| Title: | A machine vision approach to assessing steel properties through spark imaging |
|---|
| Authors: | ID Munđar, Goran (Author)
ID Kovačič, Miha (Author)
ID Župerl, Uroš (Author) |
|---|
| Files: |  tj_19_2025_si1_77-81.pdf (1,84 MB)
MD5: 43D4B20D73A8033F872817FE0D093072
 https://hrcak.srce.hr/330646
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FS - Faculty of Mechanical Engineering
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | carbon content prediction, convolutional neural networks, deep learning, machine vision, spark imaging, steel analysis |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 01.06.2025 |
|---|
| Publisher: | Hrčak srce |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 77-81 |
|---|
| Numbering: | Vol. 19, no. 1, [article no.] 330646 |
|---|
| PID: | 20.500.12556/DKUM-95864  |
|---|
| UDC: | 004.8:669.1 |
|---|
| ISSN on article: | 1848-5588 |
|---|
| COBISS.SI-ID: | 248815619 |
|---|
| DOI: | 10.31803/tg-20250327093142 |
|---|
| Publication date in DKUM: | 03.11.2025 |
|---|
| Views: | 238 |
|---|
| Downloads: | 12 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
