Simulation of friction stir welding in butt welds of grade 5 titanium alloy and measurement of heat distribution
Bibliographic record
Abstract
Residual stresses are generally referred to as stresses that exist in parts without applying external force and loading, and all of the components of these stresses have reached equilibrium with each other in different directions in the part. One of the major problems in welding is the creation of residual stress and distortion due to local heating. As a result of intense heat concentration in the welding area, the areas near the welding experience several thermal cycles. These thermal cycles cause non-uniform heating and cooling of the material and, as a result, create heterogeneous deformations and residual stresses in the part. Friction stir welding (FSW) is one of the widely used welding methods in various industries, such as aerospace. The measurement of heat distribution in the FSW process is an important challenge. In this welding method, the issue of heat transfer, fluid and dynamic equations resulting from tool movement, as well as the cooling of the part up to the temperature of phase transformation in welded parts, play a significant role in accurately predicting the residual stresses caused during welding. In this research study, the regime of cooling and heating of the material, and as a result, the residual stress magnitude during the FSW process, was simulated by using the equations related to heat transfer and temperature-dependent properties of the material. To do the simulation, ABAQUS software was used, accompanied by the DFLUX subroutine. After validating the simulation results by means of experimental welding and tests, the effect of temperature changes on the creation of residual stress resulting from heating and cooling cycles during welding was examined.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".