Further Development of Heat-Affected Zone Hardness Limits for In-Service Welding
Bibliographic record
Abstract
Welding onto an in-service pipeline is frequently required to facilitate a repair (Figure 1) or to install a branch connection using the “hot tapping” technique (Figure 2). Welds made in-service cool at an accelerated rate as the result of the ability of the flowing contents to remove heat from the pipe wall. These welds, therefore, are likely to have hard heat-affected zones (HAZs) and a subsequently increased susceptibility to hydrogen-assisted cracking. During the evaluation of procedure qualification welds, HAZ hardness is often used as an indicator of the susceptibility to cracking. A widely used value below which it is generally agreed that hydrogen cracking is not expected to occur is 350 HV. Both the US standard API 1104 Appendix B[1] and the Canadian standard CSA Z662[2] indicate that procedures for in-service welding that produce HAZ hardness greater than 350 HV should be evaluated with regard to the risk of hydrogen cracking. A previous paper on this topic[3] is referenced in CSA Z662 as guidance for performing this evaluation. The present paper describes the further development of HAZ hardness acceptance criteria that can be used to evaluate welds during the qualification of procedures for welding onto in-service pipelines.[4] This further development involved taking the effect of material thickness (restraint level) into account and further validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".