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Investigation of laser surface remelting supported by acoustic emission analysis and machine learning
ID Ravnikar, Dunja (Author), ID Mojškerc, Bor (Author), ID Šturm, Roman (Author)

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Abstract
In the present work, laser surface remelting (LSR) was carried out on C45 carbon steel using an Nd:YAG pulse laser. The effect of process parameters, such as different laser pulse durations and the absence or presence of a graphite absorber, on the microstructure, remelting depth, and microhardness was examined. In most cases, the graphite coating enhanced the laser energy absorption into the surface, resulting in greater depths of the remelted zone (RZ). RZ depth increased ranging from 8 to 350 pct, depending on the laser pulse duration. An increase in the surface microhardness by a factor of 2.6 was achieved in comparison with the substrate material microhardness, namely 559 HV 0.05 versus 211 HV 0.05. Concurrently, the LSR treatment parameters were also investigated using the in process generated acoustic emission (AE) signals. AE characteristics, such as AE peak amplitude, signal duration, count, and energy, were evaluated. A correlation of the AE characteristics was established for the various LSR treatment parameters. The LSR treatment classification results confirm the feasibility of using AE in combination with machine learning (ML) for monitoring LSR and the resulting surface properties of the hardened material.

Language:English
Keywords:laser surface remelting, microstructure, acoustic emission, machine learning
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Publication status:Published
Publication version:Author Accepted Manuscript
Year:2022
Number of pages:13 str.
Numbering:Vol. 53
PID:20.500.12556/RUL-134938 This link opens in a new window
UDC:620.1/.2:621.785:534
ISSN on article:1073-5623
DOI:10.1007/s11661-021-06552-7 This link opens in a new window
COBISS.SI-ID:96993283 This link opens in a new window
Publication date in RUL:11.02.2022
Views:4813
Downloads:213
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RAVNIKAR, Dunja, MOJŠKERC, Bor and ŠTURM, Roman, 2022, Investigation of laser surface remelting supported by acoustic emission analysis and machine learning. Metallurgical and materials transactions [online]. 2022. Vol. 53. [Accessed 7 July 2025]. DOI 10.1007/s11661-021-06552-7. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=134938
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Record is a part of a journal

Title:Metallurgical and materials transactions
Shortened title:Metall. mater. trans., A Phys. metall. mater. sci.
Publisher:Minerals, Metals & Materials Society, ASM International
ISSN:1073-5623
COBISS.SI-ID:2752790 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:04.02.2022

Secondary language

Language:Slovenian
Keywords:lasersko površinsko pretaljevanje, mikrostruktura, akustična emisija, strojno učenje

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P2-0270
Name:Proizvodni sistemi, laserske tehnologije in spajanje materialov

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