Automation of Metallographic Sample Etching Process

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F23%3A00572567" target="_blank" >RIV/68081731:_____/23:00572567 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scientific.net/DDF.423.113" target="_blank" >https://www.scientific.net/DDF.423.113</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4028/p-s347g9" target="_blank" >10.4028/p-s347g9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automation of Metallographic Sample Etching Process

Popis výsledku v původním jazyce

Chemical etching is an integral part of metallographic sample preparation. Maintaining precise etch times can be difficult and therefore repeatability is limited. The aim of this work is to improve the repeatability of sample preparation using robotization. Prior to etching, metallographic samples of S355J2 (1.0577) structural steel were finely mechanically polished. For verification, 15 specimens were prepared using an in-house designed automated etching machine with a built-in 5-axis robotic arm and 15 specimens prepared manually by an expert metallographer. The samples were etched with Kourbatoff 4 reagent for 8 seconds in a beaker placed in an ultrasonic cleaner at 80 kHz. The samples were then cleaned in 7 beakers of cleaning fluid also placed in the ultrasonic cleaner. The robotic etching and cleaning process was optimized and the quality of the resulting surface is at least as good as that of the samples prepared by an expert metallographer. The surfaces were compared using a light optical microscope (LOM) and a confocal laser scanning microscope (CLSM). The repeatability of the preparation process is a key aspect for obtaining a large dataset of steel microphotographs for training a deep neural network that will be used in future research.

Název v anglickém jazyce

Automation of Metallographic Sample Etching Process

Popis výsledku anglicky

Chemical etching is an integral part of metallographic sample preparation. Maintaining precise etch times can be difficult and therefore repeatability is limited. The aim of this work is to improve the repeatability of sample preparation using robotization. Prior to etching, metallographic samples of S355J2 (1.0577) structural steel were finely mechanically polished. For verification, 15 specimens were prepared using an in-house designed automated etching machine with a built-in 5-axis robotic arm and 15 specimens prepared manually by an expert metallographer. The samples were etched with Kourbatoff 4 reagent for 8 seconds in a beaker placed in an ultrasonic cleaner at 80 kHz. The samples were then cleaned in 7 beakers of cleaning fluid also placed in the ultrasonic cleaner. The robotic etching and cleaning process was optimized and the quality of the resulting surface is at least as good as that of the samples prepared by an expert metallographer. The surfaces were compared using a light optical microscope (LOM) and a confocal laser scanning microscope (CLSM). The repeatability of the preparation process is a key aspect for obtaining a large dataset of steel microphotographs for training a deep neural network that will be used in future research.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/TN01000008" target="_blank" >TN01000008: Centrum elektronové a fotonové optiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Defect and Diffusion Forum

ISSN

1012-0386

e-ISSN

—

Svazek periodika

423

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

6

Strana od-do

113-118

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85159048343