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Automation of Metallographic Sample Etching Process
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SYSNO ASEP 0572567 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve SCOPUS Title Automation of Metallographic Sample Etching Process Author(s) Ambrož, Ondřej (UPT-D) ORCID, RID, SAI
Čermák, Jan (UPT-D)
Jozefovič, Patrik (UPT-D)
Mikmeková, Šárka (UPT-D) RID, SAI, ORCIDNumber of authors 4 Source Title Defect and Diffusion Forum - ISSN 1012-0386
Roč. 423, April (2023), s. 113-118Number of pages 6 s. Publication form Print - P Language eng - English Country CH - Switzerland Keywords chemical etching ; metallography ; process automation ; repeatability ; robotics Subject RIV JG - Metallurgy OECD category Materials engineering R&D Projects TN01000008 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) Method of publishing Limited access Institutional support UPT-D - RVO:68081731 EID SCOPUS 85159048343 DOI 10.4028/p-s347g9 Annotation 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. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2024 Electronic address https://www.scientific.net/DDF.423.113
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