| Title: | Application of machine learning method for hardness prediction of metal materials fabricated by 3D selective laser melting |
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| Authors: | ID Babič, Matej (Author)
ID Šturm, Roman (Author)
ID Rucki, Mirosłav (Author)
ID Siemiątkowski, Zbigniew (Author) |
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| Files: |  https://www.mdpi.com/2076-3417/15/23/12832
 applsci-15-12832.pdf (2,66 MB)
MD5: BE7214C3CCEBB285D0AF2BFDADD1FAA7
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FIŠ - Faculty of Information Studies in Novo mesto
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| Abstract: | 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. |
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| Keywords: | additive manufacturing, SLM, machine learning, hardness prediction, fractal geometry |
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| Submitted for review: | 30.11.2025 |
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| Article acceptance date: | 02.12.2025 |
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| Publication date: | 04.12.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-18 |
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| Numbering: | Vol. 15, iss. 23 (12832) |
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| PID: | 20.500.12556/ReVIS-12939  |
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| COBISS.SI-ID: | 261233923 |
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| UDC: | 004.85:621.9 |
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| ISSN on article: | 2076-3417 |
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| DOI: | 10.3390/app152312832 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 12. 12. 2025;
Soavtorji: Roman Šturm, Mirosław Rucki and Zbigniew Siemiątkowski;
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| Publication date in ReVIS: | 15.01.2026 |
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| Views: | 61 |
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| Downloads: | 1 |
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