Conseil National de Recherches Canada

Beginning of the year 2016 I worked for 3 month at the National Research Council (CNRC/NRC) in Boucherville, Canada as a visiting research fellow.
The National Research Council is the primary national research and technology organization (RTO) of the Government of Canada, in science and technology research and development.[1] The Minister of Innovation, Science, and Economic Development is responsible for the NRC.
NRC is uniquely positioned to deliver a compelling value proposition to our clients and collaborators through a focus on commercial success and tangible impacts on industrial growth. NRC encourages and engages in multiple forms of collaboration for a project. Our collaborative research projects span a very broad spectrum of activities and business structure models.
Access to NRC expertise and equipment provides our collaborators with the opportunity to accelerate their commercial development timelines, while a focus on industrial applications provides NRC with insights into commercial direction for research and technology development activities.
[Online] CNRC/NRC [cited at: 19. April 2016.]

For further details see: CNRC/NRC

During my stay I was involved in optimising the X-ray Micro-CT, the existing image analysis systems as well as the implementation of new analysing methods to investigate titanium powders and foams.
The background of my work was motivated by large scale deployment of additive manufactoring processes (AM) as part quality relies specifically on the presence of internal defects and part-to-part consistency. Some of the defects observed in finished parts have been associated with porosities in the powder feedstock used in AM processes including powder bed, laser cladding, and cold spray. Since the level of porosity in these powders is generally very low, standard characterization techniques such as pycnometry, and metallography with image analysis, are not suitable for quantification. Investigating powders by using X-ray tomography and image analysis offer a new approach combining high resolution images with an automated 3D image analysis routine to evaluate and quantify porosity in titanium powder feedstock. The effects of acquisition parameters and image analysis procedures on porosity quantification were investigated to validate the proposed method and assess its reliability. Data demonstrate that the proposed technique is sufficiently sensitive to differentiate powders with different porosity levels.

First we concentrated on the image acquiring process by X-ray tomography to achieve optimal raw data. The first figure compares the obtained results when the images of the powder (Ti) are averaged on 8 frames or not; the impact on the grey value scales is also shown. Finally, one of the first steps during the image analysis routine is to use a median filter to increase phase distinction: the impact of the filter on the images and grey values is presented in Figure 1 too.

Impact of frame averaging and filtering on (a) the obtained images and on (b) the

But also the reconstructed voxel size on the obtained images was investigated. While, only a small difference in image sharpness was found for 3 µm and the 10 µm voxel sizes, the image analysis routine was not able to analyse the 10 µm image without the use of a median filter, which creates a lot of artefacts (powder merging).

Influence of voxel size on particle segmentation

The segmentation process yields similar results when we compare the 3 and 5 µm voxel size images, see image above. Now, when the results of the image analysis routine for the three resolutions are compared quantitatively (lower figure), it is clear that scanning at a resolution of 10 microns is not enough for the Ti-powder. An example of the segmentation in 2D and 3D can see in the figures below
2D slice
3D reconstruction
Another crucial powder characteristic for powder producers and part manufacturers alike is the presence of pores inside particles and the quantification of their volume fraction. Only on the optimised images above an image analysis routine can be performed to separate individual pores and to quantify those. The final result of the pore size separation can be seen in the lower movie.

Separated pores in 3D