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Naslov:Rapid assessment of steel machinability through spark analysis and data-mining techniques
Avtorji:ID Munđar, Goran (Avtor)
ID Kovačič, Miha (Avtor)
ID Brezočnik, Miran (Avtor)
ID Stępień, Krzysztof (Avtor)
ID Župerl, Uroš (Avtor)
Datoteke:.pdf metals-14-00955.pdf (5,24 MB)
MD5: 3137F172FF60892994F114D293233DA7
 
URL https://www.mdpi.com/2075-4701/14/8/955
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive and costly. This study presents a novel methodology to rapidly determine steel machinability using spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including various low-alloy and high-alloy steels, with most samples being calcium steels known for their superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15 values, which were measured using the standard ISO 3685 test. Our results demonstrate that the created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While some samples exhibited high MAPE values, the method overall provided accurate machinability predictions. Compared to the standard ISO test, which takes several hours to complete, our method is significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Ključne besede:steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:07.08.2024
Datum sprejetja članka:20.08.2024
Datum objave:22.08.2024
Založnik:MDPI
Leto izida:2024
Št. strani:19 str.
Številčenje:Vol. 14, iss. 8, [article no.] 955
PID:20.500.12556/DKUM-90645 Novo okno
UDK:621.7:004.8
COBISS.SI-ID:207322883 Novo okno
DOI:10.3390/met14080955 Novo okno
ISSN pri članku:2075-4701
Datum objave v DKUM:12.09.2024
Število ogledov:15
Število prenosov:15
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
MUNĐAR, Goran, KOVAČIČ, Miha, BREZOČNIK, Miran, STĘPIEŃ, Krzysztof in ŽUPERL, Uroš, 2024, Rapid assessment of steel machinability through spark analysis and data-mining techniques. Metals [na spletu]. 2024. Vol. 14, no. 8,  955. [Dostopano 25 marec 2025]. DOI 10.3390/met14080955. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=90645
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Gradivo je del revije

Naslov:Metals
Skrajšan naslov:Metals
Založnik:MDPI AG
ISSN:2075-4701
COBISS.SI-ID:15976214 Novo okno

Gradivo je financirano iz projekta

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P2-0157-2020
Naslov:Tehnološki sistemi za pametno proizvodnjo

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:obdelovalnost jekla, testiranje isker, podatkovno rudarjenje, strojni vid, konvolucijske nevronske mreže


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