Determination of Optimal Weld Parameter for Joining Titanium Alloys by Gas Tungsten Arc Welding using Taguchi Method
Bibliographic record
Abstract
The optimal parameters for joining two different titanium alloys were determined by the Taguchi method and applied in similar and dissimilar joining of conventional Ti-6Al-4V and newly developed Ti-3Al-2.5Valloys.The microstructures of the two alloys and their mechanical properties were comparatively evaluated at the optimal parameters.The Ti-6Al-4V alloy showed a larger back bead width than that of Ti-3Al-2.5Vunder similar heat input, because of its lower thermal conductivity and higher specific heat capacity.The welded zone of Ti-3Al-2.5Vcontained a retained beta phase, which was absent in that of Ti-6Al-4V.This indicates that the transformation of the Ti-3Al-2.5Vweld metal starts above the martensite temperature, while it starts below the martensite temperature for Ti-6Al-4V.The failure of the welded specimen occurred in the base metal for both the titanium alloys, which indicates the superior weld quality.However, the welded Ti-6Al-4V showed superior tensile strength to that of the Ti-3Al-2.5Vweld under optimal conditions, owing the high beta phase fraction in its base metal.Meanwhile, it showed inferior ductility to that of Ti-3Al-2.5Vbecause of its coarser beta phase.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".