Iron Contamination of Ni-Cr-Mo Alloy Weldments
Bibliographic record
Abstract
Abstract This paper presents findings of an investigation into the cause of contamination in alloy 59 (UNS N06059) weld qualification. Qualification specimens were welded and tested as part of the welding procedure development supporting the on-site fabrication of a large flue gas desulfurization (FGD) absorber and associated storage tanks. The specimens were fabricated by welding together carbon steel plates clad with alloy 59, or welding clad plates to solid alloy 59 with UNS N06059 filler metal. During the course of welding procedure development, various welding processes and techniques were evaluated for optimum as-welded corrosion resistance of the weldment. The evaluation included corrosion testing in the Green Death solution, which is a modified ASTM G 28 B test (the sulfuric acid content is reduced from 23 % to 11.5 % whilst keeping other constituents the same). The results of these Green Death corrosion tests included several pitting failures, which prompted a metallurgical examination to determine the cause of attack. During this investigation, iron was identified on or near the weldment surfaces of specimens not yet exposed to corrosion testing, despite substantive welding procedure development specifically designed to limit dilution from the carbon steel backing material. Such iron contamination was subsequently identified on all-alloy qualification specimens as well. This investigation determined that iron contamination was introduced during weld interpass cleaning with stainless steel power brushes. This paper describes the occurrence and solutions to this problem.
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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 | 0.001 |
| 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) | 0.000 | 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".