| Title: | Rapid assessment of steel machinability through spark analysis and data-mining techniques |
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| Authors: | ID Munđar, Goran (Author)
ID Kovačič, Miha (Author)
ID Brezočnik, Miran (Author)
ID Stępień, Krzysztof (Author)
ID Župerl, Uroš (Author) |
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| Files: |  metals-14-00955.pdf (5,24 MB)
MD5: 3137F172FF60892994F114D293233DA7
 https://www.mdpi.com/2075-4701/14/8/955
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| 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. |
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| Keywords: | steel machinability, spark testing, data mining, machine vision, convolutional neural networks |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 07.08.2024 |
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| Article acceptance date: | 20.08.2024 |
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| Publication date: | 22.08.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 19 str. |
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| Numbering: | Vol. 14, iss. 8, [article no.] 955 |
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| PID: | 20.500.12556/DKUM-90645  |
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| UDC: | 621.7:004.8 |
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| ISSN on article: | 2075-4701 |
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| COBISS.SI-ID: | 207322883 |
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| DOI: | 10.3390/met14080955 |
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| Publication date in DKUM: | 12.09.2024 |
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| Views: | 15 |
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| Downloads: | 13 |
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| Metadata: |    |
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| Categories: | Misc.
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