Numerical Investigation on the Effect of Thermo-mechanical Tensioning on the Residual Stresses in Thin Stiffened Panels
Bibliographic record
Abstract
In shipbuilding industry, thin plates are widely used to maintain the minimum hull weight and to increase the fuel efficiency. These thin plates, when welded, are more prone to buckling distortions. So, it is very much important to predict these buckling distortions beforehand and try to mitigate them in the fabrication stage rather than doing it later. By doing this, the competitiveness in cost and time can be increased. In this study, a numerical investigation on the reduction of buckling distortions in the fabrication of orthogonal stiffened panels as used in shipbuilding was conducted. A method of in-process distortion mitigation technique, named as thermo-mechanical tensioning (TMT) was introduced to reduce the compressive residual stress in the far-field zone. The detail stress development mechanism in TMT process is discussed here. Commercially available finite element software ANSYS® 15 was used for calculations. The simulations revealed that the peak tensile stresses in the weld zone and the corresponding far-field compressive residual stresses get significantly reduced by the implementation of TMT. Thus, with reduction of compressive residual stress, buckling of such stiffened panels is effectively mitigated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".