Experimental Study of the Weld Residual Stress in Manually and Mechanically Fabricated Dissimilar Metal Weld
Bibliographic record
Abstract
The dissimilar metal welds between the Inconel 600 flow element and the SA-106 Grade B carbon pipe with Alloy 82 or Alloy 182 filler material of some CANDU® designs have been identified as being susceptible to Primary Water Stress Corrosion Cracking (PWSCC). Initiation and growth of PWSCC in a Dissimilar Metal Weld (DMW) are driven primarily by Welding Residual Stresses (WRS). The present paper focuses on the experimental study of weld residual stress distribution in manually and mechanically fabricated DMWs with emphasis on the effect of repair. A series of DMW samples are firstly fabricated in accordance with the original welding procedures for those DMWs in the field, which were fabricated in 1970s and 1980s. Multiple thermocouples were used to record the temperature evolution during the entire welding process. These samples were then examined by ASME qualified personnel in accordance with the requirements for Class 1 weld in Article 9 of Section V of ASME BVPC using Visual Testing (VT) and Radiography Testing (RT). Repair was then performed in some samples, and further NDE examinations were performed. The qualified samples (with and without repair) were finally subject to destructive weld residual stress measurement using contour method. It is observed that weld repair dramatically changes the distribution of weld residuals tress. The use of a constant through-thickness WRS of 60,000 psi (415 MPa) is justified as the bounding case.
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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".