Aluminum alloys are a class of materials widely used in industry due to their low density and excellent mechanical properties. Since the beginning of the 20th century, they have contributed significantly to the development of the aerospace industry. Structure-hardened aluminum alloys of the 2xxx family, of the Al-Cu-Mg type (or Duralumins), continue to be studied by physicists and metallurgists because subtle variations in their composition or the addition of small amounts of other elements (microalloying)combined with new thermomechanical treatments can result in improved macroscopic mechanical properties. Several experimental methods can be used to quantify dislocation density (dislocation length per unit volume), including X-ray diffraction (XRD) and transmission electron microscopy (TEM). While XRD provides an average value at the macroscopic scale, TEM observations allow for a more accurate local assessment at the grain scale. However, a statistically representative assessment by TEM ideally requires the acquisition and processing of a large number of images, acquired over a large field of view (several micron squares). Measuring density by image analysis is mainly a segmentation problem where dislocations must be delineated on an image, usually manually, which requires time and expertise in understanding dislocation contrasts. The idea of automating this work has recently emerged as a possibility through the use of deep learning methods [6], but has not yet been used in this context. The aim of the internship will be to establish a relevant and effective methodology for evaluating the density of dislocations in alloys using transmission electron microscopy.
Plus d’informations :
[Télécharger l'annonce - PDF 160 Ko]