research group, under the direct supervision of Prof. Bey Vrancken – as part of the Manufacturing and Process Systems division (MaPS) at the Department of Mechanical Engineering, KU Leuven. The KU Leuven AM group has over three decades of expertise in AM research. At present, the AM group is continuing and extending their research activities in different areas related to laser-based AM (see also the research group website): broadening the materials palette for AM (ceramics, aluminum, bio materials, …); relating process conditions to static and dynamic mechanical properties via analysis of micro structure, texture, porosity, thermal stresses,…; machine design and process optimization (software and hardware, integrating new enabling technologies); and in-situ and post-process quality control (X-ray CT, process monitoring, closed loop control, post-build treatments).

Project

Online monitoring of laser powder bed fusion (LPBF) is typically performed using coaxial (viewing along the laser trajectory) melt pool cameras, coaxial photo diodes, off-axis cameras, or some combination thereof. The monitoring information is typically processed individually, even if multiple information streams are available. Unfortunately, there is a lot of noise and uncertainty related to process monitoring of LPBF, and none of the individual monitoring techniques have proven to have a highly accurate correlation with the processing conditions. Such a high fidelity correlation is required for online process monitoring to move towards online process control, which is the ultimate goals.

The project consists of:

• Use of new monitoring techniques, including acoustic emission monitoring and any other promising technique, to assess their feasibility to monitor the LPBF process.

• Improved signal processing of the existing and new techniques, to extract more process-relevant information. One such example is the recent work by L. Goossens in this publication: https://www.sciencedirect.com/science/article/pii/S2214860421000889(paper available upon request).
  

• Combining the various available sources of information (data or sensor fusion) to improve the correlation with processing phenomena, starting with the process regime, but also to individual or isolated phenomena such as the generation of defects, or even hard to observe information such as microstructural transformations or the generation of residual stresses.
  

Moreover,these steps should be performed taking into consideration the limitations imposed by the eventual implementation into process control loops: fast processing speeds, data transfer, and limited computational space. 

When successful, the project may move towards actual process control.

Profile

• The ideal candidate is highly motivated, enthusiastic and communicative researcher with a Masters degree in Mechanical Engineering, Process Engineering, Automation Engineering, Electrical Engineering, or a closely related field. You have experience with data science and are familiar with Additive Manufacturing processing conditions and machine hardware, or are eager to educate themselves on these topics. You have obtained your degree with distinction or the equivalent thereof, and are able to work independently on this PhD project, as well as collaborate with fellow researchers in the AM group and beyond.
• Candidates should have experience with experimental work, (control of) manufacturing systems, data processing/other software tools (Python, Matlab, LabView, …), and materials characterization techniques (metallography, …). Specific experience with metal additive manufacturing is a plus. Furthermore, applicants should have excellent oral and written English communication skills (TOEFL score of at least 94 or IELTS-score of 7 or higher).
• The successful candidate is expected to contribute to education at the Bachelor or Master level (master thesis supervision, teaching of exercise or practical sessions, …)

Offer

• A doctoral scholarship (fully funded) for four years at the Department of Mechanical Engineering, and a Ph.D. degree in Engineering Science if successful.
  
• A remuneration package competitive with industry standards in Belgium, a countrywith a high quality of life and excellent health care system.
  
• A highly specialized doctoral training (see Arenberg Doctoral School) in an international environment at a top European university to allow you to gain the skills required to successfully complete your PhD, as well as develop yourself as an independent researcher.
  
• Multiple benefits (health insurance, access to university infrastructure and sport facilities, self-development through skills training courses etc.).
  
• The opportunity to participate in research collaborations and international conferences.
  
• A highly motivated group of colleagues with excellent group dynamic, stimulatedt hrough various activities organized outside of work.
• A stepping stone towards future career opportunities. KU Leuven AM group alumni have an excellent track record in pursuing both academic as well as industrial careers, both in Flanders and abroad.
  
• 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 (25 km), 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.
  

The successful candidate is expected to start as early as possible, but no later than October 1st 2022 (upon agreement with Prof. Vrancken).

Interested?

Documents required forapplication:

1.       FullCV – mandatory

2.       Motivationletter (maximum 1 page) – mandatory

3.       Fulllist of credits and grades of both BSc and MSc degrees (as well as theirtranscription to English if possible) – mandatory (when you haven’t finishedyour degree yet, provide the partial list of already available credits andgrades)

4.       Proofof English proficiency (TOEFL, IELTS, …) - if available

5.       Tworeference letters - if available

6.       AnEnglish version of MSc or of a recent publication or assignment - if available

For more information do nothesitate to contact Prof. dr. ir. Bey Vrancken (tel.: +32 16 19 48 65, mail: [email protected]).

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 [email protected].