Prethodno priopćenje

https://doi.org/10.31803/tg-20250327093142

A Machine Vision Approach to Assessing Steel Properties through Spark Imaging

Goran Munđar ; University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia *
Miha Kovačič ; Štore Steel d.o.o., Železarska 3, 3220 Štore, Slovenia
Uroš Župerl ; University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia

* Dopisni autor.

Sažetak

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.

Ključne riječi

Carbon Content Prediction; Convolutional Neural Networks; Deep Learning; Machine Vision; Spark Imaging; Steel Analysis

Hrčak ID:

330646

URI

https://hrcak.srce.hr/330646

Datum izdavanja:

1.6.2025.

Posjeta: 460 *