Numerical Investigation on Interruption in the Welding Process Used in Shipbuilding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interruptions in the welding process in shipbuilding are unavoidable because of complex geometry and human fatigue. This article presents an uncoupled three dimensional finite element (FE) modeling technique for bead-on-plate welding and an interruption in the welding process for low carbon and high notch toughness steel plate typically used in shipbuilding. The goal of the FE model was to successfully predict the effect of various time delays in the welding interruption on the residual stress distributions. The FE results are compared with the experimental results for the validation of the model. The experimental work was completed using the neutron diffraction method. The element birth-and-death algorithm was used in ANSYSW to simulate the filler metal deposition. A double ellipsoid heat source was used to simulate the heat source of the weld pool. The FE model considers the temperature dependent nonlinear material properties and uses the temperature-dependent combined coefficient of heat loss. The study found that weld interruptions in the welding process change the residual stress patterns and cause an increase in the maximum longitudinal tensile residual stresses. However, the maximum longitudinal compressive stress reduces as a result of interruptions in the weld process. This study found that a weld interruption duration of approximately 2 minutes is optimum for both fatigue and buckling strength. This study also analyzed the effect of preheat on longitudinal residual stress distribution and concluded that a suitable short time lag without any preheat is equivalent to preheat after a long welding interval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it