This work aims to contribute to the improvement of automated analysis of 2D microstructures in technical aluminum alloys by evaluating the effectiveness of various microstructural image analysis methods: Traditional analysis techniques for 2D images and cutting-edge computer vision deep learning-based methods (Mask Region-based Convolutional Neural Networks or Mask R-CNN [1], a model used to detect the position and exact shape of objects) are compared for detecting, classifying, and quantifying microstructural features in technical iron containing aluminum alloys. Determining that deep learning-based methods lead to comparable or better results would allow for such models to be used for analysis of the chosen microstructural features. This could improve detection accuracy and speed, as well as measurement reproducibility.
The focus is on detecting and characterizing Fe-containing
intermetallic phases that precipitate during solidification, with the aim of
examining the effects of increased iron content on the microstructure of these
alloys. Therefore, the microstructure of directionally solidified hypoeutectic
AlSi6Cu4Fe1 and hypereutectic AlSi6Cu4Fe2 alloys is analyzed with regards to
area fraction and size of the intermetallic precipitates within two distinct
sample processing zones: the first half of the samples was solidified under
diffusive mass transport conditions and the second under forced convective
conditions, both in microgravity. This dual approach allows for the evaluation
of the impact of convection on the microstructure of aluminum alloys with
elevated iron content.
Author
(s) Details
Golo
Zimmermann
RWTH Aachen University, Foundry Institute, Intzestraße 5, 52072
Aachen, Germany.
Sonja
Steinbach
Institut für Materialphysik im Weltraum, Deutsches Zentrum für
Luft- und Raumfahrt (DLR), 51170 Köln, Germany.
Laszlo
Sturz
ACCESS e.V., Intzestraße 5, 52072 Aachen, Germany.
Alexandre
Viardin
ACCESS e.V., Intzestraße 5, 52072 Aachen, Germany.
Angelos
Theofilatos
ACCESS e.V., Intzestraße 5, 52072 Aachen, Germany.
Florian
Kargl
RWTH Aachen University, Foundry Institute, Intzestraße 5, 52072
Aachen, Germany and Institut für Materialphysik im Weltraum, Deutsches Zentrum
für Luft- und Raumfahrt (DLR), 51170 Köln, Germany.
Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49473-95-9/CH5
No comments:
Post a Comment