Computational crack propagation modeling of welded structures under as‐welded and high frequency mechanical impact (HFMI) treatment conditions
Bibliographic record
Abstract
Abstract The fatigue damage assessment of welded structures is of critical importance in the design of many engineering structures. The thermomechanical nature of the welding process leads to undesired tensile residual stresses, which results in fatigue performance degradation of welded structures. High‐frequency mechanical impact (HFMI) technique offers significant potentials to induce compressive residual stresses into the welded joints, thus improving fatigue life of welded parts. In this paper, a new crack propagation modeling approach considering effects of residual stress fields and the crack closure based on a modification of the Forman model is proposed to assess fatigue crack propagation performance of welded joints under as‐welded and HFMI treatment conditions. Experimental residual stress and fatigue data sets of 5083‐H321 aluminum, CSA 350W, ASTM A514, S355, and S960 steel welded joints are used to validate the proposed approach. Both predicted and experimental results show that the fatigue life improvement by the HFMI treatment depends on the applied stress level and material type. The fatigue life improvement is greater at the lower stress levels with a factor of more than 10–30 times, and the fatigue life increase becomes less at the higher stress levels with a factor of two to three times depending on the material type of welded joints. The fatigue life improvement by the HFMI is more significant with a higher material yield strength. Compared results showed that the proposed modeling approach provides efficient and accurate life predictions of the welded joints of five different materials under both as‐weld and HFMI conditions. The proposed modeling framework can be used as an effective analysis technique for fatigue crack propagation life of welded structures under pre‐ and post‐weld treatment conditions to account for residual stress fields.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".