On the effect of heat treatment schedules on the structure-property behaviour of heat-affected zones of ASTM A335 steel: Gleeble thermal-mechanical simulation
Bibliographic record
Abstract
This study reports on microstructure evolution and mechanical properties of weld joints of two ASTM A335 P92 steels with varying chromium and tungsten content influenced by heat treatment. Physical simulation samples for fine-grain and intercritical heat-affected zones were done using the Gleeble® 3500 equipment. A peak temperature of 900°C (intercritical zone) and 950°C (fine-grained zone) simulated different heat-affected zones. After physical simulation, the test samples underwent two heat treatment schedules: post-weld heat treatment (PWHT) and normalisation, followed by tempering. The results show that the two steels had similar martensite microstructure. The microstructure further exhibited the presence of M23C6 carbides along the grain and lath boundaries. The P92-B steel had the highest hardness values after heat treatment except at FGHAZ + PWHT condition, which had a lower hardness value (271.9 ± 5.0 HV0.5). In this condition (FGHAZ + PWHT), P92-B steel had a higher Charpy toughness value (180J), slightly higher than the base metal (178J) due to fully formed martensite microstructure. ICHAZ + heat treatment, P92-B steel had the lowest toughness values (74J for r-PWHT and 83J for PWHT), but these values were higher than the minimum toughness value of 47J of the weld joint required for hydro-testing of the vessels. The study revealed no marked significant differences between the two steels. The heat treatment method (r-PWHT) is applicable in the industry for Type IV crack mitigation of the weld joint.
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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".