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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20506 - Coating and films

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

High Temperature Corrosion of Materials

ISSN

2731-8397

e-ISSN

2731-8400

Svazek periodika

101

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1497-1512

Kód UT WoS článku

001342064500001

EID výsledku v databázi Scopus

2-s2.0-85207370113