Simplified FEA Models in the Analysis of the Redistribution of Beneficial Compressive Stresses in Welds During Cyclic Loading
Bibliographic record
Abstract
The fatigue life of welded joints can be improved by modifying the weld toe geometry or by inducing beneficial compressive residual stresses in the weld. However, in the second case, the induced compressive residual stresses may relax when the welded joint is subjected to cyclic loading containing high tensile or compressive stress peaks. The stability of induced compressive stresses is investigated for a longitudinal gusset made of a S355 steel. Two methods are considered; either carrying out a high frequency mechanical impact (HFMI) treatment after welding or alternatively using low transformation temperature (LTT) electrodes during welding. The specimen is then subjected to a cyclic loading case with one cycle with a tensile peak (with magnitude reaching the local yield stress level) followed by cycles with constant amplitude. A sequential finite element analysis (FEA) is performed thereby preserving the history of the elasto-plastic behavior. Both the welding process and the HFMI treatment are simulated using simplified approaches, i.e., the welding process is simulated by applying a simplified thermal cycle while the HFMI treatment is simulated by a quasi-static contact analysis. It is shown that using the simplified approaches to modelling both the welding process and HFMI treatment gives results that correlate qualitatively well with the experimental and FEA data available in the literature. Thus, for comparison purposes, simplified models may be sufficient. Both the use of the HFMI treatment and LTT electrodes give approximately the same compressive stress at the weld toe but the extent of the compressive stress zone is deeper for HFMI case. During cyclic loading it is shown that the beneficial effect of both methods will be substantially reduced if the test specimen is subjected to unexpected peak loads. For the chosen load sequence, with the same maximum local stress at the weld toe, the differences in stress curves of the HFMI-treated specimen and that with LTT electrodes remain. While the LTT electrode gives the lowest (compressive) stress right at the well toe, it is shown that the overall effect of the HFMI treatment is more beneficial.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".