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June 26, 2026Journal of Sustainable Metallurgy0 citationsOpen Access

Image Segmentation for Characterizing Tramp Material in Steel Scrap

Image-Segmentation-Guided Fragmentized Steel Scrap Tramp Material Characterization

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Authors

YQYijun QuanSSSanjay SinghalZLZushu Li

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Overview

Randomized trial demonstrates effective tramp element estimation in steel scrap using image-based methods, suggesting improved quality monitoring.

Key Points

  • The aim is to develop a fast and cost-effective method to estimate tramp element compositions in fragmentized steel scrap.
  • Curated an image dataset of steel and copper fragments with corresponding weight measurements.
  • Trained a neural network using image segmentation techniques on the dataset.
  • Classified images based on predicted segmentation maps.
  • Achieved 86.67% accuracy in assigning images to the correct copper composition class.
  • Demonstrated the feasibility of computer vision for tramp element analysis.
  • Provided a practical approach to enhance scrap quality monitoring in the steel industry.

Cite This Study

Quan et al. (2026) studied this question.

synapsesocial.com/papers/6a3e1a65030ad1a9b3092eb0https://doi.org/10.1007/s40831-026-01569-x
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