Internship LPBF Slicing and downskin improvement

Development of an advanced Laser Powder Bed Fusion build file generation tool for producing homogeneous and predictable material

Graduation project or internship

Background

Additive Manufacturing (AM) technologies build products layer by layer using data directly from 3D CAD models. AM has high potential for improving the performance and reducing the weight of parts which is especially beneficial for aerospace applications. Reliable and predictable behaviour is essential for safety in aerospace applications. This internship project focuses on Laser Powder Bed Fusion (L-PBF) technology. With L-PBF, complex products are constructed layer-by-layer within a powder bed (see Figure 1). The metal powder is selectively molten by a laser beam. Dedicated software tools are used to generate a build file in which the applied process parameters and the laser scan pattern for each layer are defined. The complex design can easily cause large temperature variations during LPBF production which can result in variation of material properties, for example in overhanging structures and sharp corners trapping heat. Currently available software tools do not offer the required flexibility in generating dedicated scan patterns with variation of process parameters to produce homogeneous material with predictable properties.

Assignment

The goal of this project is to develop a software tool for creating toolpaths for the laser and implementing appropriate laser scanning strategies for bulk and overhanging regions. Currently, the creation of laser toolpaths is done using licensed software, limiting the flexibility in customising the toolpath and scan parameters. Open source python libraries such as the PySLM library can be used to build a software tool that generates the scan vectors and identifies regions where different scanning strategies should be applied. This increases control over the toolpaths and allows for more advanced optimisation of the LPBF process.

Once the basic structure of the software tool is operational, more complex scanning strategies can be implemented to improve material quality and homogeneity in complex geometries. Some examples of these geometries is shown in Figure 2, where overhanging regions either require significant amounts of supporting structures or advanced scanning strategies to prevent large temperature variations and deformations.

As complex geometry can significantly increase the time required for the generation of the toolpaths it is essential to keep in mind the efficiency of the software tool and possible optimization through rewriting in a lower-level language such as C++.

Activities

• Set up a project plan

• Literature study into:

o LPBF in general

o Slicing of CAD geometries for Additive Manufacturing

o Methods of down-skin identification

o Enable application of dedicated scan strategies in down skin areas

o Enable application of variable process parameters in scan strategy

• Design a test geometry for validation of slicer

• Fabrication and investigation of processed structures

• Analysis of experimental results

• Writing the report

Duration

The duration is expected to be at least 6 months. The duration and starting time may be discussed depending on the requirements of the study programme.

Profile

For this internship, we are looking for a curious, enthusiastic, academic-level thinking and independent student who has affinity with Python and Additive Manufacturing.

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 on an actual R&D project as part of our team

• Internship results to be used in current and future projects

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 1000 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 is hosted by the Metal Additive Manufacturing Technology Centre (MAMTeC) within the Aerospace Vehicles Metal Additive Manufacturing and Computational Mechanics (AVMC) department. More information can be found here: https://www.nlr.org/capabilities/additive-manufacturing

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.

Solliciteren?

Mooi, we zijn benieuwd naar jou! Stuur je motivatiebrief en CV via de ‘Solliciteren’ button.

Voor deze rol is er een screening VOG van toepassing.

Acquisitie wordt niet op prijs gesteld!