20.500.12556/ReVIS-12939 Application of machine learning method for hardness prediction of metal materials fabricated by 3D selective laser melting In this article, models for prediction of surface hardness for SLM specimens are presented. In experiments, EOS Maraging Steel MS1 was processed using EOS M 290 3D printer via selective laser melting (SLM). To predict hardness of SLM specimens, several machine learning methods were applied, including genetic programming, neural network, multiple regression, k-nearest neighbors, support vector machine, logistic regression, and random forest. In the research, fractal geometry was used to characterize the complexity of SLM-shaped microstructures. It was found that fractal geometry combined with machine learning techniques together greatly improved our comprehension of the intricacies of surface analysis and provided highly efficient predictions. All the applied algorithms exhibited predictability above 90%, with the best average result of 98.7% for genetic programming. additive manufacturing SLM machine learning hardness prediction fractal geometry aditivna proizvodnja SLM strojno učenje napovedovanje trdote fraktalna geometrija true true true Angleški jezik Slovenski jezik Neznano 2026-01-15 10:48:20 2026-01-15 10:48:20 2026-01-16 03:10:46 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 12. 12. 2025; Soavtorji: Roman Šturm, Mirosław Rucki and Zbigniew Siemiątkowski; str. 1-18 iss. 23 (12832) Vol. 15 2025 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 2025-11-30 2025-12-02 2025-12-04 004.85:621.9 2076-3417 261233923 10.3390/app152312832 https://www.mdpi.com/2076-3417/15/23/12832 1 53947963-f1f7-11f0-94a7-001a4af901a5 https://revis.openscience.si/Dokument.php?lang=slv&id=15267 applsci-15-12832.pdf applsci-15-12832.pdf 1 BE7214C3CCEBB285D0AF2BFDADD1FAA7 0f616467bc4c006abe68b4759f6f93d93b9c79c8b7e765f53ce8ac285c89602c c873c42c-f1f7-11f0-94a7-001a4af901a5 https://revis.openscience.si/Dokument.php?lang=slv&id=15268 Fakulteta za informacijske študije v Novem mestu 0 0 0