Effect of aluminium concentration in filler alloys on HAZ cracking in TIG welded cast Inconel 738LC superalloy
Bibliographic record
Abstract
The effect of concentration of aluminium in filler alloys on heat affected zone (HAZ) microfissuring in TIG welded cast Inconel 738LC (IN 738LC) superalloy was studied. Three fillers, IN 625, IN 617 and Haynes 214, with aluminium concentrations varying from 0·2 to 4·5 wt-%, respectively, were used to TIG weld cast IN 738LC alloy plates subjected to two different preweld heat treatments. One preweld heat treatment was the standard solution heat treatment at 1120°C for 2 h followed by argon quenching. The second was a novel overaging treatment developed by the present authors, termed UM treatment, involving solution treatment at 1120°C followed by air cooling and subsequent ageing at 1025°C followed by water quenching. Detailed microstructural analysis of the welds and base metal was done by optical and analytical electron microscopy. Intergranular microfissures were observed in the HAZ of all the welds, irrespective of the filler alloy and the preweld heat treatment, while no cracks were observed in the fusion zone in any of the samples. The cracks were mostly found to be associated with constitutionally liquated MC carbides, borides, sulphocarbides, γ–γ′ eutectic and γ′ precipitates. The cracking was found to increase with increase in the fusion zone hardness and the aluminium content of the fillers, i.e. it was minimum for IN 625 and maximum for Haynes 214, for a particular preweld heat treatment. Between the two heat treatments, the UM treated samples with a smaller base metal hardness, however, exhibited a considerably reduced HAZ cracking. That is, the hardness of the fusion zone as well as the base metal appears to have a significant effect on the cracking susceptibility of the welds made with different fillers.
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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) | 0.001 | 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".