Status of Joining Thin Sheet and Thin Wall Tubes of 14YWT
Bibliographic record
Abstract
Beginning this fiscal year, the FCRD research project initiated an investigation on joining thin sections of the advanced ODS 14YWT ferritic alloy. Friction stir welding (FSW) was investigated as a method to join thin plate and tubing of 14YWT since it is a solid state joining method that has been shown in past studies to be a promising method for joining plates of ODS alloys, such as 14YWT. However, this study will attempt to be the first to demonstrate if FSW can successfully join thin plates and thin wall tubing of 14YWT. In the first FSW attempt, a 1.06 cm thick plate of 14YWT (SM13 heat) was successfully rolled at 1000ºC to the target thickness of 0.1 cm with no edge cracking. This achievement is a highlight since previous attempts to roll 14YWT plates have resulted in extensive cracking. For the FSW run, a pin tool being developed by the ORNL FSW Process Development effort was used. The first FSW run successfully produced a bead-on-plate weld in the 0.1 cm thick plate. The quality of the weld zone appears very good with no evidence of large defects such as cavities. The microstructural characterization study of the bead-on-plate weld zone has been initiated to compare the results of the microstructure analysis with those obtained in the reference microstructural analysis of the 14YWT (SM13 heat) that showed ultra-fine grain size of 0.43 μm and a high number density of ~2-5 nm sizes oxygen-enriched nanoclusters.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".