A machine vision approach to assessing steel properties through spark imagingMunđar, Goran (Avtor) Kovačič, Miha (Avtor) Župerl, Uroš (Avtor) carbon content predictionconvolutional neural networksdeep learningmachine visionspark imagingsteel analysisAccurate 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.Hrčak srce20252025-11-03 12:09:31Članek v reviji95864UDK: 004.8:669.1COBISS_ID: 248815619DOI: 10.31803/tg-20250327093142ISSN pri članku: 1848-5588sl