A New Approach to Measure Strain During Friction Stir Welding Using Visioplasticity
Bibliographic record
Abstract
During modeling of the friction stir welding (FSW) process, the prediction of strain range experienced by the material is important as it affects the microstructure and mechanical properties of the final weld [1–7]. For aluminum alloys, this range has been reported very scarcely and/or scattered widely in the literature (the range of the maximum equivalent plastic strain has been reported to be from 2.4 to 184 [8–24]). A new approach is proposed in this article for measuring strain during friction stir welding using visioplasticity. In this approach, strains are calculated from changes in the boundaries of a small cylindrical Al-30% SiC composite marker mounted in the advancing side of mid-plane of adjacent plates during welding. The marker shape change is observed by a “stop action” (freeze-in) technique midway the process. COMSOL numerical modeling is then used to compute the strain distribution using the observed boundary changes compared to the initial marker boundaries. As an illustrative example, the method is applied to the results reported by London et al. [25] for the friction stir welding of 6.35 mm thick 7050 aluminum plates, welded with tool RPM of 350, welding speed of 1.69 mm/sec, tool pin diameter of 8 mm, tool shoulder diameter of 24 mm, and tool tilt angle of 3 degrees. A lower and upper bound of cumulative equivalent plastic strain of 14.1 and 20.3, respectively, were found to be in the neighborhood where the marker enters the severe deformation zone at mid-plane of plates in front of the leading edge of the pin.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".