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Measurement-aware learning for reliable grain-boundary analysis in quantitative metallography.

Boaz Meivar¹, Inbal Cohen¹, Matan Rusanovsky²

¹Tel Aviv University, Tel Aviv-Yafo, Israel.

Scientific Reports

|July 1, 2026

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This summary is machine-generated.

Reliable quantitative metallography needs context and accurate annotations. MLOGRAPHY++ improves boundary detection by preserving context and aligning supervision, enhancing grain-size analysis accuracy.

Area of Science:

Materials Science
Computational Imaging
Metallurgy

Background:

Quantitative metallography relies on accurate boundary detection for measurements.
Existing computational methods struggle with weak, interrupted interfaces and crop-based workflows.
Reliability gaps persist in automated boundary analysis.

Purpose of the Study:

To investigate the impact of context, supervision alignment, and validation on boundary-derived grain-size analysis.
To develop and evaluate improved computational approaches for reliable interface detection.
To establish best practices for quantitative metallography using computational tools.

Main Methods:

Context-ablation experiments to assess the role of global context in boundary identifiability.

Keywords:

Context-aware inference Exploratory super-resolution Heyn intercept analysis Partial-label learning Quantitative metallography Texture boundary detection

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Introduction of MLOGRAPHY++, a context-preserving partial-labeling method.

Evaluation of Heyn-Compare for endpoint-oriented validation of open-boundary predictions.

Exploratory analysis of diffusion-based super-resolution under controlled conditions.

Main Results:

Removing global context significantly degrades boundary identifiability.
MLOGRAPHY++ aligns supervision with annotation ambiguity, reducing post-processing needs.
Heyn-Compare complements pixel-overlap metrics for open-boundary predictions.
Aggregate grain-size error decreased from 9.98 px to 5.38 px with MLOGRAPHY++.

Conclusions:

Context is crucial for reliable interface detection in quantitative metallography.
A measurement-aware approach combining context preservation, annotation-aligned supervision, and endpoint validation is effective.
Generated super-resolution details should be interpreted cautiously and not as physical ground truth.