| Naslov: | A machine vision approach to assessing steel properties through spark imaging |
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| Avtorji: | ID Munđar, Goran (Avtor)
ID Kovačič, Miha (Avtor)
ID Župerl, Uroš (Avtor) |
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| Datoteke: |  tj_19_2025_si1_77-81.pdf (1,84 MB)
MD5: 43D4B20D73A8033F872817FE0D093072
 https://hrcak.srce.hr/330646
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FS - Fakulteta za strojništvo
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| Opis: | Accurate and efficient evaluation of steel properties is crucial for modern manufacturing. This study presents a novel approach that combines spark imaging and deep learning to predict carbon content in steel. By capturing and analyzing sparks generated during grinding, the method offers a fast and cost-effective alternative to conventional testing. Using convolutional neural networks (CNNs), the proposed models demonstrate high reliability and adaptability across different steel types. Among the tested architectures, MobileNet-v2 achieved the best performance, balancing accuracy and computational efficiency. The findings highlight the potential of machine vision and artificial intelligence in non-destructive steel analysis, providing rapid and precise insights for industrial applications. |
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| Ključne besede: | carbon content prediction, convolutional neural networks, deep learning, machine vision, spark imaging, steel analysis |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Datum objave: | 01.06.2025 |
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| Založnik: | Hrčak srce |
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| Leto izida: | 2025 |
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| Št. strani: | str. 77-81 |
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| Številčenje: | Vol. 19, no. 1, [article no.] 330646 |
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| PID: | 20.500.12556/DKUM-95864  |
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| UDK: | 004.8:669.1 |
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| COBISS.SI-ID: | 248815619 |
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| DOI: | 10.31803/tg-20250327093142 |
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| ISSN pri članku: | 1848-5588 |
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| Datum objave v DKUM: | 03.11.2025 |
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| Število ogledov: | 240 |
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| Število prenosov: | 12 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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