Numerical Simulation of Residual Stresses in Welding and Ultrasonic Impact Treatment Process
Bibliographic record
Abstract
Welding technology is considered as a reliable and efficient joining method, which has been widely used in almost all the industry departments. Detrimental factors induced by welding such as micro-cracks/flaws, tensile residual stresses, high stress concentration may degrade the mechanical and fatigue properties of weld joints. Ultrasonic impact treatment (UIT) is considered one of the most efficient post-weld treatment which could improve the fatigue performance of weld joints. In this study, the effect of the UIT on residual stress distribution of 304L weld joints was particularly investigated. FE analysis simulation and the XRD experiment were performed to predict and measure residual stresses of both as-welded and the UIT-treated joints. Compared results show that simulated stresses are in good agreement with the experimental results along various paths, confirming the validity of welding model. The UIT introduces a compressive residual stress layer with depth between 2 and 3 mm near the impacting surface of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".