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Spark Mapping Analysis for Segregation Partitioning in Large-Scale Super-Critical-Power Steel
Baibing Li1, Lei Zhao2, Liang Sheng2
1National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|July 12, 2025
Summary
This study introduces Spark Mapping Analysis for Large Samples (SMALS) to assess P91 steel composition. A new statistical model effectively identifies and quantifies macro-segregation in steel ingots and tubes.
Area of Science:
- Materials Science
- Metallurgy
- Analytical Chemistry
Background:
- P91 steel is crucial for high-temperature applications.
- Uniform macro-composition distribution is vital for P91 steel properties.
- Macro-segregation negatively impacts material performance.
Purpose of the Study:
- To apply Spark Mapping Analysis for Large Samples (SMALS) for P91 steel.
- To characterize non-destructively the macro-composition distribution in P91 steel ingots and tubes.
- To develop a method for analyzing large samples and mitigating macro-segregation.
Main Methods:
- First-time application of Spark Mapping Analysis for Large Samples (SMALS).
- Development of an innovative partition-based statistical analysis model for large specimens.
- Systematic methodology and workflow for partition analysis established using Sample A.
Main Results:
- Successfully identified and quantified segregation band widths (6 mm, 20 mm, 8 mm) in Sample A.
- Applied the partition analysis to different smelting batches (B1, B2, C1, C2).
- Delineated macro-segregation areas and performed quantitative evaluations based on statistical upper segregation limits.
Conclusions:
- Provided essential experimental insights into full-field compositional heterogeneity of P91 steel.
- Delivered critical methodological guidelines for optimizing steel smelting processes.
- Established a robust approach to control and mitigate macro-segregation in P91 steel.
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