Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]23/09/2025 at 10:27 AM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 7 18

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    Rakesh Prasad, Lokesh Pachauri, Alok Singh, Madhav Rawat, Hardeep Singh, Vijay S. Katta,

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  1. Assistant Professor, Student, Student, Student, Student, Assistant Professor, Mechanical Engineering Department, Hindustan College of Science & Technology, Mathura, Computer Science & Engineering Department, Hindustan College of Science & Technology, Mathura, Computer Science & Engineering Department, Hindustan College of Science & Technology, Mathura, Computer Science & Engineering Department, Hindustan College of Science & Technology, Mathura, Computer Science & Engineering Department, Hindustan College of Science & Technology, Mathura, Computer Science & Engineering Department, Hindustan College of Science & Technology, Mathura, Uttar Pradesh, Uttar Pradesh, Uttar Pradesh, Uttar Pradesh, Uttar Pradesh, Uttar Pradesh, India, India, India, India, India, India

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Abstract

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nThe present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, and manual pixel-based testing to classify microstructures effectively. K-Means segments SEM images based on pixel intensity, identifying different material phases. Canny Edge Detection is used to highlight sharp boundaries and fine structures in the images, improving the clarity of segmentation. DBSCAN further enhances classification by detecting dense microstructural regions and filtering out noise and outliers. Together, these techniques automate and improve the accuracy of microstructure analysis. This integrated method provides reliable insights into the relationship between microstructure and pellet quality. The project ultimately aims to optimize pellet production, reduce inspection time, lower operational costs, and enhance the efficiency of steel manufacturing processes.nn

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Keywords: Optical sensing, energy band, microstructure, image processing, K-mean, DBSCAN

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Instrumentation Technology & Innovations ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Instrumentation Technology & Innovations (joiti)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nRakesh Prasad, Lokesh Pachauri, Alok Singh, Madhav Rawat, Hardeep Singh, Vijay S. Katta. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach[/if 2584]. Journal of Instrumentation Technology & Innovations. 26/07/2025; 15(03):7-18.

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How to cite this URL:
nRakesh Prasad, Lokesh Pachauri, Alok Singh, Madhav Rawat, Hardeep Singh, Vijay S. Katta. [if 2584 equals=”][226 striphtml=1][else]Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach[/if 2584]. Journal of Instrumentation Technology & Innovations. 26/07/2025; 15(03):7-18. Available from: https://journals.stmjournals.com/joiti/article=26/07/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 23/05/2025
Accepted 26/05/2025
Published 26/07/2025
Retracted
Publication Time 64 Days

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