Comparison of Ni-Steel Dissimilar Joints for Coke Drum External Weld Repairs Based on Isothermal Low-Cycle Fatigue Tests
Bibliographic record
Abstract
Abstract Low-cycle fatigue failure has been widely accepted as the key mechanism causing damages of coke drums during cyclic thermal-mechanical loadings. Common damages of coke drums known as bulging and cracking are associated with accumulative plasticity caused by thermal and mechanical strains. External repairs using temper-bead welding techniques are implemented to repair welds in the damaged areas of coke drums, which provide structural support to the vessels. Compared with matching filler metals, Ni-base fillers including alloy 625 and alloy 182 are compatible with both low-alloy steel base metal and internal clads in terms of weldability and thermal expansion. However, the differences of yield strengths and cyclic hardening behaviors of nickel-base alloys from base metals compromise the fatigue resistances of weld joints. In this study, alloy 182 and alloy 625 repair coupons were evaluated and compared based on isothermal low-cycle fatigue tests. Low-cycle fatigue behaviors of both weld metals and 1.25Cr-0.5Mo base metal were measured at 1.0%, 1.5% and 2.0% strain amplitudes. Test results indicate both nickel-base filler metals exhibit overmatching strength over the base metal due to cyclic hardening. Low-cycle fatigue tests of Heat Affected zone (HAZ) samples show the failures of alloy 625 weld joints occur in the base metal, while the failures of alloy 182 weld joints occur along the fusion boundary. The observations show strength mismatch and fatigue resistance are the key factors to determine failure locations of the joints. In addition, cyclic hardening coefficients based on kinematic hardening model were extracted from experimental data to simulate the cyclic behaviors of the weld joints. Finite element simulation results were shown to be consistent with experimental data at stabilized cycles. Cyclic behaviors of weld metal and base metal within a weld transition sample were calculated based on the numerical model.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".