A Novel Study on the Effect of Tool Offset in Friction Stir Processing for Mg-NiTi Composite
Bibliographic record
Abstract
Mg-NiTi-based metal matrix composites are appropriate solutions for the two most important goals of material engineers in the present day, i.e., imparting functional behaviour and the light weighting of metallic structures. In recent years, due to its solid-state nature, the development of Mg-based metal matrix composites has largely benefited from friction stir processing. Despite the great effort of researchers in the domain of friction stir welding and processing, finding optimum process parameters for efficient material mixing and consolidation remains a rigorous and exhaustive challenge. Tool offset variation has been seen to aid the integrity and strength of friction stir welds; however, its effect upon the stir zone structure, material flow, particle distribution, and defect formation has not been investigated for friction stir processing. Therefore, the authors employed Mg as the base metal and NiTi shape memory alloy as the reinforcement to the targeted metal matrix composite. The tool offset was linearly varied by tilting the slotted length with respect to the traverse direction. Friction stir processing performed at a rotational speed of 560 rpm and traverse speed of 80 mm/min revealed crucial changes in defect morphology and area, which has been explicated with the quantified variation in tool offset from the advancing side to the retreating side. For the positive offset conditions, i.e., tool offset towards the advancing side, the shape of the tunnelling defect was chiefly convex from the outward direction. Meanwhile, for the negative offset conditions, i.e., tool offset towards the retreating side, the tunnelling defect exhibited a concave outward shape. A transition from rectangular to triangular morphology was also observed as the tool moved from an offset of 1.75 mm in the advancing side to 1.75 mm in the retreating side.
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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 | 0.000 |
| 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 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".