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<p>This study presents an integrated framework combining supervised classification and composition-driven regression modeling for automated phase identification and quantification in steel microstructures. SEM micrographs of three commercially used steels EN3, EN353, and 20MnCr5 were acquired at magnifications of 5000×, 10,000×, and 20,000×. Images were segmented using the SLIC algorithm into 64 × 64 patches, from which six Gray Level Co-occurrence Matrix (GLCM) features were extracted: contrast, correlation, energy, homogeneity, dissimilarity, and angular second moment (ASM). The proposed framework provides a preliminary demonstration of interpretable classification and composition-linked regression modeling for phase prediction in steels, with future work required to validate its generalizability across broader steel systems. Using these features, a Random Forest classifier achieved 70% classification accuracy and a macro F1-score of 0.61 in identifying four phases: ferrite, pearlite, distorted pearlite, and bainite. Patch-wise predictions (972 in total) were aggregated to evaluate steel-specific phase trends. Distorted pearlite was predominant in EN3 and EN353, while bainite appeared mainly in 20MnCr5. A regression model was developed to predict global phase percentages from alloying elements (C, Mn, Cr, Ni) and magnification level, achieving strong agreement with machine learning predictions (R² = 0.88 for pearlite and 0.83 for distorted pearlite), moderate agreement for bainite (R² = 0.69), and weak agreement for ferrite (R² = 0.07). This hybrid framework exhibits potential for microstructural classification of texture-based classification and composition-informed modeling in capturing microstructural complexity. The approach lays groundwork for scalable microstructure analysis for steel evaluation and supports data-driven microstructure design and analysis.</p>

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Hybrid machine learning and regression framework for automated phase classification and quantification in SEM images of commercial steels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月6日

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  • Pavan Hiremath,
  • Krishnamurthy D. Ambiger,
  • Shilpa Suresh,
  • Ranjan Kumar Ghadai,
  • Ramakrishna Vikas Sadanand,
  • G. Divya Deepak

摘要

This study presents an integrated framework combining supervised classification and composition-driven regression modeling for automated phase identification and quantification in steel microstructures. SEM micrographs of three commercially used steels EN3, EN353, and 20MnCr5 were acquired at magnifications of 5000×, 10,000×, and 20,000×. Images were segmented using the SLIC algorithm into 64 × 64 patches, from which six Gray Level Co-occurrence Matrix (GLCM) features were extracted: contrast, correlation, energy, homogeneity, dissimilarity, and angular second moment (ASM). The proposed framework provides a preliminary demonstration of interpretable classification and composition-linked regression modeling for phase prediction in steels, with future work required to validate its generalizability across broader steel systems. Using these features, a Random Forest classifier achieved 70% classification accuracy and a macro F1-score of 0.61 in identifying four phases: ferrite, pearlite, distorted pearlite, and bainite. Patch-wise predictions (972 in total) were aggregated to evaluate steel-specific phase trends. Distorted pearlite was predominant in EN3 and EN353, while bainite appeared mainly in 20MnCr5. A regression model was developed to predict global phase percentages from alloying elements (C, Mn, Cr, Ni) and magnification level, achieving strong agreement with machine learning predictions (R² = 0.88 for pearlite and 0.83 for distorted pearlite), moderate agreement for bainite (R² = 0.69), and weak agreement for ferrite (R² = 0.07). This hybrid framework exhibits potential for microstructural classification of texture-based classification and composition-informed modeling in capturing microstructural complexity. The approach lays groundwork for scalable microstructure analysis for steel evaluation and supports data-driven microstructure design and analysis.