인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract 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.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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