Investigation of Welding Heat Input Influences on the Characteristics of Welded Joint of Storage Tank Wall Using Multiple Passes
Bibliographic record
Abstract
The correlation of welding input heat and microstructure and its impacts on multi-pass mechanical properties in storage tanks alloy steel plate of thickness 6mm using multiple passes SMAW was investigated experimentally and using SOLIDWORKS and ANSYS thermal and Mechanical Simulator.The heat-affected zone (HAZ) dimension was calculated, and the deformation in each pass was also calculated.Optical microscopy was used to characterize the weld metal microstructure effects on joint mechanical properties, the effects of the three pass sequences in deformation, and the resulting alterations in weld metal microstructure.In addition, the impact of this alteration in corrosion resistance characteristics was investigated, and steel samples were simulated.The investigation results illustrated increasing the weldment deformation with increasing heat inputs in the three-pass sequence.The welding joint microstructure shows a big difference in M-A (martensite-austenite) phase formation between the first, second, and third pass microstructure.In addition, the microstructure examinations showed the formation of AF (acicular ferrite) in the third pass, with a higher percentage in the second and first pass.This research demonstrated scientific information about the expected deformation of each pass from the three welding joints, the alteration in microstructures, and the corrosion resistance of the weldment according to the alteration with welding heat input.
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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".