Modeling and simulation of weld residual stresses and ultrasonic impact treatment of welded joints
Bibliographic record
Abstract
Most structures are fabricated using welded joints because of its low cost, structural strength and geometric flexibility. Welding is considered a highly complex metallurgical process that results in irregular geometries, material imperfections/flaws and tensile residual stresses. High tensile residual stresses and stress concentrations resulting from the weld process have a significant impact on fatigue life of structures, and thus a topic of great concern in product design. Ultrasonic impact treatment (UIT) is regarded as one of the most effective post welding treatment techniques to enhance the fatigue performance of welded structures. The UIT aims to introduce fatigue-beneficial compressive stresses by plastically deforming the weld toe and reduce stress concentrations by modifying local weld geometries. In this study, 3D modeling and simulation using finite element (FE) method has been performed to simulate welding process and numerical modeling of the UIT process to predict weld residual stress distribution of butt and T weld joints. The predicted numerical results under as-welded and UIT treatment conditions were compared to present weld residual stress improvements. Compared results shows that the UIT has potential applications on the fatigue design of welded structures, can lead to lighter structures and products, in which structures can be down-sized and optimized to reduce weights.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".