A General Expression for the Welding Tendon Force
Bibliographic record
Abstract
Abstract This study presents a novel expression for the tendon force associated with residual stresses produced during welding of large, thin sections. A general engineering equation is presented as the combination of a closed-form expression, based on idealized treatment, and correction factors to account for the effects of temperature-dependent thermal and mechanical material properties. The closed-form expression corresponds to the assumption of constant material properties. A rigorous mathematical treatment is utilized to derive explicit, exact expressions for the temperature-dependent correction factors without the need for empirical correlations. The temperature-dependent behavior of materials is captured accurately using four dimensionless groups. The analysis was validated through numerical simulations with common structural grades of low-carbon steel, stainless steel, aluminum, and titanium. The idealized treatment resulted in predictions with a mean difference of 18%, which was reduced to 7% by incorporating the correction factors. The remaining error is a systematic overestimate, which can be attributed to compliance effects of the finite plate used in the simulations, and is the focus of ongoing research. The utility of applying the novel tendon force equation to problems in fabrication procedure design is demonstrated with an example predicting distortion during manufacturing of hollow structural sections.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".