Three-dimensional characterization of morphology and distribution of boundary networks in a low-carbon martensitic stainless steel
Bibliographic record
Abstract
This study presents a comprehensive three-dimensional (3D) characterization of lath martensite and its internal boundary networks in a low-carbon 13Cr-4Ni stainless steel (CA6NM cast alloy), utilizing large-volume Xe + plasma focused ion beam (PFIB) serial sectioning and electron backscatter diffraction (EBSD). Based on the 3D EBSD data, the martensitic microstructure was segmented into prior austenite grains (PAGs), packets, blocks, and sub-blocks, enabling statistical analysis of these structural units. The 3D networks of sub-block, block, packet, and PAG boundaries were identified, quantified, and classified using the Kurdjumov–Sachs (K-S) orientation relationship and its associated intervariant misorientations, providing new insights into the morphology, crystallography, and spatial distribution of internal boundaries. The dominant intervariant boundaries were found to predominantly terminate on {110} planes, exhibiting symmetric tilt, twist, or mixed character depending on the misorientation axis, underscoring the anisotropic crystallographic nature of the martensitic boundary network. In addition, boundaries with a 60°/[011] misorientation exhibited the highest degree of connectivity and continuity across the 3D microstructure. Morphological analysis further revealed three primary types of interactions between martensitic features: hard impingement of blocks from different packets, mutual intersection of blocks from distinct packets, and interpenetration of sub-blocks or blocks within a single packet. These interactions contribute to the formation of an interlocked martensitic microstructure, characterized by inhomogeneous boundary networks with complex morphological and crystallographic features. These new insights highlight the advantages of advanced 3D techniques in capturing microstructural intricacies, offering a robust foundation for developing predictive models that link microstructure to the mechanical performance of these alloys.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".