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Title:Modeliranje topografskih lastnosti 3D tiskanih kovinskih materialov
Authors:ID Babič, Matej (Author)
Files:URL https://journals.um.si/index.php/anali-pazu/article/view/5660/3485
 
.pdf Clanek+3_43-53.pdf (2,88 MB)
MD5: 10F6887CF7B0EAB7E4E833D6F9BA0A33
 
Language:Slovenian
Work type:Unknown
Typology:1.01 - Original Scientific Article
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract:Ta raziskava predstavlja prelomno metodo, ki združuje fraktale, teorijo omrežij in genetsko programiranje, z analizo topografije površine kovinskih aditivnih delov. Natančneje, osredotočena je na selektivno lasersko taljenje maragingnega jekla EOS MS1, obdelanega s 3D-tiskalnikom EOS M 290. Ugotovitve kažejo na znatno izboljšanje natančnosti karakterizacije hrapavosti površine pri uporabi genetskega programiranja. Uporaba fraktalne geometrije in strojnega učenja je izboljšala naše razumevanje kompleksnosti selektivnega laserskega taljenja površin. Ta študija ne le prispeva k področju aditivne proizvodnje s ponujanjem učinkovitejšega in natančnejšega pristopa k nadzoru kakovosti, temveč tudi postavlja temelje za prihodnja raziskovanja drugih materialov in izpopolnjevanje analitičnih tehnik. Potencial te metode pri podpori praks 3D-tiskanja kovin je precejšen, kar kaže na obetavno prihodnost za industrijo tako v smislu inovacij kot uporabe.
Keywords:aditivna proizvodnja, selektivno lasersko taljenje, hrapavost površine, fraktalna geometrija, teorija omrežij, genetsko programiranje
Submitted for review:27.11.2025
Article acceptance date:01.11.2025
Publication date:28.11.2025
Year of publishing:2025
Number of pages:str. 43-53
Numbering:Letn. 15, št. 1/2
PID:20.500.12556/ReVIS-12938 New window
COBISS.SI-ID:261239043 New window
UDC:004.94:621.9
ISSN on article:2820-364X
Publication date in ReVIS:15.01.2026
Views:57
Downloads:1
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Record is a part of a journal

Title:Anali PAZU
Publisher:Združenje Pomurska akademsko znanstvena unija, Združenje Pomurska akademsko znanstvena unija, Univerzitetna založba Univerze v Mariboru
ISSN:2820-364X
COBISS.SI-ID:97522435 New window

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

Language:English
Title:Modeling the topographic properties of 3D printed metal materials
Abstract:This research presents a groundbreaking method that combines fractals, network theory, and genetic programming to analyze the surface topography of metal additive parts. Specifically, it focuses on the selective laser melting of EOS MS1 maraging steel processed with the EOS M 290 3D printer. The findings show a significant improvement in the accuracy of surface roughness characterization using genetic programming. The use of fractal geometry and machine learning has improved our understanding of the complexity of selective laser melting of surfaces. This study not only contributes to the field of additive manufacturing by offering a more efficient and accurate approach to quality control, but also lays the foundation for future research into other materials and the refinement of analytical techniques. The potential of this method in supporting metal 3D printing practices is considerable, indicating a promising future for the industry in terms of both innovation and application
Keywords:senzor additive manufacturing, selective laser melting, surface roughness, fractal geometry, network theory, genetic programming


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