Investigation of crack tip opening displacement fracture toughness in repair welds of process piping
Bibliographic record
Abstract
Process piping welds are important because the equipment is a key component in energy and chemical processing. The study assesses repair and original welds using tensile tests, microhardness, microscopy, crack tip opening displacement (CTOD) at −29 °C and Charpy at three distinct temperatures, followed by statistical analysis and numerical modelling. Experiment involves the welding of 18-inch-diameter carbon steel piping materials. Correlogram delineates the primary factors of CTOD, whereas one-way ANOVA evaluates the influence of CTOD configurations. Weld centrelines (WCLs) without repairs exhibit superior CTOD values as compared to fusion lines. Tensile properties exceed those of the base metal, with average yield and ultimate tensile strengths of 479 MPa and 568 MPa for repair welds (RWs) and 516 MPa and 604 MPa for original welds, respectively. The overall minimum value of Charpy is 39 J at −29 °C, while the lowest CTOD values for repair and original welds are 0.204 mm and 0.44 mm, respectively, both above the 0.15 mm industry norm. Numerical modelling defines the overall correlation of CTOD with the ratios of average crack length to width and yield strength to tensile strength. Repair and original welds exceeded the requirements of ASME B31.3, improving performance and extending the operational lifespan of equipment.
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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) | 0.001 | 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".