Friction-forging tubular additive manufacturing (FFTAM): A new route of solid-state layer-upon-layer metal deposition
Bibliographic record
Abstract
A new solid-state 3D-printing technology based on the friction-stir processing (FSP) treatment and forging consolidation is examined here for additive manufacturing (AM) to produce the tubular components in a layer-by-layer method, yielding extremely refined microstructure and attractive mechanical properties. Therefore, this novel technology can be termed “friction-forging tubular additive manufacturing (FFTAM)” as an alternative approach of sheet lamination (SL) process according to the ASTM designation for AM routes. In this research, the FFTAM process was accomplished to manufacture an Al–Al2O3 composite structure with a tubular shape design and a fully dense construction in the solid-state with continuous metallurgical bonding between the layers under the influence of hydrostatic pressure in combination with high temperatures generated by frictional-heating. A key finding is forming a superb high strength structure with an exceptional ultimate tensile strength (UTS) of around 900 MPa. This can be attributed to the significant grain structural refinement (down to less than 1 μm) and the homogenous incorporation of reinforcing alumina nanoparticles along with severe straining.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".