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Title:Rapid assessment of steel machinability through spark analysis and data-mining techniques
Authors:ID Munđar, Goran (Author)
ID Kovačič, Miha (Author)
ID Brezočnik, Miran (Author)
ID Stępień, Krzysztof (Author)
ID Župerl, Uroš (Author)
Files:.pdf metals-14-00955.pdf (5,24 MB)
MD5: 3137F172FF60892994F114D293233DA7
 
URL https://www.mdpi.com/2075-4701/14/8/955
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract: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.
Keywords:steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Publication status:Published
Publication version:Version of Record
Submitted for review:07.08.2024
Article acceptance date:20.08.2024
Publication date:22.08.2024
Publisher:MDPI
Year of publishing:2024
Number of pages:19 str.
Numbering:Vol. 14, iss. 8, [article no.] 955
PID:20.500.12556/DKUM-90645 New window
UDC:621.7:004.8
ISSN on article:2075-4701
COBISS.SI-ID:207322883 New window
DOI:10.3390/met14080955 New window
Publication date in DKUM:12.09.2024
Views:15
Downloads:15
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
MUNĐAR, Goran, KOVAČIČ, Miha, BREZOČNIK, Miran, STĘPIEŃ, Krzysztof and ŽUPERL, Uroš, 2024, Rapid assessment of steel machinability through spark analysis and data-mining techniques. Metals [online]. 2024. Vol. 14, no. 8,  955. [Accessed 25 March 2025]. DOI 10.3390/met14080955. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=90645
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Record is a part of a journal

Title:Metals
Shortened title:Metals
Publisher:MDPI AG
ISSN:2075-4701
COBISS.SI-ID:15976214 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0157-2020
Name:Tehnološki sistemi za pametno proizvodnjo

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:obdelovalnost jekla, testiranje isker, podatkovno rudarjenje, strojni vid, konvolucijske nevronske mreže


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