A Finite Volume Framework for the Simulation of Additive Friction Stir Deposition
Bibliographic record
Abstract
Abstract In this study, a finite volume simulation framework was developed, validated, and employed for the first time in a new solid-state additive manufacturing and repair process, Additive Friction Stir Deposition (AFSD). The open-source computational fluid dynamics (CFD) code openfoam was used to simulate the deposition of a single layer of Aluminum Alloy 6061 feedstock onto a substrate, using a viscoplastic model to predict the flow behavior of the material. Conjugate heat transfer was considered between the build layer, the surrounding atmosphere, and the substrate, and the resulting temperatures were validated against experimental data recorded for three processing cases. Excellent agreement between simulated and measured temperature data was obtained, as well as a good qualitative prediction of overall build layer morphology. Further analysis of the temperature field was conducted to reveal the variation of temperature in the build direction, an analysis not possible with previous experimental or numerical methods, as well as a global heat transfer analysis to determine the relative importance of various modes of heat input and cooling. Tool heating was found to be the primary heat input to the system, representing 73% of energy input, while conduction to the substrate was the main mode of part cooling, representing 73% of heat loss from the build layer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".