ArticleiScience2026
Development and application of an instrument for microstructure matrix inclusion distribution analysis in oversized metallic materials.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
To address the urgent need for inclusion analysis in clean steel production, this study develops an automated detection system for large metallic samples. Integrating a high-precision CNC stage, multi-unit microscopic imaging, laser spectroscopy, and a YOLOv11-based deep learning model, the system enables full-area rapid scanning of meter-scale samples. It automatically identifies and classifies inclusions (types A-D) with significantly improved efficiency-over 20 times faster than conventional methods. Experimental validation on automotive sheet samples successfully characterized 533,041 inclusions in size, distribution, and composition, while directly locating the largest inclusions, overcoming the limitations of small-area extrapolation.
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Registered trials
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