Statistical modelling and optimization of FS-welded 6061-T651 Aluminum alloy
Bibliographic record
Abstract
An RSM-based experimental design, mathematical modelling and statistical optimization of friction stir welding process parameters was studied. A quadratic fitting model developed from a five-leveled four-factor parametric setting predicted the Ultimate Tensile Strength (UTS) of the welded AA6061- T651 joints. Statistical analysis at 95% Confidence Interval using ANOVA validated the conformity of the developed model with experimental data and also verified the adequacy of the model for UTS prediction and optimization. Results showed that the model was statistically significant (p<0.0001) with no notable lack of fit with the four parameters and their squared terms also significant statistically. The numerical optimization resulted to an optimum UTS of 166.32MPa from rotational speed, traversing speed, tool tilt angle and axial load values of 1293.641rpm, 48.467mm/min, 1.888° and 4.720kN, respectively with a desirability of 0.944. Also, 2D contour and 3D surface plots showed that the four parameters made decreasing effects on the UTS after reaching their optimized UTS. Driving forces for high UTS were: sufficient heat generation for plastic deformation, effective material coalescence, appropriate extrusion of molten material towards the trailing edge, adequate heat and mass transfer to control grain coarsening, void and flash formations. With an SN-ratio of 45.963 and low coefficient-of-variation of 1.11%, the conformity of the predicted and adjusted regression coefficients (R²) of 0.9619 and 0.9868 respectively supported by the confirmatory test and diagnostic plots showed a strong correlation between the experimental and predicted results. These demonstrated that the developed model was sufficient for predicting and optimizing the UTS of Friction Stir Weld (FSW) AA6061T651 plates.
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.
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 | 0.001 |
| 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".