Generation of regression models and multi-response optimization of friction stir welding technique parameters during the fabrication of AZ80A Mg alloy joints
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Bibliographic record
Abstract
Conventional methodologies employed for the selection of weld process parameters for fabricating sound quality weldments have been found to consume more time and are often unreliable. Therefore, in this paper, an analysis was made to develop a quadratic regression model and empirical relationships by employing response surface methodology between various input parameters of the friction stir welding (FSW) technique including rotational speed of tool, axial force, and traversing speed of tool and five responses of output including percentage of elongation, yield strength, tensile strength, grain size, and microhardness. The discrepancies between the anticipated values and the genuine experimental outcomes are within ±1%, which reveals that the established mathematical quadratic regression model was a good fit to the actual experimental results. The experimental analysis also determined the elite combination of input parameters of the FSW technique for the output parameters.
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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 it