Segmentation and Metallographic Evaluation of Aluminium Slurry Coatings Using Machine Learning Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10255819" target="_blank" >RIV/61989100:27740/24:10255819 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s11085-024-10321-3" target="_blank" >https://link.springer.com/article/10.1007/s11085-024-10321-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11085-024-10321-3" target="_blank" >10.1007/s11085-024-10321-3</a>

Result language

angličtina

Original language name

Segmentation and Metallographic Evaluation of Aluminium Slurry Coatings Using Machine Learning Techniques

Original language description

Analysis of scanning electron microscope (SEM) images is crucial for characterising aluminide diffusion coatings deposited via the slurry route on steels, yet challenging due to various factors like imaging artefacts, noise, and overlapping features such as resin, precipitates, cracks, and pores. This study focuses on determining the thicknesses of the coating layers Fe2Al5 and, if present, FeAl, pore characteristics, and chromium precipitate fractions after the heat treatment that forms the diffusion coating. A deep learning SEM image segmentation model utilising U-Net architecture is proposed. Ground truth data were generated using the trainable Weka segmentation plugin in ImageJ, manually refined for accuracy, and supplemented with synthetic data from Blender 3D software for data augmentation of a limited number of SEM label images. The deep learning model trained on a combination of synthetic and real SEM data achieved mean dice scores of 98.7% +- 0.2 for the Fe2Al5 layer, 82.6% +- 8.1 for pores, and 81.48% +- 3.6 for precipitates when evaluated on manually labelled SEM data. The deep learning procedure was applied to evaluate a series of SEM images of diffusion coatings obtained with three different slurry compositions. The evaluation revealed that using a slurry without a rheology modifier may lead to a thicker partial Fe2Al5 layer that is formed by inward diffusion. The relation between the outward and inward diffusion Fe2Al5 layers was not affected by the coating thickness. The thinner diffusion coating presents lower pores and chromium precipitate fractions independently of the slurry selected.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20506 - Coating and films

Project

—

Continuities

—

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

High Temperature Corrosion of Materials

ISSN

2731-8397

e-ISSN

2731-8400

Volume of the periodical

101

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

1497-1512

UT code for WoS article

001342064500001

EID of the result in the Scopus database

2-s2.0-85207370113