A computational and experimental approach to understanding material flow behavior during additive friction stir deposition (AFSD)
Bibliographic record
Abstract
In this study, a combined computational and experimental particle tracking investigation was performed for a solid-state additive manufacturing and repair process, Additive Friction Stir Deposition (AFSD). Specifically, smoothed particle hydrodynamics (SPH) simulations of AFSD were conducted in-order to elucidate deposition mechanics. The particle tracking of the SPH AFSD simulations was validated using experimental depositions of two feedstock varieties, including anodized AA6061-T6 feedstock to track external particles and AA6061-T6 copper wire core feedstock to track internal particles, to represent flow behavior from different regions of the feedstock. The X-Ray computed tomography (CT) experimental results revealed that the anodized oxides on the outside of the feedstock flowed to the retreating side, whereas the copper wire in the center of the feedstock migrated to the advancing side. Particle tracking results from the SPH simulations showed that, in general, particle movement is limited to directly beneath the feedstock. The rotational, radial, and traverse flow interactions visualized by AFSD simulations explained the advancing and retreating side biases experienced by the internal copper wire and surface oxides on the anodized feedstock. This work demonstrates the ability to predict AFSD material distributions, which has a significant impact on as-deposited material quality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".