Effect of Post-Weld Heat Treatment on Nd: YAG Laser Welded Ti-6Al-4V Alloy Quality
Bibliographic record
Abstract
The effects of post-weld heat treatment on 3.05-mm thick Ti-6Al-4V alloy were investigated using a 4 kW Nd:YAG laser. Two main defects, underfill and porosity, were observed. The use of filler wire reduced underfill defects but slightly increased porosity. No cracks were detected. The as-welded and stress-relief annealed welds had very similar microstructures, hardness, and tensile properties. However, the post-weld solution heat treatment and aging transformed the martensite in the fusion zone into a coarse interlamellar α-β structure, causing a decrease in ductility but a more homogeneous distribution in the hardness of the welds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".