Effect of sulphur on hot ductility and heat affected zone microfissuring in Inconel 718 welds
Bibliographic record
Abstract
The influence of sulphur on microfissuring has been studied by characterising the hot ductility and heat affected zone (HAZ) microfissuring in welded wrought Inconel 718. A series of Inconel 718 base alloys, with sulphur concentrations in the range 7–110 wt-ppm and with the lowest possible concentrations of carbon, boron, and phosphorus were used in the study. The hot ductility of the alloys was measured using a Gleeble 1500 system, and their weldability was evaluated by bead on plate electron beam welding. The effect of sulphur on microfissuring in the weld HAZ was compared with that of boron in boron containing, but sulphur free, alloys. Sulphur reduced the weldability of the material, but its influence was very moderate in comparison with that of boron. The rate of increase of the brittle temperature range hot ductility parameter with increasing sulphur concentration was significantly smaller compared with that with increasing boron concentration. This may be because sulphur and boron seem to influence grain boundary liquation in the HAZ differently during welding of these alloys. Sulphur influences the weld HAZ microfissuring mainly by reducing the solidus temperature of liquid films during the cooling part of the welding cycle. Boron, however, not only reduces the solidus temperature, but also promotes grain boundary liquation during the heating part, and is therefore more effective than sulphur in promoting HAZ microfissuring.
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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) | 0.001 | 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".