Microstructure/Property Examination of Weld HAZ in Grade 100 Microalloyed Steel
Bibliographic record
Abstract
The weld thermal cycle in microalloyed pipeline and structural steels results in significant changes in microstructure and, consequently, mechanical properties of the weld heat-affected zone (HAZ). To have better control of the properties of the HAZ, knowledge of these changes and correlation with mechanical property variations is required. The first step in achieving this is to construct a methodology to examine different regions of the HAZ thoroughly, so that important characteristics such as grain size, microstructure, precipitate type and distribution, and mechanical properties are determined. The next step would be using this methodology to examine different regions of the HAZ under different welding conditions (the most important of which is heat input) and therefore to understand the effects of the welding process. In this paper, a methodology for studying HAZ microstructure/property relationships is demonstrated for a Grade 100 microalloyed steel welded autogenously (no filler metal). Microhardness measurements are used to assess the mechanical property variation across the HAZ. Optical microscopy (OM) and transmission electron microscopy (TEM) are used for grain structure examination and precipitate analysis. The relationship between microstructure and mechanical properties, with emphasis on precipitate reactions, is presented for different regions of the HAZ.
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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 | 0.000 | 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".