Functie

Background

3D printing (or additive manufacturing) is a manufacturing process which is gaining popularity within the aerospace sector thanks to the possibilities offered by this technology to manufacture highly optimized and efficient, lightweight structures.

One of the most used metal additive manufacturing methods is Laser Powder Bed Fusion (L-PBF), in which a laser selectively heats and melts powder in a powder bed. By adding material layer by layer a metal part is built. The stability of the LPBF process is strongly dependent on the printed geometry. Variations in microstructure and properties observed in the same product are caused by variations in thermal history of the L-PBF process. To be able to prevent these variations, an accurate prediction of this multiscale process is required. For this, phenomena on the temporal and spatial scale of the melt pool (<1ms and <0.2 mm), as well as on the scale of a complete part (>10 hours and >10 cm) should be captured. Analytical and finite element models on these  different spatial and temporal scales are available. However, to efficiently employ these models for process optimization is still an open point for research.

Assignment

The goal of this assignment is to implement the available modelling methods in innovative ways to optimize the LPBF process. An initial framework is available in Python which can be expanded to include more intelligent methods for adjusting the LPBF process for a more stable and predictable component. These methods can be verified experimentally by analysing print results and monitoring data.

Result

Methods to optimize the LPBF process based on (multi-scale) modelling results.

Duration

5-9 months

Profiel

Profile

• HBO or WO Master in mechanical or aerospace engineering (or another relevant field)
• Experience with FEM and scripting in python is a plus
• Assertive and self-motivated, able to be part of the project team and also proceed individually

Arbeidsvoorwaarden

What we offer

• A challenging graduation project/internship in a high-tech result orientated work environment
• Weekly supervision and availability of the technical staff for support
• An internship allowance
• Working in an actual R&D project as part of the team
• Internship results to be used in the current and future projects

Informatie

About NLR

Royal NLR has been the ambitious research organisation with the will to keep innovating for over 100 years. With that drive, we make the world of transportation safer, more sustainable, more efficient and more effective. We are on the threshold of breakthrough innovations. Plans and ideas start to move when these are fed with the right energy. Over 650 driven professionals work on research and innovation. From aircraft engineers to psychologists and from mathematicians to application experts.

Our colleagues are happy to tell you what it’s like to work at NLR.
This assignment will be managed by the computational mechanics group within the Aerospace Vehicles Collaborative Engineering (AVCE) department.

Solliciteren

Interested?

Send your application, together with your motivation letter and CV to tim.koenis@nlr.nl and we will contact you as soon as possible.